diff --git a/wordlist/00-frequency-list.csv.gz b/wordlist/00-frequency-list.csv.gz new file mode 100644 index 0000000..9714bb3 Binary files /dev/null and b/wordlist/00-frequency-list.csv.gz differ diff --git a/wordlist/01-errored-lemmatized-words.csv b/wordlist/01-errored-lemmatized-words.csv new file mode 100644 index 0000000..7515ee4 --- /dev/null +++ b/wordlist/01-errored-lemmatized-words.csv @@ -0,0 +1,501 @@ +WORD,ATTEMPTED_LEMMATIZATION,LEMMATIZER +THEMSELVES,THEMSELVE,SPACY +PERHAPS,PERHAP,SPACY +SERIES,SERIE,SPACY +OURSELVES,OURSELVE,SPACY +EXCEED,EXCEE,SPACY +BLEED,BLEE,SPACY +MATHEMATICS,MATHEMATIC,SPACY +NAKED,NAKE,SPACY +SKILLED,SKILLE,SPACY +BELOVED,BELOVE,SPACY +LEST,L,SPACY +WICKED,WICKE,SPACY +EMBED,EMBE,SPACY +DIABETES,DIABETE,SPACY +ONGOING,ONGOE,SPACY +ASHAMED,ASHAME,SPACY +CREED,CREE,SPACY +SINNER,SINN,SPACY +INDEBTED,INDEBTE,SPACY +UNCHANGED,UNCHANGE,SPACY +UNPUBLISHED,UNPUBLISHE,SPACY +UNEMPLOYED,UNEMPLOYE,SPACY +FORTHCOMING,FORTHCOME,SPACY 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+HABDABS,HABDAB,SPACY +RELISTING,RELISTE,SPACY +HOUGHING,HOUGHE,SPACY diff --git a/wordlist/01-lemmatized-words.csv b/wordlist/01-lemmatized-words.csv deleted file mode 100644 index 36a8125..0000000 --- a/wordlist/01-lemmatized-words.csv +++ /dev/null @@ -1,122 +0,0 @@ -word,lemmatized_word -the,THE -of,OF -to,TO -in,IN -is,BE -that,THAT -for,FOR -be,BE -by,BY -with,WITH -on,ON -not,NOT -this,THIS -are,BE -at,AT -from,FROM -he,HE -which,WHICH -his,HIS -have,HAVE -an,AN -but,BUT -you,YOU -they,THEY -were,BE -had,HAVE -we,WE -all,ALL -one,ONE -their,THEIR -been,BE -will,WILL -there,THERE -can,CAN -if,IF -other,OTHER -would,WOULD -no,NO -her,SHE -may,MAY -more,MORE -when,WHEN -who,WHO -such,SUCH -these,THESE -any,ANY -she,SHE -new,NEW -time,TIME -than,THAN -do,DO -some,SOME -what,WHAT -only,ONLY -into,INTO -them,THEY -two,TWO -also,ALSO -about,ABOUT -out,OUT -him,HE -my,MY -said,SAY -up,UP -our,OUR -first,FIRST -should,SHOULD -under,UNDER -made,MAKE -state,STATE -see,SEE -after,AFTER -could,COULD -then,THEN -me,I -most,MOST -over,OVER -very,VERY -your,YOUR -between,BETWEEN -where,WHERE -now,NOW -shall,SHALL -work,WORK -those,THOSE -same,SAME -well,WELL -each,EACH -many,MANY -being,BE -years,YEAR -did,DO -year,YEAR -through,THROUGH -must,MUST -upon,UPON -before,BEFORE -like,LIKE -use,USE -part,PART -general,GENERAL -people,PEOPLE -because,BECAUSE -used,USE -how,HOW -even,EVEN -much,MUCH -states,STATE -during,DURING -both,BOTH -case,CASE -three,THREE -number,NUMBER -make,MAKE -per,PER -great,GREAT -act,ACT -way,WAY -life,LIFE -good,GOOD -day,DAY diff --git a/wordlist/01-lemmatized-words.csv.gz b/wordlist/01-lemmatized-words.csv.gz new file mode 100644 index 0000000..66cdb4d Binary files /dev/null and b/wordlist/01-lemmatized-words.csv.gz differ diff --git a/wordlist/01-lemmatized-words.py b/wordlist/01-lemmatized-words.py index e744771..73e9aac 100755 --- a/wordlist/01-lemmatized-words.py +++ b/wordlist/01-lemmatized-words.py @@ -1,103 +1,70 @@ #!/usr/bin/env python3 # coding: utf-8 -print("Step 1") +print("Loading dependencies") - -try: - _initialized -except: - # !pip install spacy - # !python -m spacy download en_core_web_trf - import spacy - from tqdm import tqdm - - nlp = spacy.load('en_core_web_trf', disable=['parser', 'ner']) - - _initialized=True - -import pandas as pd +import spacy +import nltk +from tqdm import tqdm import gzip -import re +# Wordnet +try: + from nltk.stem.wordnet import WordNetLemmatizer +except: + nltk.download("wordnet") + from nltk.stem.wordnet import WordNetLemmatizer +wordnet = WordNetLemmatizer() -print("Step 2") +# Spacy +nlp = spacy.load('en_core_web_trf', disable=['parser', 'ner']) +print("Loading initial wordlist") -def get_lines(filename): - with gzip.open(filename, 'r') as f: - ret = [] - for l in f: - if len(ret) > 30_000: - return ret - ret.append(str(l).lower()) - return ret +words = [] +with gzip.open("./00-frequency-list.csv.gz", 'r') as infile: + for line in infile: + words.append(line.decode('ascii').split(",")[0]) +# Remove header +words = words[1:] +print(words[0:5]) -WORDLIST_SIZE = 8192 + 3 -word_re = re.compile(r"^[A-Za-z]+$") +print("Lemmatizing words") +seen_lemmatizations = set() -print("Step 3") +with open("./01-errored-lemmatized-words.csv", 'w') as erroutfile: + erroutfile.write("WORD,ATTEMPTED_LEMMATIZATION,LEMMATIZER\n") + with gzip.open("./01-lemmatized-words.csv.gz", 'w') as outfile: + outfile.write("WORD,LEMMATIZED_WORD,LEMMATIZER\n".encode("ascii")) -annotated_words=pd.read_excel("annotated_words.ods") + iter = tqdm(words) -excluded_words = list(annotated_words[annotated_words["keep"] != "Yes"]["word"].str.lower()) -excluded_words[0:10] + for word in iter: + lemmatized_words = [ + # Wordnet + (wordnet.lemmatize(word).upper(), 'WORDNET'), + # Spacy + (nlp(word)[0].lemma_.upper().upper(), 'SPACY'), + ] -custom_maps = annotated_words[annotated_words["maps_to"].notna()][["word","maps_to"]].assign(maps_to=lambda x: x["maps_to"].map(lambda y: y.split(","))) + for (lemmatized_word, lemmatizer) in lemmatized_words: + if word == lemmatized_word: + continue -custom_maps = [ - (m[1]["word"].lower(), mapping.lower()) - for m in custom_maps.iterrows() - for mapping in m[1]["maps_to"] -] -custom_maps + if (word, lemmatized_word) in seen_lemmatizations: + continue + seen_lemmatizations.add((word, lemmatized_word)) -print("Step 4") + if lemmatized_word not in words: + iter.write(f"{lemmatized_word} ({lemmatizer}) not in all_words") + erroutfile.write(f"{word},{lemmatized_word},{lemmatizer}\n") + continue + iter.write(f"{word} => {lemmatized_word} ({lemmatizer}) added") -# Start parsing the wordlist -all_words = get_lines("00-frequency-all.txt.gz") - -# Delete header line -all_words = all_words[1:] - -# Get only the word (fixed width) -all_words = [w[13:36].strip() for w in all_words] - -# Remove special characters -all_words = [w for w in all_words if word_re.search(w)] - -# Remove all removed words -all_words = [w for w in all_words if w not in excluded_words] - -# Add all custom mappings -for m in list(sum(custom_maps, ())): - if m[0] not in all_words: - all_words.append(m[0]) - if m[1] not in all_words: - all_words.append(m[1]) - - -print("Step 5") - -# Lemmatize all words (plural -> singular) -# lemmatize_mappings = [(w, nlp(w)[0].lemma_) for w in tqdm(all_words)] - -with open("01-lemmatized-words.csv", "w") as f: - f.write("word,lemmatized_word\n") - - iter = tqdm(all_words[:1000]) - - for w in iter: - lemmatized_word = nlp(w)[0].lemma_.upper() - if lemmatized_word == w: - continue - if lemmatized_word not in all_words: - iter.write(f"{lemmatized_word} not in all_words") - - f.write(f"{w},{lemmatized_word}\n") + outfile.write(f"{word},{lemmatized_word},{lemmatizer}\n".encode("ascii")) diff --git a/wordlist/01-lemmatized-words.txt b/wordlist/01-lemmatized-words.txt new file mode 100644 index 0000000..04be864 --- /dev/null +++ b/wordlist/01-lemmatized-words.txt @@ -0,0 +1,1285 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+CHILE,CHILI +CHILE,CHILLY +COLOURLESS,COLORLESS +COM,CALM +CORP,CORE +CORPS,CORE +CUE,QUEUE +DAZE,DAY +DAZED,DAY +DEAR,DEER +DESSERT,DESERT +DEW,DO +DEW,DUE +DIED,DYED +EFFECTIVE,EFFECT +EFFECTS,EFFECT +ELECTRONICS,ELECTRONIC +FAVOUR,FAVOR +FAX,FACTS +FILING,FILLING +FILINGS,FILLING +FORTUNATELY,FORTUNATE +FOUR,FOR +GRATE,GREAT +HAIRY,HARRY +HARRY,HAIRY +HEIR,HAIR +HEIRS,HAIR +HEM,HIM +HONOUR,HONOR +HONOURS,HONORS +HYMN,HIM +HYMNS,HIM +IMPROVES,IMPROVE +ISLE,AISLE +KNIGHT,NIGHT +KNOT,NOT +KNOTS,NOT +LARVAE,LARVA +LECTURER,LECTURE +MANOR,MANNER +MONIES,MONEYS +NEIGHBOURHOOD,NEIGHBORHOOD +NEIGHBOUR,NEIGHBOR +NEIGHBOURS,NEIGHBOR +NOSE,KNOW +NUN,NONE +ORE,OAR +ORE,OR +ORGANISATIONAL,ORGANIZATIONAL +ORGANISATION,ORGANIZATION +ORGANISATIONS,ORGANIZATION +OWE,OH +PAR,PARSE +PARS,PARSE +PEOPLES,PEOPLE +PER,PURR +PETAL,PEDAL +PROVIDES,PROVIDE +RAP,WRAP +REFORMED,REFORM +SCENT,CENT +SCENTS,CENT +SENSE,CENT +SENSED,CENT +SENSES,CENT +SIMULTANEOUSLY,SIMULTANEOUS +TELECOMMUNICATIONS,TELECOMMUNICATION +THEATRES,THEATER +THEATRE,THEATER +THRU,THROUGH +VAPOUR,VAPOR +VARY,VERY +VERTEBRA,VERTEBRAE +WEARY,WARY +WEIGHS,WAY +WEIGH,WAY +YELLOW,HELLO +CACHE,CASH +BYTE,BITE +COUNSELLOR,COUNSELOR diff --git a/wordlist/02-exclude.txt b/wordlist/03-exclude.csv similarity index 92% rename from wordlist/02-exclude.txt rename to wordlist/03-exclude.csv index f26f9fc..a26bc0a 100644 --- a/wordlist/02-exclude.txt +++ b/wordlist/03-exclude.csv @@ -4,6 +4,7 @@ ABA ABANDON ABANDONING ABANDONMENT +ABATE ABBE ABBEY ABBOT @@ -16,6 +17,8 @@ ABOLISH ABOLITION ABORIGINAL ABRAHAM +ABSURD +ABSURDITY ABU ABUTTING AC @@ -26,6 +29,7 @@ ACTON AD ADA ADAM +ADDICTION ADDIS ADELAIDE ADEN @@ -36,6 +40,7 @@ ADO ADOBE ADRIAN ADRIATIC +ADULTERY ADVERTISER AEGEAN AERO @@ -43,12 +48,14 @@ AERONAUTICAL AERONAUTICS AEROSPACE AETNA +AFFIX AFGHAN AFGHANISTAN AFORE AFRICA AFRICAN AFRO +AGGRESSION AGNES AGRI AGRO @@ -66,6 +73,7 @@ ALBANY ALBERT ALBERTA ALBION +ALBUMIN ALBUQUERQUE ALCOHOLISM ALDER @@ -104,6 +112,7 @@ AMI AMIR AMOS AMP +AMPLY AMSTERDAM AMY ANA @@ -119,6 +128,7 @@ ANGELES ANGLICAN ANGLO ANGOLA +ANGRILY ANGUS ANIL ANITA @@ -130,7 +140,9 @@ ANNE ANNETTE ANNEX ANNIE +ANNIHILATE ANNUM +ANOINT ANON ANSI ANTARCTIC @@ -138,11 +150,13 @@ ANTARCTICA ANTHONY ANTHRACITE ANTILLES +ANTING ANTONIO ANTONY ANTWERP ANUS ANXIOUS +ANXIOUSLY APACHE API APOCALYPSE @@ -164,6 +178,7 @@ ARCHIBALD ARCHIE ARCHIPELAGO ARCTIC +ARDENT ARGENTINA ARGENTINE ARGUS @@ -179,10 +194,14 @@ ARMENIAN ARMORY ARMSTRONG ARNOLD +AROUSAL +ARROGANCE +ARSENIC ARTHUR ARYAN AS ASA +ASCETIC ASIA ASIAN ASIATIC @@ -220,6 +239,7 @@ AUSTRALIA AUSTRALIAN AUSTRIA AUSTRIAN +AUTHORITATIVE AVENUE AVIV AVON @@ -249,9 +269,11 @@ BALTIC BALTIMORE BANCO BANCROFT +BANG BANGALORE BANGKOK BANGLADESH +BANKRUPT BANTAM BANTU BAPTIST @@ -285,6 +307,7 @@ BAYARD BAYOU BBC BEACON +BEATING BEATRICE BEAU BEAUFORT @@ -301,7 +324,6 @@ BELGIAN BELGIUM BELGRADE BELIZE -BELL BELLA BELLE BEN @@ -326,11 +348,13 @@ BERT BERTHA BERTRAM BERWICK +BESEECH BESS BESSEMER BESSIE BETHEL BETHLEHEM +BETRAYAL BETSY BETTY BEY @@ -343,6 +367,7 @@ BIRMINGHAM BIS BISHOP BISMARCK +BITCH BLACKBURN BLAKE BLANC @@ -351,6 +376,7 @@ BLAND BLEED BLEEDING BLEST +BLUNT BO BOB BOBBY @@ -405,6 +431,7 @@ BRENT BRETON BREWER BRIAN +BRIBE BRIDGEPORT BRIDGET BRIG @@ -418,6 +445,7 @@ BRITAIN BRITANNIA BRITISH BRITTANY +BRITTLE BROADWAY BROCK BRONX @@ -429,6 +457,7 @@ BROUGHAM BROWNING BRUNEI BRUSSELS +BRUTALITY BRUTUS BUCHAREST BUCKINGHAM @@ -440,6 +469,7 @@ BUENOS BUFFALO BULGARIA BULGARIAN +BULGE BULLOCK BUNKER BUR @@ -473,6 +503,7 @@ CALCUTTA CALEB CALEDONIA CALGARY +CALIBRE CALIF CALIFORNIA CALIPH @@ -495,11 +526,12 @@ CANTERBURY CANTO CANTON CANTOR -CANYON CARACAS CARBONIFEROUS +CARCINOMA CARDIFF CARDINAL +CARELESS CARIBBEAN CARL CARLISLE @@ -522,6 +554,7 @@ CASTILE CASTRO CATALOGING CATALOGUING +CATASTROPHE CATECHISM CATHARINE CATHEDRAL @@ -535,10 +568,12 @@ CD CECIL CEDAR CELTIC +CENSOR CENTENARY CENTENNIAL CENTER CENTUM +CERVIX CEYLON CHAD CHAIRPERSON @@ -548,6 +583,7 @@ CHAMPAIGN CHANCELLOR CHANCERY CHANDLER +CHAOTIC CHAPLIN CHAPMAN CHARLEMAGNE @@ -577,6 +613,8 @@ CHILDHOOD CHILEAN CHINA CHINESE +CHOKE +CHOKING CHOMSKY CHOPIN CHOW @@ -614,6 +652,7 @@ CLEOPATRA CLEVELAND CLIFFORD CLIFTON +CLIMAX CLINTON CLYDE CM @@ -623,6 +662,7 @@ COCA COCHIN CODEX COED +COKE COL COLA COLCHESTER @@ -679,6 +719,7 @@ CONSTANTINOPLE CONSUL CONSULATE CONSULTATIVE +CONTIGUOUS CONTRABAND CONVERSELY CONVOCATION @@ -721,6 +762,8 @@ CRETACEOUS CRETE CRIMEA CRIMINOLOGY +CRIPPLE +CRITIQUE CROATIA CROATIAN CROFT @@ -729,9 +772,11 @@ CROOK CROUCH CROW CRT +CRUCIFY CRUZ CUBA CUBAN +CULT CULVER CUM CUMBERLAND @@ -741,7 +786,9 @@ CURATOR CURIE CURRY CURT +CUTANEOUS CUTLER +CUTTER CV CYNTHIA CYPRUS @@ -754,6 +801,7 @@ D DA DAD DADDY +DAGGER DAISY DAK DAKOTA @@ -761,6 +809,8 @@ DALE DALLAS DAMASCUS DAME +DAMN +DAMNED DAN DANE DANIEL @@ -791,16 +841,19 @@ DEARBORN DEATH DEB DEBORAH +DEBRIS DEC DECEASED DECKER DECREE DEE +DEFIANCE DEI DEL DELAWARE DELHI DELIA +DELINQUENCY DELL DELPHI DEMISE @@ -810,6 +863,7 @@ DENMARK DENNIS DENT DENVER +DEPRESSIVE DERBY DERBYSHIRE DERRICK @@ -827,6 +881,7 @@ DIANA DICK DICKENS DICKEY +DICTATORSHIP DIE DIEGO DILL @@ -837,9 +892,15 @@ DIRECTORATE DISCHARGE DISCHARGING DISCRIMINATE +DISCURSIVE +DISINTEGRATE +DISINTEGRATION DISMAL DISNEY +DISOBEDIENCE DISQUALIFICATION +DISSEMINATION +DISSIPATION DITTO DIVERSITY DIVISION @@ -879,9 +940,11 @@ DP DRAKE DRAPER DRESDEN +DROOP DROSOPHILA DRUG DRUGGIST +DRUNKEN DUBLIN DUCHESS DUCHY @@ -904,6 +967,7 @@ EAGLE EARL EASTER EC +ECSTASY ECU ECUADOR ED @@ -940,8 +1004,10 @@ ELLEN ELLIOT ELMER ELSIE +ELUCIDATE ELY EM +EMBARRASS EMBASSY EMERITUS EMERSON @@ -951,6 +1017,7 @@ EMMA EMMANUEL EMORY EMPRESS +EMULSION EN ENCYCLOPEDIA ENDOCRINOLOGY @@ -982,6 +1049,7 @@ EROS ERR ESKIMO ESP +ESPECIAL ESQUIRE ESS ESSEX @@ -1012,6 +1080,7 @@ EVE EVELYN EWE EX +EXACERBATE EXCEL EXCELLENCY EXCHEQUER @@ -1032,10 +1101,13 @@ FAILING FAILURE FAIRBANKS FAIRFAX +FAKE FALCON FANG FANNY +FANTASY FARADAY +FASCIST FATIGUE FATTY FAULKNER @@ -1053,11 +1125,14 @@ FERDINAND FERMI FERNANDO FERRY +FERTILIZE FIBRE +FICTITIOUS FIDE FIELDING FIFE FIJI +FILTHY FINCH FINISHING FINK @@ -1065,11 +1140,13 @@ FINLAND FINN FINNISH FIONA +FIREARM FIRSTLY FIRTH FITCH FITZGERALD FLANDERS +FLECTION FLEMING FLEMISH FLEXOR @@ -1080,6 +1157,7 @@ FLORENTINE FLORIDA FOB FOE +FOOLISH FORD FORESTER FOREWORD @@ -1090,6 +1168,7 @@ FOUNDATION FOURIER FOWLER FOX +FRAIL FRANCE FRANCES FRANCIS @@ -1108,13 +1187,17 @@ FREDERICK FREEMAN FRENCH FRENCHMAN +FRESCO FREUD FREUDIAN +FRIAR FRIDAY +FRIGHTFUL FRITZ FRO FUCK FUCKING +FUGITIVE FULLER FULTON FUNERAL @@ -1134,6 +1217,7 @@ GALLOWAY GAMBIA GAMBLE GANDHI +GANG GANGES GAOL GAR @@ -1142,6 +1226,7 @@ GARDNER GARLAND GARNER GARY +GASTROINTESTINAL GAUL GAULLE GAUSS @@ -1174,6 +1259,7 @@ GERTRUDE GETTYSBURG GHANA GHENT +GHETTO GIBBON GIBRALTAR GIDEON @@ -1181,6 +1267,7 @@ GIL GILBERT GILES GILL +GILT GIN GIT GLADSTONE @@ -1199,8 +1286,10 @@ GOLDSMITH GORBACHEV GORDON GORE +GORGE GOSPEL GOTHIC +GOUGE GRAHAM GRAN GRANADA @@ -1226,6 +1315,7 @@ GRIFFIN GRIFFITH GRIMM GROSS +GROTESQUE GROVE GUAM GUATEMALA @@ -1251,6 +1341,7 @@ HAL HALE HALIFAX HALLE +HALLUCINATION HAMBURG HAMILTON HAMLET @@ -1291,7 +1382,9 @@ HAWAIIAN HAWK HAWTHORNE HAYDN +HAZARDOUS HAZEL +HE HEATH HEATHER HEBREW @@ -1321,10 +1414,12 @@ HERMES HERMITAGE HEROD HERODOTUS +HEROINE HERON HERTZ HESSE HESTER +HETEROGENEITY HETEROSEXUAL HEW HEWLETT @@ -1356,6 +1451,7 @@ HOLLYWOOD HOLOCAUST HOLT HOLY +HOMELESS HOMER HOMERIC HOMO @@ -1370,6 +1466,7 @@ HOOVER HORACE HORATIO HORTICULTURAL +HOSTAGE HOSTILITY HOT HOUGH @@ -1385,6 +1482,7 @@ HUGH HUGO HUH HULL +HUMILIATION HUMPHREY HUN HUNGARIAN @@ -1417,12 +1515,17 @@ IK IKE ILIAC ILIAD +ILLEGITIMATE ILLINOIS IMAM IMF +IMMORTALITY IMMUNODEFICIENCY IMMUNOLOGY IMP +IN +INADEQUACY +INCOMPETENT INDIA INDIAN INDIANA @@ -1432,17 +1535,23 @@ INDONESIA INDONESIAN INDUS INFECT +INFERIORITY INFIRMARY INFO +INIQUITY INN INQUISITION INS +INSECURITY INSULAR +INSULT INTEL INTERESTINGLY INTERGOVERNMENTAL INTERN INTERNATIONALE +INTERSTITIAL +INTOXICATE INVERNESS IOWA IP @@ -1465,6 +1574,7 @@ ISABEL ISABELLA ISAIAH ISBN +ISCHEMIA ISLAM ISLAMIC ISM @@ -1565,6 +1675,7 @@ JUDAISM JUDAS JUDICATURE JUDICIARY +JUDICIOUS JUDITH JUDY JULIA @@ -1645,6 +1756,7 @@ LAMA LAMB LAMBERT LAMBETH +LAME LAN LANCASHIRE LANCASTER @@ -1654,7 +1766,6 @@ LANKA LAO LAOS LAPLACE -LASTLY LATIN LATINO LATVIA @@ -1704,6 +1815,7 @@ LEY LI LIAS LIB +LIBEL LIBER LIBERALITY LIBERIA @@ -1732,6 +1844,7 @@ LISTER LITHUANIA LITHUANIAN LITRE +LITURGY LIVERPOOL LIZ LIZZIE @@ -1771,6 +1884,7 @@ LUMPUR LUNA LUND LUST +LUSTRE LUTHER LUTHERAN LUXEMBOURG @@ -1813,6 +1927,7 @@ MALCOLM MALE MALI MALICE +MALICIOUS MALIGNANT MALL MALLET @@ -1827,6 +1942,7 @@ MANHATTAN MANHOOD MANILA MANITOBA +MANLY MANN MANNING MANSFIELD @@ -1934,6 +2050,7 @@ MILTON MILWAUKEE MINERVA MINISTER +MINISTERIAL MINISTRY MINNEAPOLIS MINNESOTA @@ -1943,9 +2060,13 @@ MIOCENE MIRANDA MIRIAM MISCELLANEOUS +MISFORTUNE +MISLEAD MISSISSIPPI MISSOURI MISTER +MISTRESS +MISUNDERSTOOD MIT MITSUBISHI MO @@ -1961,6 +2082,7 @@ MONGOL MONGOLIA MONGOLIAN MONICA +MONOTONOUS MONSIEUR MONT MONTAGU @@ -1975,6 +2097,8 @@ MOOR MOORE MOORISH MOOSE +MORBID +MORBIDITY MOREOVER MORGAN MORMON @@ -2006,6 +2130,7 @@ MURIEL MURPHY MURRAY MUSE +MUSHROOM MUSLIM MUSSOLINI MYRON @@ -2022,9 +2147,11 @@ NAPIER NAPLES NAPOLEON NAPOLEONIC +NARCOTIC NASA NASHVILLE NASSAU +NASTY NAT NATAL NATALIE @@ -2038,6 +2165,7 @@ NEBRASKA NEC NED NEE +NEEDY NEGRO NEHRU NELL @@ -2051,6 +2179,7 @@ NESS NETHERLANDS NEUMANN NEUROLOGY +NEUROSIS NEVADA NEVILLE NEWARK @@ -2114,7 +2243,10 @@ ODYSSEUS ODYSSEY OEDEMA OEDIPUS +OFFENCE +OFFEND OFFENDER +OFFENDING OFFENSE OFFENSIVE OFT @@ -2181,6 +2313,8 @@ PALL PALMER PAMELA PANAMA +PANCREAS +PANCREATIC PANTHEON PANTHER PAP @@ -2206,6 +2340,7 @@ PASSOVER PASTEUR PAT PATER +PATRIARCH PATRICIA PATRICK PATTEN @@ -2227,6 +2362,7 @@ PEGGY PEKING PELL PEMBROKE +PENANCE PENELOPE PENGUIN PENINSULA @@ -2239,12 +2375,15 @@ PERCY PERENNIAL PERI PERMIAN +PERNICIOUS PERRY PERSIA PERSIAN PERTH PERU PERUVIAN +PERVERT +PESO PETE PETER PETERBOROUGH @@ -2303,6 +2442,7 @@ POLISH POLK POLLARD POLLOCK +POLLUTE POLLY POLO POLYTECHNIC @@ -2324,6 +2464,7 @@ PR PRAGUE PRAXIS PRE +PREDATOR PREFECTURE PREHEAT PREMIER @@ -2337,12 +2478,17 @@ PRINCE PRINCESS PRIORY PRIVY +PROBLEMATIC PROCTOR PROD PROF +PROFANE PROG +PROLETARIAN PROM PROMETHEUS +PROSTITUTE +PROSTITUTION PROTECTORATE PROTEST PROTESTANT @@ -2359,8 +2505,10 @@ PS PSALM PSYCHIATRY PSYCHIC +PSYCHOSIS PTOLEMY PUB +PUBERTY PUBLISHING PUEBLO PUERTO @@ -2403,6 +2551,7 @@ RANDOLPH RANDY RANGER RANGOON +RAPE RAPHAEL RAPPORTEUR RAVEN @@ -2412,10 +2561,12 @@ RE REAGAN REALTY REBECCA +REBELLIOUS RECTAL RECTOR RECTUM REDEEMER +REDUNDANT REDWOOD REED REEVE @@ -2428,12 +2579,15 @@ REGENT REGINALD REGISTRAR REICH +RELUCTANT REMBRANDT REMINGTON RENAISSANCE REP +REPRESSION REPRODUCTIVE REPUBLICAN +REPULSE RESTATEMENT RET RETARD @@ -2444,6 +2598,7 @@ REUBEN REV REVERE REVEREND +REVOLVER REVUE REX RHINE @@ -2520,9 +2675,11 @@ RUTLAND RWANDA SABBATH SAC +SACRAMENT SACRAMENTO SADDAM SADIE +SADLY SAG SAGE SAHARA @@ -2544,6 +2701,7 @@ SAMPSON SAMSON SAMUEL SAN +SANE SANSKRIT SANTA SANTIAGO @@ -2570,6 +2728,7 @@ SCANDINAVIA SCANDINAVIAN SCARBOROUGH SCHILLER +SCHIZOPHRENIC SCHOONER SCHUBERT SCHUMANN @@ -2597,6 +2756,7 @@ SEN SENATE SENECA SENEGAL +SENSUAL SENTINEL SEOUL SEPT @@ -2621,6 +2781,7 @@ SHAN SHANGHAI SHANNON SHAW +SHE SHEA SHEFFIELD SHEIKH @@ -2658,6 +2819,7 @@ SINAI SINCLAIR SINGAPORE SINGLETON +SINISTER SIOUX SIVA SKINNER @@ -2672,6 +2834,8 @@ SLY SMASH SMITH SMYRNA +SNEER +SNEERING SO SOCIALIST SOCIOLOGY @@ -2688,6 +2852,7 @@ SONY SOPHIA SOPHIE SOPHOCLES +SOU SOUTHAMPTON SOUTHEAST SOUTHERN @@ -2715,6 +2880,7 @@ STAFFORD STAFFORDSHIRE STALIN STAMFORD +STANDARDISATION STANFORD STANHOPE STANLEY @@ -2726,6 +2892,7 @@ STEPHANIE STEPHEN STEVE STEVENSON +STIGMA STIRLING STOCKHOLM STOIC @@ -2782,6 +2949,7 @@ SYRIA SYRIAN TACITUS TAIN +TAINT TAIWAN TALMUD TAMIL @@ -2805,6 +2973,7 @@ TENNESSEE TENNYSON TERENCE TERESA +TERRIBLY TERRY TERTIARY TESTAMENT @@ -2819,6 +2988,7 @@ THEBES THENCE THEODORE THERAPEUTICS +THERAPY THERESA THERETO THETA @@ -2827,6 +2997,7 @@ THOMAS THOMPSON THOMSON THRO +THROB THY TIBERIUS TIBET @@ -2838,6 +3009,7 @@ TIM TIMOR TIMOTHY TINA +TING TINKER TIT TITAN @@ -2858,7 +3030,9 @@ TORTURE TORY TOULOUSE TOXICOLOGY +TRAMPLE TRANSVAAL +TREACHEROUS TREASON TRENT TRENTON @@ -2873,6 +3047,7 @@ TROY TRUMAN TRUMP TSAR +TUBERCLE TUCK TUCKER TUDOR @@ -2890,6 +3065,7 @@ TV TWAIN TWEED TWITTER +TYPHOID TYRE TYRONE UCLA @@ -2904,12 +3080,17 @@ UNA UNCLASSIFIED UNCONSTITUTIONAL UNDERWOOD +UNDULY +UNEASINESS UNESCO UNFORTUNATELY UNIONIST UNITARIAN UNIX UNOBLIGATED +UNSATISFACTORY +UNWANTED +UPS URDU UREA URL @@ -2920,6 +3101,7 @@ USA USD USSR UTAH +UTERINE UTOPIA UTRECHT VAGINA @@ -2931,6 +3113,7 @@ VALENTINE VALLEY VAN VANCOUVER +VASSAL VAT VATICAN VEGA @@ -2950,8 +3133,10 @@ VERONICA VERSAILLES VET VETERINARY +VIBRATE VICE VICEROY +VICIOUS VICTOR VICTORIA VICTORIAN @@ -2960,10 +3145,12 @@ VIE VIENNA VIETNAM VIETNAMESE +VIGOROUSLY VIKING VILLA VINCENT VIOLA +VIOLENTLY VIRGIL VIRGIN VIRGINIA @@ -2975,6 +3162,7 @@ VIVIAN VIZ VLADIMIR VODKA +VOLATILE VOLTAIRE VULNERABILITY VULNERABLE @@ -2989,6 +3177,7 @@ WALNUT WALT WALTER WAN +WARNING WARREN WARSAW WARWICK @@ -2998,12 +3187,14 @@ WATERLOO WATERMAN WATSON WATT +WEAKLY WEAVER WEB WEBER WEBSTER WEIMAR WEIR +WEIRD WELCH WELLINGTON WELSH @@ -3011,6 +3202,7 @@ WEN WESTMINSTER WHEELER WHEELING +WHENCE WHEREFORE WHIG WHISKY @@ -3031,6 +3223,7 @@ WILTSHIRE WINCHESTER WINDSOR WINNIPEG +WIPE WISCONSIN WITTGENSTEIN WO diff --git a/wordlist/04-deduplicated-words.csv b/wordlist/04-deduplicated-words.csv new file mode 100644 index 0000000..13e5467 --- /dev/null +++ b/wordlist/04-deduplicated-words.csv @@ -0,0 +1,8851 @@ +WORD,NUMBER +THE,1 +OF,2 +BEE,3 +ARE,3 +BE,3 +TO,4 +A,5 +THAT,6 +HAVE,7 +FOR,8 +FOUR,8 +WITH,9 +BY,10 +BYE,10 +ON,11 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+HEM,8073 +HYMN,8073 +HIM,8073 +BOG,8074 +CHORE,8075 +STRATIFICATION,8076 +ANTHROPOLOGICAL,8077 +LEAFLET,8078 +CONSTANCY,8079 +SUBCUTANEOUS,8080 +FUNCTIONALITY,8081 +SEEDLING,8082 +LIGHTEN,8083 +OWL,8084 +REMORSE,8085 +MASCULINITY,8086 +ENRICHMENT,8087 +HAM,8088 +DISINTERESTED,8089 +SOCK,8090 +MALADY,8091 +COAGULATION,8092 +OFFSHORE,8093 +SUBTLETY,8094 +GENETICS,8095 +GENETIC,8095 +SOMBER,8096 +HABITUALLY,8097 +GASEOUS,8098 +THEORIZE,8099 +EQUATOR,8100 +HURRICANE,8101 +RIGIDLY,8102 +DEADLINE,8103 +TEMPERANCE,8104 +DECENTRALISATION,8105 +BRISTLE,8106 +ARROGANT,8107 +CRICKET,8108 +INTERRELATE,8109 +INTERRELATING,8109 +CONGENIAL,8110 +PROPHESY,8111 +DISMANTLE,8112 +SPIRITUALLY,8113 +PERSEVERANCE,8114 +TUMULT,8115 +HYSTERICAL,8116 +CORROBORATE,8117 +PERFORATE,8118 +OSTENSIBLY,8119 +PRUNE,8120 +PEEP,8121 +STRINGENT,8122 +CIRCUS,8123 +COMPANIONSHIP,8124 +JEWELRY,8125 +LUXURIOUS,8126 +ABYSS,8127 +PERILOUS,8128 +COWARD,8129 +PITCHER,8130 +SCRUTINIZE,8131 +IDOLATRY,8132 +CRATER,8133 +WHARF,8134 +ARDUOUS,8135 +KETTLE,8136 +MERCIFUL,8137 +SUNRISE,8138 +DEPARTMENTAL,8139 +GAIT,8140 +BACKUP,8141 +EXHORTATION,8142 +EMPHATIC,8143 +ABBREVIATION,8144 +DENUNCIATION,8145 +ENCROACHMENT,8146 +TREMOR,8147 +DOGMATIC,8148 +DOGMATICS,8148 +FLANGE,8149 +DEFLECT,8150 +JUSTIFIABLE,8151 +UNNOTICED,8152 +PROMINENTLY,8153 +INTERNATIONALLY,8154 +BOMBARDMENT,8155 +ASTOUND,8156 +FABRICATION,8157 +MANIFESTLY,8158 +REQUISITION,8159 +IRREVERSIBLE,8160 +DWINDLE,8161 +SWIRL,8162 +ICY,8163 +PRODIGIOUS,8164 +CYNICAL,8165 +ASYMMETRY,8166 +DECENTRALIZE,8167 +BACHELOR,8168 +PATRONIZE,8169 +PRONOUNCEMENT,8170 +MATERIALIZE,8171 +BENEFACTOR,8172 +EMULATE,8173 +ADJUNCT,8174 +FORETELL,8175 +SAW,8176 +TACT,8177 +SCOLD,8178 +COWBOY,8179 +MARTYRDOM,8180 +SCHEMATIC,8181 +ANTHOLOGY,8182 +RECTIFY,8183 +FASCIA,8184 +SIMPLIFICATION,8185 +HEREDITY,8186 +DISTILLATION,8187 +SEQUEL,8188 +WHISKEY,8189 +PLURALITY,8190 +CULTIVATOR,8191 +UNPAID,8192 +LINER,8193 +DICE,8194 diff --git a/wordlist/04-deduplicated-words.py b/wordlist/04-deduplicated-words.py new file mode 100755 index 0000000..fc6b055 --- /dev/null +++ b/wordlist/04-deduplicated-words.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import gzip +from pprint import pprint +from tqdm import tqdm + +# 2**13 + 2 since two can be skipped +WORDLIST_SIZE=8192+2 + +print("Loading full wordlist") + +all_words = [] +with gzip.open("./00-frequency-list.csv.gz", 'r') as infile: + first = True + for line in infile: + if first: + first = False + continue + all_words.append(line.decode('ascii').split(",")[0]) + +print("Building lemmatization graph") + +lemmatization_graph = list() +def add_lemmatization(word1, word2): + for lemmatization in lemmatization_graph: + word1_contained = word1 in lemmatization + word2_contained = word2 in lemmatization + + if word1_contained or word2_contained: + if word1_contained and word2_contained: + print(f"Warning: lemmatization {word1}<=>{word2} already in set: {lemmatization}") + + lemmatization.add(word1) + lemmatization.add(word2) + + # Success. We added the words + return + else: + # This lemmatization doesn't contain either. This is the common case + pass + + # If we get here, there is no known lemmatization between these two. Add it + lemmatization_graph.append(set((word1, word2))) + +def get_lemmatization(word): + for lemmatization in lemmatization_graph: + if word in lemmatization: + return lemmatization + +print("\tAdding automatic lemmatizations") +# First, iterate over automated lemmatizations +with gzip.open("./01-lemmatized-words.csv.gz") as infile: + first = True + for line in infile: + if first: + first = False + continue + split = line.decode('ascii').strip().split(",") + add_lemmatization(split[0], split[1]) + +print("\tAdding custom lemmatizations") +# Next, iterate over manual lemmatizations +with open("./02-custom-lemmatizations.csv") as infile: + first = True + for line in infile: + if first: + first = False + continue + split = line.strip().split(",") + add_lemmatization(split[0], split[1]) + +print("Lemmatization graph constructed:") +pprint(lemmatization_graph) + +print("Loading exclude wordlist") +with open("./03-exclude.csv") as infile: + first = True + exclude_words = set() + for line in infile: + if first: + first = False + continue + exclude_words.add(line.strip()) + +# Now, start printing the first WORDLIST_SIZE elements +seen_word_lemmatizations = set() +final_wordlist = [] +ending_word_index = 0 +for word in all_words: + ending_word_index += 1 + + word_lemmatizations = get_lemmatization(word) + + if not word_lemmatizations: + word_lemmatizations = set([word]) + + if len(word_lemmatizations - exclude_words) != len(word_lemmatizations) : + print(f"Note: {word_lemmatizations} is excluded") + continue + + if word_lemmatizations in seen_word_lemmatizations: + # We already added this one + continue + + final_wordlist.append(word_lemmatizations) + + if len(final_wordlist) >= WORDLIST_SIZE: + # We've added all the words we need + break + +assert len(final_wordlist) == WORDLIST_SIZE +pprint(list(enumerate(final_wordlist))) +print(f"Ending index: {ending_word_index}") + +final_wordlist = [ + (idx + 1, word) + for idx, words in enumerate(final_wordlist) + for word in words +] + +with open("./04-deduplicated-words.csv", 'w') as outfile: + outfile.write("WORD,NUMBER\n") + for (idx, word) in final_wordlist: + outfile.write(f"{word},{idx}\n") + # all_words.append(line.decode('ascii').split(",")[0]) diff --git a/wordlist/annotated_words.ods b/wordlist/annotated_words.ods deleted file mode 100644 index 6979649..0000000 Binary files a/wordlist/annotated_words.ods and /dev/null differ diff --git a/wordlist/requirements.txt b/wordlist/requirements.txt index f99d8bb..ef94b2c 100644 --- a/wordlist/requirements.txt +++ b/wordlist/requirements.txt @@ -6,6 +6,7 @@ asttokens==2.2.1 attrs==22.2.0 backcall==0.2.0 beautifulsoup4==4.11.2 +black==23.1.0 bleach==6.0.0 blis==0.7.9 catalogue==2.0.8 @@ -33,6 +34,7 @@ ipywidgets==8.0.4 isoduration==20.11.0 jedi==0.18.2 Jinja2==3.1.2 +joblib==1.2.0 jsonpointer==2.3 jsonschema==4.17.3 jupyter==1.0.0 @@ -49,11 +51,13 @@ MarkupSafe==2.1.2 matplotlib-inline==0.1.6 mistune==2.0.5 murmurhash==1.0.9 +mypy-extensions==1.0.0 nbclassic==0.5.2 nbclient==0.7.2 nbconvert==7.2.9 nbformat==5.7.3 nest-asyncio==1.5.6 +nltk==3.8.1 notebook==6.5.2 notebook_shim==0.2.2 numpy==1.24.2 @@ -66,6 +70,7 @@ packaging==23.0 pandas==1.5.3 pandocfilters==1.5.0 parso==0.8.3 +pathspec==0.11.0 pathy==0.10.1 pexpect==4.8.0 pickleshare==0.7.5 diff --git a/wordlist/wordlist-new.ipynb b/wordlist/wordlist-new.ipynb deleted file mode 100644 index 9978270..0000000 --- a/wordlist/wordlist-new.ipynb +++ /dev/null @@ -1,2657 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0b00342f-7b19-49cc-bc6c-21019f8cc7dc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting nltk\n", - " Downloading nltk-3.8.1-py3-none-any.whl (1.5 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", - "\u001b[?25hCollecting odfpy\n", - " Downloading odfpy-1.4.1.tar.gz (717 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m717.0/717.0 kB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting regex>=2021.8.3\n", - " Downloading regex-2022.10.31-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (770 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m770.5/770.5 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk) (1.2.0)\n", - "Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk) (8.1.3)\n", - "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk) (4.64.1)\n", - "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.10/site-packages (from odfpy) (0.7.1)\n", - "Building wheels for collected packages: odfpy\n", - " Building wheel for odfpy (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for odfpy: filename=odfpy-1.4.1-py2.py3-none-any.whl size=160672 sha256=5bfe9fcd7c590666411d404ea3e4ef0f704c9e62ff6621deb4ab09c84bec082a\n", - " Stored in directory: /home/jovyan/.cache/pip/wheels/c8/2e/95/90d94fe33903786937f3b8c33dd88807f792359c6424b40469\n", - "Successfully built odfpy\n", - "Installing collected packages: regex, odfpy, nltk\n", - "Successfully installed nltk-3.8.1 odfpy-1.4.1 regex-2022.10.31\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[nltk_data] Downloading package wordnet to /home/jovyan/nltk_data...\n" - ] - } - ], - "source": [ - "try:\n", - " _initialized\n", - "except:\n", - " !pip install nltk odfpy\n", - " import nltk\n", - " \n", - " nltk.download(\"wordnet\")\n", - " _initialized=True\n", - " \n", - "from nltk.stem.wordnet import WordNetLemmatizer\n", - "import pandas as pd\n", - "import gzip\n", - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "985883de-8049-4f81-acd9-34e1abcd4070", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def get_lines(filename):\n", - " with gzip.open(filename, 'r') as f:\n", - " ret = []\n", - " for l in f:\n", - " if len(ret) > 30_000:\n", - " return ret\n", - " ret.append(str(l).lower())\n", - " return ret\n", - "\n", - "\n", - " \n", - "WORDLIST_SIZE = 8192 + 3\n", - "lemmatizer = WordNetLemmatizer()\n", - "word_re = re.compile(r\"^[A-Za-z]+$\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "926d0d84-0d7e-4939-b87f-1a170f870a8f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "annotated_words=pd.read_excel(\"annotated_words.ods\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8b0d26e4-051c-4669-b566-bbd5ddbbe02b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['a', 'as', 'it', 'was', 'i', 'has', 'so', 'its', 's', 'p']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "excluded_words = list(annotated_words[annotated_words[\"keep\"] != \"Yes\"][\"word\"].str.lower())\n", - "excluded_words[0:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2eea14b2-82bf-4353-8982-76a6c7f46d22", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('be', 'bee'),\n", - " ('by', 'bye'),\n", - " ('per', 'purr'),\n", - " ('sense', 'cent'),\n", - " ('died', 'dyed'),\n", - " ('cents', 'sense'),\n", - " ('yellow', 'hello'),\n", - " ('corps', 'core'),\n", - " ('ore', 'oar'),\n", - " ('ore', ' or'),\n", - " ('vary', 'very'),\n", - " ('com', 'calm'),\n", - " ('filing', 'filling'),\n", - " ('fax', 'facts'),\n", - " ('favour', 'favor'),\n", - " ('theatre', 'theater'),\n", - " ('par', 'parse'),\n", - " ('honour', 'honor'),\n", - " ('harry', 'hairy'),\n", - " ('brings', 'bring'),\n", - " ('organisation', 'organization'),\n", - " ('simultaneously', 'simultaneous'),\n", - " ('aluminum', 'aluminium'),\n", - " ('knight', 'night'),\n", - " ('electronics', 'electronic'),\n", - " ('senses', 'cent'),\n", - " ('organisations', 'organization'),\n", - " ('fortunately', 'fortunate'),\n", - " ('corp', 'core'),\n", - " ('chile', 'chilly'),\n", - " ('chile', ' chili'),\n", - " ('owe', 'oh'),\n", - " ('capitol', 'capital'),\n", - " ('weary', 'wary'),\n", - " ('berry', 'barry'),\n", - " ('lecturer', 'lecture'),\n", - " ('weigh', 'way'),\n", - " ('aluminium', 'aluminum'),\n", - " ('isle', 'aisle'),\n", - " ('boulder', 'bolder'),\n", - " ('blew', 'blue'),\n", - " ('reformed', 'reform'),\n", - " ('scent', 'cent'),\n", - " ('ads', 'adds'),\n", - " ('honours', 'honors'),\n", - " ('bot', 'bought'),\n", - " ('dew', 'do'),\n", - " ('dew', ' due'),\n", - " ('theatres', 'theater'),\n", - " ('thru', 'through'),\n", - " ('sensed', 'cent'),\n", - " ('monies', 'moneys'),\n", - " ('cue', 'queue'),\n", - " ('hairy', 'harry'),\n", - " ('weighs', 'way'),\n", - " ('hem', 'him'),\n", - " ('nun', 'none'),\n", - " ('organisational', 'organizational'),\n", - " ('grate', 'great'),\n", - " ('dessert', 'desert'),\n", - " ('aux', 'ox'),\n", - " ('rap', 'wrap'),\n", - " ('filings', 'filling'),\n", - " ('pars', 'parse'),\n", - " ('dazed', 'day'),\n", - " ('scents', 'cent'),\n", - " ('daze', 'day'),\n", - " ('four', 'for')]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "custom_maps = annotated_words[annotated_words[\"maps_to\"].notna()][[\"word\",\"maps_to\"]].assign(maps_to=lambda x: x[\"maps_to\"].map(lambda y: y.split(\",\")))\n", - "\n", - "custom_maps = [\n", - " (m[1][\"word\"].lower(), mapping.lower())\n", - " for m in custom_maps.iterrows()\n", - " for mapping in m[1][\"maps_to\"]\n", - "]\n", - "custom_maps" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "8bdfd108-bf43-4c0f-bc5c-f91925da753f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Start parsing the wordlist\n", - "all_words = get_lines(\"frequency-all.txt.gz\")\n", - "\n", - "# Delete header line\n", - "all_words = all_words[1:]\n", - "\n", - "# Get only the word (fixed width)\n", - "all_words = [w[13:36].strip() for w in all_words]\n", - "\n", - "# Remove special characters\n", - "all_words = [w for w in all_words if word_re.search(w)]\n", - "\n", - "# Remove all removed words\n", - "all_words = [w for w in all_words if w not in excluded_words]\n", - "\n", - "# Add all custom mappings\n", - "for m in list(sum(custom_maps, ())):\n", - " if m[0] not in all_words:\n", - " all_words.append(m[0])\n", - " if m[1] not in all_words:\n", - " all_words.append(m[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e42f2b56-98b3-4465-95be-812d8584b511", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['the',\n", - " 'of',\n", - " 'and',\n", - " 'to',\n", - " 'in',\n", - " 'is',\n", - " 'that',\n", - " 'for',\n", - " 'be',\n", - " 'by',\n", - " 'with',\n", - " 'on',\n", - " 'not',\n", - " 'or',\n", - " 'this',\n", - " 'are',\n", - " 'at',\n", - " 'from',\n", - " 'he',\n", - " 'which',\n", - " 'his',\n", - " 'have',\n", - " 'an',\n", - " 'but',\n", - " 'you',\n", - " 'they',\n", - " 'were',\n", - " 'had',\n", - " 'we',\n", - " 'all',\n", - " 'one',\n", - " 'their',\n", - " 'been',\n", - " 'will',\n", - " 'there',\n", - " 'can',\n", - " 'if',\n", - " 'other',\n", - " 'would',\n", - " 'no',\n", - " 'her',\n", - " 'may',\n", - " 'more',\n", - " 'when',\n", - " 'who',\n", - " 'such',\n", - " 'these',\n", - " 'any',\n", - " 'she',\n", - " 'new',\n", - " 'time',\n", - " 'than',\n", - " 'do',\n", - " 'some',\n", - " 'what',\n", - " 'only',\n", - " 'into',\n", - " 'them',\n", - " 'two',\n", - " 'also',\n", - " 'about',\n", - " 'out',\n", - " 'him',\n", - " 'my',\n", - " 'said',\n", - " 'up',\n", - " 'our',\n", - " 'first',\n", - " 'should',\n", - " 'under',\n", - " 'made',\n", - " 'state',\n", - " 'see',\n", - " 'after',\n", - " 'could',\n", - " 'then',\n", - " 'me',\n", - " 'most',\n", - " 'over',\n", - " 'very',\n", - " 'your',\n", - " 'between',\n", - " 'where',\n", - " 'now',\n", - " 'shall',\n", - " 'work',\n", - " 'those',\n", - " 'same',\n", - " 'well',\n", - " 'each',\n", - " 'many',\n", - " 'being',\n", - " 'years',\n", - " 'did',\n", - " 'year',\n", - " 'through',\n", - " 'must',\n", - " 'upon',\n", - " 'before',\n", - " 'like',\n", - " 'use',\n", - " 'part',\n", - " 'general',\n", - " 'people',\n", - " 'because',\n", - " 'used',\n", - " 'how',\n", - " 'even',\n", - " 'much',\n", - " 'states',\n", - " 'during',\n", - " 'both',\n", - " 'case',\n", - " 'three',\n", - " 'number',\n", - " 'make',\n", - " 'per',\n", - " 'great',\n", - " 'act',\n", - " 'way',\n", - " 'life',\n", - " 'good',\n", - " 'day',\n", - " 'public',\n", - " 'man',\n", - " 'however',\n", - " 'system',\n", - " 'water',\n", - " 'without',\n", - " 'government',\n", - " 'while',\n", - " 'long',\n", - " 'order',\n", - " 'law',\n", - " 'section',\n", - " 'court',\n", - " 'high',\n", - " 'right',\n", - " 'own',\n", - " 'found',\n", - " 'united',\n", - " 'just',\n", - " 'here',\n", - " 'against',\n", - " 'world',\n", - " 'does',\n", - " 'company',\n", - " 'within',\n", - " 'given',\n", - " 'service',\n", - " 'house',\n", - " 'another',\n", - " 'power',\n", - " 'place',\n", - " 'know',\n", - " 'little',\n", - " 'down',\n", - " 'present',\n", - " 'every',\n", - " 'national',\n", - " 'back',\n", - " 'take',\n", - " 'information',\n", - " 'men',\n", - " 'since',\n", - " 'might',\n", - " 'small',\n", - " 'large',\n", - " 'school',\n", - " 'following',\n", - " 'still',\n", - " 'less',\n", - " 'last',\n", - " 'city',\n", - " 'second',\n", - " 'development',\n", - " 'different',\n", - " 'university',\n", - " 'old',\n", - " 'form',\n", - " 'point',\n", - " 'total',\n", - " 'data',\n", - " 'too',\n", - " 'committee',\n", - " 'report',\n", - " 'business',\n", - " 'think',\n", - " 'end',\n", - " 'get',\n", - " 'set',\n", - " 'research',\n", - " 'say',\n", - " 'come',\n", - " 'country',\n", - " 'never',\n", - " 'fact',\n", - " 'go',\n", - " 'control',\n", - " 'thus',\n", - " 'having',\n", - " 'value',\n", - " 'social',\n", - " 'department',\n", - " 'few',\n", - " 'above',\n", - " 'important',\n", - " 'interest',\n", - " 'study',\n", - " 'off',\n", - " 'area',\n", - " 'means',\n", - " 'office',\n", - " 'group',\n", - " 'give',\n", - " 'again',\n", - " 'war',\n", - " 'whether',\n", - " 'question',\n", - " 'called',\n", - " 'period',\n", - " 'line',\n", - " 'land',\n", - " 'four',\n", - " 'among',\n", - " 'table',\n", - " 'board',\n", - " 'until',\n", - " 'hand',\n", - " 'taken',\n", - " 'need',\n", - " 'education',\n", - " 'certain',\n", - " 'county',\n", - " 'action',\n", - " 'several',\n", - " 'am',\n", - " 'course',\n", - " 'cases',\n", - " 'far',\n", - " 'effect',\n", - " 'possible',\n", - " 'though',\n", - " 'left',\n", - " 'further',\n", - " 'home',\n", - " 'days',\n", - " 'person',\n", - " 'health',\n", - " 'amount',\n", - " 'members',\n", - " 'subject',\n", - " 'yet',\n", - " 'program',\n", - " 'therefore',\n", - " 'process',\n", - " 'services',\n", - " 'rate',\n", - " 'local',\n", - " 'name',\n", - " 'find',\n", - " 'necessary',\n", - " 'often',\n", - " 'others',\n", - " 'whole',\n", - " 'change',\n", - " 'example',\n", - " 'president',\n", - " 'history',\n", - " 'best',\n", - " 'although',\n", - " 'family',\n", - " 'side',\n", - " 'women',\n", - " 'held',\n", - " 'based',\n", - " 'south',\n", - " 'special',\n", - " 'required',\n", - " 'came',\n", - " 'thought',\n", - " 'five',\n", - " 'always',\n", - " 'himself',\n", - " 'air',\n", - " 'known',\n", - " 'head',\n", - " 'either',\n", - " 'property',\n", - " 'cost',\n", - " 'rather',\n", - " 'bill',\n", - " 'put',\n", - " 'human',\n", - " 'figure',\n", - " 'results',\n", - " 'level',\n", - " 'conditions',\n", - " 'full',\n", - " 'times',\n", - " 'book',\n", - " 'available',\n", - " 'early',\n", - " 'matter',\n", - " 'common',\n", - " 'light',\n", - " 'let',\n", - " 'society',\n", - " 'body',\n", - " 'international',\n", - " 'including',\n", - " 'free',\n", - " 'evidence',\n", - " 'better',\n", - " 'type',\n", - " 'provided',\n", - " 'due',\n", - " 'next',\n", - " 'production',\n", - " 'once',\n", - " 'done',\n", - " 'making',\n", - " 'least',\n", - " 'support',\n", - " 'north',\n", - " 'later',\n", - " 'using',\n", - " 'things',\n", - " 'economic',\n", - " 'chapter',\n", - " 'various',\n", - " 'why',\n", - " 'white',\n", - " 'going',\n", - " 'commission',\n", - " 'federal',\n", - " 'away',\n", - " 'field',\n", - " 'result',\n", - " 'nature',\n", - " 'policy',\n", - " 'become',\n", - " 'political',\n", - " 'increase',\n", - " 'around',\n", - " 'age',\n", - " 'want',\n", - " 'low',\n", - " 'trade',\n", - " 'half',\n", - " 'position',\n", - " 'young',\n", - " 'money',\n", - " 'percent',\n", - " 'cent',\n", - " 'class',\n", - " 'words',\n", - " 'view',\n", - " 'provide',\n", - " 'seen',\n", - " 'show',\n", - " 'district',\n", - " 'party',\n", - " 'analysis',\n", - " 'care',\n", - " 'june',\n", - " 'foreign',\n", - " 'shown',\n", - " 'received',\n", - " 'management',\n", - " 'third',\n", - " 'took',\n", - " 'something',\n", - " 'tax',\n", - " 'account',\n", - " 'problem',\n", - " 'almost',\n", - " 'west',\n", - " 'nothing',\n", - " 'together',\n", - " 'individual',\n", - " 'open',\n", - " 'material',\n", - " 'paper',\n", - " 'feet',\n", - " 'force',\n", - " 'association',\n", - " 'purpose',\n", - " 'terms',\n", - " 'method',\n", - " 'help',\n", - " 'real',\n", - " 'ever',\n", - " 'already',\n", - " 'along',\n", - " 'went',\n", - " 'term',\n", - " 'systems',\n", - " 'member',\n", - " 'particular',\n", - " 'problems',\n", - " 'energy',\n", - " 'secretary',\n", - " 'date',\n", - " 'price',\n", - " 'short',\n", - " 'true',\n", - " 'street',\n", - " 'building',\n", - " 'room',\n", - " 'market',\n", - " 'look',\n", - " 'similar',\n", - " 'industry',\n", - " 'areas',\n", - " 'bank',\n", - " 'according',\n", - " 'studies',\n", - " 'itself',\n", - " 'application',\n", - " 'current',\n", - " 'read',\n", - " 'press',\n", - " 'community',\n", - " 'plan',\n", - " 'whose',\n", - " 'major',\n", - " 'considered',\n", - " 'mind',\n", - " 'union',\n", - " 'cause',\n", - " 'able',\n", - " 'surface',\n", - " 'face',\n", - " 'river',\n", - " 'council',\n", - " 'income',\n", - " 'july',\n", - " 'near',\n", - " 'experience',\n", - " 'non',\n", - " 'paid',\n", - " 'pay',\n", - " 'reason',\n", - " 'themselves',\n", - " 'asked',\n", - " 'march',\n", - " 'king',\n", - " 'higher',\n", - " 'single',\n", - " 'rights',\n", - " 'average',\n", - " 'father',\n", - " 'note',\n", - " 'treatment',\n", - " 'love',\n", - " 'changes',\n", - " 'black',\n", - " 'knowledge',\n", - " 'enough',\n", - " 'future',\n", - " 'kind',\n", - " 'lower',\n", - " 'authority',\n", - " 'past',\n", - " 'natural',\n", - " 'six',\n", - " 'persons',\n", - " 'food',\n", - " 'working',\n", - " 'central',\n", - " 'college',\n", - " 'self',\n", - " 'products',\n", - " 'model',\n", - " 'brought',\n", - " 'greater',\n", - " 'countries',\n", - " 'test',\n", - " 'nor',\n", - " 'students',\n", - " 'private',\n", - " 'construction',\n", - " 'perhaps',\n", - " 'ground',\n", - " 'sir',\n", - " 'basis',\n", - " 'months',\n", - " 'growth',\n", - " 'increased',\n", - " 'word',\n", - " 'east',\n", - " 'language',\n", - " 'rule',\n", - " 'continued',\n", - " 'quite',\n", - " 'except',\n", - " 'series',\n", - " 'practice',\n", - " 'thing',\n", - " 'night',\n", - " 'works',\n", - " 'eyes',\n", - " 'oil',\n", - " 'art',\n", - " 'told',\n", - " 'especially',\n", - " 'population',\n", - " 'science',\n", - " 'whom',\n", - " 'obtained',\n", - " 'parts',\n", - " 'capital',\n", - " 'include',\n", - " 'generally',\n", - " 'meeting',\n", - " 'specific',\n", - " 'described',\n", - " 'believe',\n", - " 'review',\n", - " 'issue',\n", - " 'respect',\n", - " 'contract',\n", - " 'became',\n", - " 'effects',\n", - " 'medical',\n", - " 'road',\n", - " 'got',\n", - " 'clear',\n", - " 'main',\n", - " 'labor',\n", - " 'operation',\n", - " 'size',\n", - " 'below',\n", - " 'hours',\n", - " 'sense',\n", - " 'addition',\n", - " 'probably',\n", - " 'mean',\n", - " 'groups',\n", - " 'century',\n", - " 'personal',\n", - " 'plant',\n", - " 'training',\n", - " 'design',\n", - " 'statement',\n", - " 'structure',\n", - " 'project',\n", - " 'million',\n", - " 'usually',\n", - " 'range',\n", - " 'call',\n", - " 'mother',\n", - " 'seems',\n", - " 'standard',\n", - " 'return',\n", - " 'title',\n", - " 'established',\n", - " 'keep',\n", - " 'space',\n", - " 'annual',\n", - " 'record',\n", - " 'close',\n", - " 'april',\n", - " 'complete',\n", - " 'page',\n", - " 'heart',\n", - " 'says',\n", - " 'fig',\n", - " 'quality',\n", - " 'gas',\n", - " 'methods',\n", - " 'letter',\n", - " 'stock',\n", - " 'costs',\n", - " 'gave',\n", - " 'related',\n", - " 'administration',\n", - " 'activities',\n", - " 'condition',\n", - " 'theory',\n", - " 'town',\n", - " 'equipment',\n", - " 'rates',\n", - " 'soon',\n", - " 'decision',\n", - " 'pressure',\n", - " 'written',\n", - " 'lines',\n", - " 'corporation',\n", - " 'tell',\n", - " 'schools',\n", - " 'agreement',\n", - " 'reported',\n", - " 'attention',\n", - " 'materials',\n", - " 'fire',\n", - " 'direct',\n", - " 'saw',\n", - " 'published',\n", - " 'temperature',\n", - " 'species',\n", - " 'really',\n", - " 'laws',\n", - " 'woman',\n", - " 'function',\n", - " 'military',\n", - " 'proposed',\n", - " 'january',\n", - " 'additional',\n", - " 'late',\n", - " 'books',\n", - " 'opinion',\n", - " 'loss',\n", - " 'limited',\n", - " 'source',\n", - " 'article',\n", - " 'notice',\n", - " 'security',\n", - " 'organization',\n", - " 'hands',\n", - " 'financial',\n", - " 'rules',\n", - " 'follows',\n", - " 'miles',\n", - " 'values',\n", - " 'points',\n", - " 'chief',\n", - " 'distribution',\n", - " 'sometimes',\n", - " 'insurance',\n", - " 'son',\n", - " 'strong',\n", - " 'length',\n", - " 'activity',\n", - " 'original',\n", - " 'forms',\n", - " 'yes',\n", - " 'effective',\n", - " 'defendant',\n", - " 'living',\n", - " 'december',\n", - " 'character',\n", - " 'began',\n", - " 'carried',\n", - " 'supply',\n", - " 'blood',\n", - " 'taking',\n", - " 'manner',\n", - " 'journal',\n", - " 'hundred',\n", - " 'red',\n", - " 'shows',\n", - " 'developed',\n", - " 'performance',\n", - " 'situation',\n", - " 'programs',\n", - " 'felt',\n", - " 'workers',\n", - " 'volume',\n", - " 'presented',\n", - " 'knew',\n", - " 'answer',\n", - " 'resources',\n", - " 'questions',\n", - " 'industrial',\n", - " 'needs',\n", - " 'twenty',\n", - " 'sent',\n", - " 'looked',\n", - " 'purposes',\n", - " 'library',\n", - " 'added',\n", - " 'passed',\n", - " 'ten',\n", - " 'sea',\n", - " 'applied',\n", - " 'included',\n", - " 'physical',\n", - " 'across',\n", - " 'army',\n", - " 'toward',\n", - " 'produced',\n", - " 'makes',\n", - " 'placed',\n", - " 'role',\n", - " 'october',\n", - " 'final',\n", - " 'approach',\n", - " 'provisions',\n", - " 'leave',\n", - " 'director',\n", - " 'employment',\n", - " 'anything',\n", - " 'particularly',\n", - " 'hard',\n", - " 'outside',\n", - " 'week',\n", - " 'feel',\n", - " 'charge',\n", - " 'indeed',\n", - " 'degree',\n", - " 'reference',\n", - " 'requirements',\n", - " 'september',\n", - " 'today',\n", - " 'western',\n", - " 'influence',\n", - " 'unit',\n", - " 'solution',\n", - " 'chairman',\n", - " 'legal',\n", - " 'motion',\n", - " 'region',\n", - " 'idea',\n", - " 'list',\n", - " 'judgment',\n", - " 'determined',\n", - " 'poor',\n", - " 'disease',\n", - " 'civil',\n", - " 'turn',\n", - " 'modern',\n", - " 'normal',\n", - " 'appear',\n", - " 'employees',\n", - " 'latter',\n", - " 'heard',\n", - " 'top',\n", - " 'sure',\n", - " 'moment',\n", - " 'code',\n", - " 'reports',\n", - " 'wife',\n", - " 'post',\n", - " 'difficult',\n", - " 'recent',\n", - " 'extent',\n", - " 'longer',\n", - " 'story',\n", - " 'meet',\n", - " 'officers',\n", - " 'companies',\n", - " 'patients',\n", - " 'front',\n", - " 'doing',\n", - " 'staff',\n", - " 'product',\n", - " 'august',\n", - " 'needed',\n", - " 'involved',\n", - " 'likely',\n", - " 'former',\n", - " 'run',\n", - " 'author',\n", - " 'middle',\n", - " 'turned',\n", - " 'agency',\n", - " 'reading',\n", - " 'beginning',\n", - " 'duty',\n", - " 'movement',\n", - " 'month',\n", - " 'alone',\n", - " 'issues',\n", - " 'beyond',\n", - " 'fine',\n", - " 'base',\n", - " 'parties',\n", - " 'relations',\n", - " 'simple',\n", - " 'consider',\n", - " 'proper',\n", - " 'instead',\n", - " 'significant',\n", - " 'appears',\n", - " 'equal',\n", - " 'lost',\n", - " 'followed',\n", - " 'hope',\n", - " 'cut',\n", - " 'unless',\n", - " 'nearly',\n", - " 'claim',\n", - " 'associated',\n", - " 'expected',\n", - " 'operations',\n", - " 'difference',\n", - " 'funds',\n", - " 'direction',\n", - " 'cross',\n", - " 'live',\n", - " 'finally',\n", - " 'weight',\n", - " 'lead',\n", - " 'trial',\n", - " 'justice',\n", - " 'officer',\n", - " 'factors',\n", - " 'response',\n", - " 'cells',\n", - " 'earth',\n", - " 'rest',\n", - " 'fund',\n", - " 'bring',\n", - " 'trust',\n", - " 'goods',\n", - " 'observed',\n", - " 'behind',\n", - " 'job',\n", - " 'door',\n", - " 'types',\n", - " 'understand',\n", - " 'acid',\n", - " 'hold',\n", - " 'technology',\n", - " 'wide',\n", - " 'protection',\n", - " 'basic',\n", - " 'november',\n", - " 'seemed',\n", - " 'throughout',\n", - " 'levels',\n", - " 'importance',\n", - " 'sales',\n", - " 'sale',\n", - " 'stated',\n", - " 'address',\n", - " 'potential',\n", - " 'payment',\n", - " 'prior',\n", - " 'discussion',\n", - " 'conference',\n", - " 'writing',\n", - " 'stage',\n", - " 'fall',\n", - " 'notes',\n", - " 'iron',\n", - " 'play',\n", - " 'ask',\n", - " 'plants',\n", - " 'relationship',\n", - " 'towards',\n", - " 'regard',\n", - " 'referred',\n", - " 'patient',\n", - " 'flow',\n", - " 'consideration',\n", - " 'hospital',\n", - " 'seem',\n", - " 'february',\n", - " 'soil',\n", - " 'morning',\n", - " 'commercial',\n", - " 'planning',\n", - " 'provides',\n", - " 'appropriate',\n", - " 'technical',\n", - " 'demand',\n", - " 'sufficient',\n", - " 'principal',\n", - " 'credit',\n", - " 'peace',\n", - " 'previous',\n", - " 'object',\n", - " 'prices',\n", - " 'kept',\n", - " 'sound',\n", - " 'wanted',\n", - " 'looking',\n", - " 'entire',\n", - " 'plaintiff',\n", - " 'heat',\n", - " 'ways',\n", - " 'otherwise',\n", - " 'judge',\n", - " 'hour',\n", - " 'capacity',\n", - " 'brown',\n", - " 'music',\n", - " 'risk',\n", - " 'box',\n", - " 'exchange',\n", - " 'produce',\n", - " 'station',\n", - " 'big',\n", - " 'primary',\n", - " 'institute',\n", - " 'mentioned',\n", - " 'prepared',\n", - " 'cell',\n", - " 'spirit',\n", - " 'allowed',\n", - " 'claims',\n", - " 'site',\n", - " 'green',\n", - " 'directly',\n", - " 'text',\n", - " 'friends',\n", - " 'presence',\n", - " 'survey',\n", - " 'determine',\n", - " 'car',\n", - " 'larger',\n", - " 'gives',\n", - " 'deep',\n", - " 'simply',\n", - " 'immediately',\n", - " 'distance',\n", - " 'coming',\n", - " 'seven',\n", - " 'steel',\n", - " 'friend',\n", - " 'records',\n", - " 'existing',\n", - " 'clearly',\n", - " 'actual',\n", - " 'relation',\n", - " 'born',\n", - " 'learning',\n", - " 'forces',\n", - " 'voice',\n", - " 'earlier',\n", - " 'circumstances',\n", - " 'safety',\n", - " 'ago',\n", - " 'issued',\n", - " 'upper',\n", - " 'require',\n", - " 'scale',\n", - " 'island',\n", - " 'culture',\n", - " 'employed',\n", - " 'eight',\n", - " 'estate',\n", - " 'facts',\n", - " 'portion',\n", - " 'deal',\n", - " 'share',\n", - " 'actually',\n", - " 'aid',\n", - " 'engineering',\n", - " 'continue',\n", - " 'formed',\n", - " 'agricultural',\n", - " 'entitled',\n", - " 'mass',\n", - " 'truth',\n", - " 'giving',\n", - " 'numbers',\n", - " 'places',\n", - " 'met',\n", - " 'built',\n", - " 'content',\n", - " 'connection',\n", - " 'assistance',\n", - " 'coal',\n", - " 'progress',\n", - " 'receive',\n", - " 'active',\n", - " 'nation',\n", - " 'contact',\n", - " 'amendment',\n", - " 'interests',\n", - " 'net',\n", - " 'wall',\n", - " 'standards',\n", - " 'farm',\n", - " 'understanding',\n", - " 'strength',\n", - " 'minutes',\n", - " 'figures',\n", - " 'move',\n", - " 'elements',\n", - " 'concerned',\n", - " 'regulations',\n", - " 'step',\n", - " 'literature',\n", - " 'units',\n", - " 'opportunity',\n", - " 'investment',\n", - " 'led',\n", - " 'reduced',\n", - " 'follow',\n", - " 'facilities',\n", - " 'benefit',\n", - " 'compared',\n", - " 'reached',\n", - " 'student',\n", - " 'religious',\n", - " 'measure',\n", - " 'individuals',\n", - " 'meaning',\n", - " 'considerable',\n", - " 'relative',\n", - " 'electric',\n", - " 'joint',\n", - " 'certainly',\n", - " 'failure',\n", - " 'apply',\n", - " ...]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_words" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd9e939e-7827-42f9-89be-bcfbb8bd3f52", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "64b6fcd3-acf7-45da-a335-79c538963bdd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "discus\n", - "physic\n", - "posse\n", - "serf\n", - "sens\n", - "caput\n", - "bos\n", - "graf\n", - "pant\n", - "barrack\n", - "auspex\n", - "footstep\n", - "colonist\n", - "villager\n", - "kilometer\n", - "granule\n", - "credential\n", - "petal\n", - "trouser\n", - "shortcoming\n", - "microorganism\n", - "italic\n", - "grandchild\n", - "munition\n", - "parenthesis\n", - "foodstuff\n", - "attache\n", - "grandparent\n", - "tropic\n", - "kilometre\n", - "congratulation\n", - "fume\n", - "convulsion\n", - "nostril\n", - "utensil\n", - "cooky\n", - "amenity\n", - "reptile\n", - "pretension\n", - "sock\n", - "peso\n", - "mitochondrion\n", - "reminiscence\n", - "invader\n", - "macrophage\n", - "eyelid\n", - "dweller\n", - "bristle\n", - "tenet\n", - "taxon\n", - "outskirt\n", - "policyholder\n", - "stamen\n", - "horseman\n", - "striker\n", - "ramification\n", - "tuft\n", - "cultivar\n", - "interrogatory\n", - "bylaw\n", - "bellow\n", - "neoplasm\n", - "insurgent\n", - "chore\n", - "pensioner\n", - "exigency\n", - "forefather\n", - "atrocity\n", - "dissenter\n", - "corpuscle\n", - "islander\n", - "numeral\n", - "bureaucrat\n", - "classmate\n", - "crossroad\n", - "pitfall\n", - "firework\n", - "ravage\n", - "broadcaster\n", - "heretic\n", - "appurtenance\n", - "potentiality\n", - "louse\n", - "conspirator\n", - "revers\n", - "combatant\n", - "conferee\n", - "serviceman\n", - "repercussion\n", - "grader\n", - "exhibitor\n", - "alkaloid\n", - "collaborator\n", - "slipper\n", - "foothill\n", - "homeowner\n", - "hallucination\n", - "ailment\n", - "crumb\n", - "milligram\n", - "turnip\n", - "fingertip\n", - "tradesman\n", - "archaeologist\n", - "bondholder\n", - "lira\n", - "emolument\n", - "tailing\n", - "enthusiast\n", - "tubule\n", - "warship\n", - "speculator\n", - "jobber\n", - "raisin\n", - "vicissitude\n", - "courtier\n", - "clove\n", - "entrant\n", - "festivity\n", - "bough\n", - "imago\n", - "fibroblast\n", - "bruise\n", - "misgiving\n", - "parishioner\n", - "bract\n", - "microbe\n", - "industrialist\n", - "sprout\n", - "wrinkle\n", - "worshipper\n", - "retiree\n", - "cracker\n", - "negotiator\n", - "pronouncement\n", - "devotee\n", - "sandal\n", - "sepal\n", - "interrelationship\n", - "corticosteroid\n", - "sou\n", - "framer\n", - "knuckle\n", - "leukocyte\n", - "malformation\n", - "geographer\n", - "fastener\n", - "ruble\n", - "whisker\n", - "tentacle\n", - "footprint\n", - "ratepayer\n", - "marketer\n", - "refiner\n", - "cilium\n", - "inroad\n", - "dragoon\n", - "litigant\n", - "kilo\n", - "shipowner\n", - "rudiment\n", - "appointee\n", - "fingerprint\n", - "anther\n", - "depredation\n", - "stave\n", - "rancher\n", - "cytokine\n", - "artefact\n", - "freeholder\n", - "churchman\n", - "fungicide\n", - "inequity\n", - "contraindication\n", - "arrhythmia\n", - "functionary\n", - "bandit\n", - "horde\n", - "spermatozoon\n", - "selectman\n", - "blocker\n", - "inaccuracy\n", - "gramme\n", - "billet\n", - "radiograph\n", - "demonstrator\n", - "amphibian\n", - "mussel\n", - "rafter\n", - "headlight\n", - "vestige\n", - "loin\n", - "raider\n", - "crevice\n", - "suitor\n", - "technologist\n", - "trooper\n", - "globule\n", - "firefighter\n", - "woodcut\n", - "purr\n", - " or\n", - "parse\n", - " chili\n", - "bolder\n", - " due\n", - "scents\n", - "daze\n" - ] - } - ], - "source": [ - "# Lemmatize all words (plural -> singular)\n", - "lemmatize_mappings = [\n", - " (w, lemmatizer.lemmatize(w)) \n", - " for w in all_words\n", - " # if w != lemmatizer.lemmatize(w)\n", - "]\n", - "\n", - "# Remove all words that lemmatize to another word\n", - "#all_words = [w for w in all_words if w not in ]\n", - "\n", - "# Add custom lemmatizations\n", - "for l in custom_maps:\n", - " if l in lemmatize_mappings:\n", - " print(f\"Warning: {l} is already lemmatized\")\n", - " else:\n", - " lemmatize_mappings.append(l)\n", - " \n", - "lemmatize_mappings = [w for w in lemmatize_mappings if w[1] not in excluded_words]\n", - "\n", - "# Now, re-add all lematized words to the list of every word\n", - "for w in sum(lemmatize_mappings, ()):\n", - " if w not in all_words:\n", - " print(w)\n", - " all_words.append(w)\n", - " \n", - "lemmatize_mappings = {k: v for k, v in lemmatize_mappings}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8bdff9d0-f3ff-498f-952d-13f1a91bfbd5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "final_wordlist = []\n", - "seen_lemmatizations = set()\n", - "for w in all_words:\n", - " lemmatized = lemmatize_mappings.get(w) or w\n", - " if lemmatized in seen_lemmatizations:\n", - " # The lemmatized version of this word was already seen\n", - " continue\n", - " else:\n", - " # The lemmatized version hasn't been seen. We're good to add it\n", - " final_wordlist.append([\n", - " k\n", - " for k\n", - " in lemmatize_mappings.keys()\n", - " if lemmatize_mappings[k] == lemmatized\n", - " ])\n", - " seen_lemmatizations.add(lemmatized)\n", - "\n", - " if len(final_wordlist) >= WORDLIST_SIZE:\n", - " break\n", - "\n", - "# Now, convert it to the format (number, word)\n", - "final_wordlist = [\n", - " (idx, w)\n", - " for idx, words in enumerate(final_wordlist)\n", - " for w in words\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "65bd6887-613e-45ae-ac45-6ed5967b3a43", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 'the'),\n", - " (1, 'of'),\n", - " (2, 'and'),\n", - " (3, 'to'),\n", - " (4, 'in'),\n", - " (5, 'is'),\n", - " (6, 'that'),\n", - " (7, 'for'),\n", - " (7, 'four'),\n", - " (8, 'be'),\n", - " (8, 'bee'),\n", - " (8, 'bees'),\n", - " (9, 'by'),\n", - " (9, 'bye'),\n", - " (10, 'with'),\n", - " (11, 'on'),\n", - " (12, 'not'),\n", - " (13, 'or'),\n", - " (14, 'this'),\n", - " (15, 'are'),\n", - " (16, 'at'),\n", - " (17, 'from'),\n", - " (18, 'he'),\n", - " (19, 'which'),\n", - " (20, 'his'),\n", - " (21, 'have'),\n", - " (22, 'an'),\n", - " (23, 'but'),\n", - " (24, 'you'),\n", - " (25, 'they'),\n", - " (26, 'were'),\n", - " (27, 'had'),\n", - " (28, 'we'),\n", - " (29, 'all'),\n", - " (30, 'one'),\n", - " (30, 'ones'),\n", - " (31, 'their'),\n", - " (32, 'been'),\n", - " (33, 'will'),\n", - " (33, 'wills'),\n", - " (34, 'there'),\n", - " (35, 'can'),\n", - " (35, 'cans'),\n", - " (36, 'if'),\n", - " (37, 'other'),\n", - " (38, 'would'),\n", - " (39, 'no'),\n", - " (39, 'nos'),\n", - " (40, 'her'),\n", - " (41, 'may'),\n", - " (42, 'more'),\n", - " (42, 'mores'),\n", - " (43, 'when'),\n", - " (44, 'who'),\n", - " (45, 'such'),\n", - " (46, 'these'),\n", - " (47, 'any'),\n", - " (48, 'she'),\n", - " (49, 'new'),\n", - " (50, 'time'),\n", - " (50, 'times'),\n", - " (51, 'than'),\n", - " (52, 'do'),\n", - " (53, 'some'),\n", - " (54, 'what'),\n", - " (55, 'only'),\n", - " (56, 'into'),\n", - " (57, 'them'),\n", - " (58, 'two'),\n", - " (59, 'also'),\n", - " (60, 'about'),\n", - " (61, 'out'),\n", - " (61, 'outs'),\n", - " (62, 'him'),\n", - " (62, 'hem'),\n", - " (63, 'my'),\n", - " (64, 'said'),\n", - " (65, 'up'),\n", - " (66, 'our'),\n", - " (67, 'first'),\n", - " (68, 'should'),\n", - " (69, 'under'),\n", - " (70, 'made'),\n", - " (71, 'state'),\n", - " (71, 'states'),\n", - " (72, 'see'),\n", - " (72, 'sees'),\n", - " (73, 'after'),\n", - " (74, 'could'),\n", - " (75, 'then'),\n", - " (76, 'me'),\n", - " (77, 'most'),\n", - " (78, 'over'),\n", - " (79, 'very'),\n", - " (79, 'vary'),\n", - " (80, 'your'),\n", - " (81, 'between'),\n", - " (82, 'where'),\n", - " (83, 'now'),\n", - " (84, 'shall'),\n", - " (85, 'work'),\n", - " (85, 'works'),\n", - " (86, 'those'),\n", - " (87, 'same'),\n", - " (88, 'well'),\n", - " (88, 'wells'),\n", - " (89, 'each'),\n", - " (90, 'many'),\n", - " (91, 'being'),\n", - " (91, 'beings'),\n", - " (92, 'years'),\n", - " (92, 'year'),\n", - " (93, 'did'),\n", - " (94, 'through'),\n", - " (94, 'thru'),\n", - " (95, 'must'),\n", - " (96, 'upon'),\n", - " (97, 'before'),\n", - " (98, 'like'),\n", - " (98, 'likes'),\n", - " (99, 'use'),\n", - " (100, 'part'),\n", - " (100, 'parts'),\n", - " (101, 'general'),\n", - " (101, 'generals'),\n", - " (102, 'people'),\n", - " (102, 'peoples'),\n", - " (103, 'because'),\n", - " (104, 'used'),\n", - " (105, 'how'),\n", - " (106, 'even'),\n", - " (107, 'much'),\n", - " (108, 'during'),\n", - " (109, 'both'),\n", - " (110, 'case'),\n", - " (110, 'cases'),\n", - " (111, 'three'),\n", - " (112, 'number'),\n", - " (112, 'numbers'),\n", - " (113, 'make'),\n", - " (113, 'makes'),\n", - " (114, 'per'),\n", - " (115, 'great'),\n", - " (115, 'grate'),\n", - " (116, 'act'),\n", - " (116, 'acts'),\n", - " (117, 'way'),\n", - " (117, 'ways'),\n", - " (117, 'weigh'),\n", - " (117, 'weighs'),\n", - " (118, 'life'),\n", - " (118, 'lives'),\n", - " (119, 'good'),\n", - " (119, 'goods'),\n", - " (120, 'day'),\n", - " (120, 'days'),\n", - " (120, 'dazed'),\n", - " (120, 'daze'),\n", - " (121, 'public'),\n", - " (122, 'man'),\n", - " (122, 'mans'),\n", - " (123, 'however'),\n", - " (124, 'system'),\n", - " (124, 'systems'),\n", - " (125, 'water'),\n", - " (125, 'waters'),\n", - " (126, 'without'),\n", - " (127, 'government'),\n", - " (127, 'governments'),\n", - " (128, 'while'),\n", - " (129, 'long'),\n", - " (130, 'order'),\n", - " (130, 'orders'),\n", - " (131, 'law'),\n", - " (131, 'laws'),\n", - " (132, 'section'),\n", - " (132, 'sections'),\n", - " (133, 'court'),\n", - " (133, 'courts'),\n", - " (134, 'high'),\n", - " (135, 'right'),\n", - " (135, 'rights'),\n", - " (136, 'own'),\n", - " (137, 'found'),\n", - " (138, 'united'),\n", - " (139, 'just'),\n", - " (140, 'here'),\n", - " (141, 'against'),\n", - " (142, 'world'),\n", - " (142, 'worlds'),\n", - " (144, 'company'),\n", - " (144, 'companies'),\n", - " (145, 'within'),\n", - " (146, 'given'),\n", - " (147, 'service'),\n", - " (147, 'services'),\n", - " (148, 'house'),\n", - " (148, 'houses'),\n", - " (149, 'another'),\n", - " (150, 'power'),\n", - " (150, 'powers'),\n", - " (151, 'place'),\n", - " (151, 'places'),\n", - " (152, 'know'),\n", - " (152, 'knows'),\n", - " (153, 'little'),\n", - " (154, 'down'),\n", - " (155, 'present'),\n", - " (155, 'presents'),\n", - " (156, 'every'),\n", - " (157, 'national'),\n", - " (157, 'nationals'),\n", - " (158, 'back'),\n", - " (158, 'backs'),\n", - " (159, 'take'),\n", - " (159, 'takes'),\n", - " (160, 'information'),\n", - " (161, 'men'),\n", - " (162, 'since'),\n", - " (163, 'might'),\n", - " (164, 'small'),\n", - " (165, 'large'),\n", - " (166, 'school'),\n", - " (166, 'schools'),\n", - " (167, 'following'),\n", - " (168, 'still'),\n", - " (170, 'last'),\n", - " (170, 'lasts'),\n", - " (171, 'city'),\n", - " (171, 'cities'),\n", - " (172, 'second'),\n", - " (172, 'seconds'),\n", - " (173, 'development'),\n", - " (173, 'developments'),\n", - " (174, 'different'),\n", - " (175, 'university'),\n", - " (175, 'universities'),\n", - " (176, 'old'),\n", - " (177, 'form'),\n", - " (177, 'forms'),\n", - " (178, 'point'),\n", - " (178, 'points'),\n", - " (179, 'total'),\n", - " (179, 'totals'),\n", - " (180, 'data'),\n", - " (181, 'too'),\n", - " (182, 'committee'),\n", - " (182, 'committees'),\n", - " (183, 'report'),\n", - " (183, 'reports'),\n", - " (184, 'business'),\n", - " (184, 'businesses'),\n", - " (185, 'think'),\n", - " (185, 'thinks'),\n", - " (186, 'end'),\n", - " (186, 'ends'),\n", - " (187, 'get'),\n", - " (187, 'gets'),\n", - " (188, 'set'),\n", - " (188, 'sets'),\n", - " (189, 'research'),\n", - " (189, 'researches'),\n", - " (190, 'say'),\n", - " (190, 'says'),\n", - " (191, 'come'),\n", - " (191, 'comes'),\n", - " (192, 'country'),\n", - " (192, 'countries'),\n", - " (193, 'never'),\n", - " (194, 'fact'),\n", - " (194, 'facts'),\n", - " (195, 'go'),\n", - " (195, 'goes'),\n", - " (196, 'control'),\n", - " (196, 'controls'),\n", - 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" (593, 'originals'),\n", - " (594, 'yes'),\n", - " (595, 'effective'),\n", - " (596, 'defendant'),\n", - " (596, 'defendants'),\n", - " (597, 'living'),\n", - " (598, 'december'),\n", - " (599, 'character'),\n", - " (599, 'characters'),\n", - " (600, 'began'),\n", - " (601, 'carried'),\n", - " (602, 'supply'),\n", - " (602, 'supplies'),\n", - " (603, 'blood'),\n", - " (604, 'taking'),\n", - " (605, 'manner'),\n", - " (605, 'manners'),\n", - " (606, 'journal'),\n", - " (606, 'journals'),\n", - " (607, 'hundred'),\n", - " (607, 'hundreds'),\n", - " (608, 'red'),\n", - " (609, 'developed'),\n", - " (610, 'performance'),\n", - " (610, 'performances'),\n", - " (611, 'situation'),\n", - " (611, 'situations'),\n", - " (612, 'felt'),\n", - " (613, 'workers'),\n", - " (613, 'worker'),\n", - " (614, 'volume'),\n", - " (614, 'volumes'),\n", - " (615, 'presented'),\n", - " (616, 'knew'),\n", - " (617, 'answer'),\n", - " (617, 'answers'),\n", - " (618, 'resources'),\n", - " (618, 'resource'),\n", - " (619, 'industrial'),\n", - " (620, 'twenty'),\n", - " (620, 'twenties'),\n", - " (621, 'sent'),\n", - " (622, 'looked'),\n", - " (623, 'library'),\n", - " (623, 'libraries'),\n", - " (624, 'added'),\n", - " (625, 'passed'),\n", - " (626, 'ten'),\n", - " (626, 'tens'),\n", - " (627, 'sea'),\n", - " (627, 'seas'),\n", - " (628, 'applied'),\n", - " (629, 'included'),\n", - " (630, 'physical'),\n", - " (631, 'across'),\n", - " (632, 'army'),\n", - " (632, 'armies'),\n", - " (633, 'toward'),\n", - " (634, 'produced'),\n", - " (635, 'placed'),\n", - " (636, 'role'),\n", - " (636, 'roles'),\n", - " (637, 'october'),\n", - " (638, 'final'),\n", - " (639, 'approach'),\n", - " (639, 'approaches'),\n", - " (640, 'provisions'),\n", - " (640, 'provision'),\n", - " (641, 'leave'),\n", - " (642, 'director'),\n", - " (642, 'directors'),\n", - " (643, 'employment'),\n", - " (643, 'employments'),\n", - " (644, 'anything'),\n", - " (645, 'particularly'),\n", - " (646, 'hard'),\n", - " (647, 'outside'),\n", - " (648, 'week'),\n", - " (648, 'weeks'),\n", - " (649, 'feel'),\n", - " (649, 'feels'),\n", - " (650, 'charge'),\n", - " (650, 'charges'),\n", - " (651, 'indeed'),\n", - " (652, 'degree'),\n", - " (652, 'degrees'),\n", - " (653, 'reference'),\n", - " ...]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_wordlist" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d1a06597-4ad5-4566-a716-8bbad416b7ab", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "with open(\"final_wordlist.csv\", \"w\") as f:\n", - " f.write(\"word,number\\n\")\n", - " \n", - " for w in final_wordlist:\n", - " lemmatized = \"\" if not w[1] else w[1]\n", - " f.write(f\"{w[1].upper()},{w[0]}\")\n", - " f.write(\"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c88fe193-11cc-4a06-a3cf-d1ad85f44d14", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "final_wordlist" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a0d177b-3499-42fb-8091-29547567d69a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/wordlist/wordlist-new2.ipynb b/wordlist/wordlist-new2.ipynb deleted file mode 100644 index 52a0d2f..0000000 --- a/wordlist/wordlist-new2.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "991a711f-be98-4aae-a657-84b065449916", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "try:\n", - " _initialized\n", - "except:\n", - " # !pip install spacy\n", - " # !python -m spacy download en_core_web_trf\n", - " import spacy\n", - " \n", - " nlp = spacy.load('en_core_web_trf', disable=['parser', 'ner'])\n", - " \n", - " _initialized=True\n", - " \n", - "import pandas as pd\n", - "import gzip\n", - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d130bb84", - "metadata": {}, - "outputs": [], - "source": [ - "def get_lines(filename):\n", - " with gzip.open(filename, 'r') as f:\n", - " ret = []\n", - " for l in f:\n", - " if len(ret) > 30_000:\n", - " return ret\n", - " ret.append(str(l).lower())\n", - " return ret\n", - "\n", - "\n", - " \n", - "WORDLIST_SIZE = 8192 + 3\n", - "word_re = re.compile(r\"^[A-Za-z]+$\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "de2d1731", - "metadata": {}, - "outputs": [], - "source": [ - "!pwd\n", - "!ls" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "90665714", - "metadata": {}, - "outputs": [], - "source": [ - "annotated_words=pd.read_excel(\"annotated_words.ods\")\n", - "\n", - "excluded_words = list(annotated_words[annotated_words[\"keep\"] != \"Yes\"][\"word\"].str.lower())\n", - "excluded_words[0:10]\n", - "\n", - "custom_maps = annotated_words[annotated_words[\"maps_to\"].notna()][[\"word\",\"maps_to\"]].assign(maps_to=lambda x: x[\"maps_to\"].map(lambda y: y.split(\",\")))\n", - "\n", - "custom_maps = [\n", - " (m[1][\"word\"].lower(), mapping.lower())\n", - " for m in custom_maps.iterrows()\n", - " for mapping in m[1][\"maps_to\"]\n", - "]\n", - "custom_maps" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fb50c69e", - "metadata": {}, - "outputs": [], - "source": [ - "# Start parsing the wordlist\n", - "all_words = get_lines(\"00-frequency-all.txt.gz\")\n", - "\n", - "# Delete header line\n", - "all_words = all_words[1:]\n", - "\n", - "# Get only the word (fixed width)\n", - "all_words = [w[13:36].strip() for w in all_words]\n", - "\n", - "# Remove special characters\n", - "all_words = [w for w in all_words if word_re.search(w)]\n", - "\n", - "# Remove all removed words\n", - "all_words = [w for w in all_words if w not in excluded_words]\n", - "\n", - "# Add all custom mappings\n", - "for m in list(sum(custom_maps, ())):\n", - " if m[0] not in all_words:\n", - " all_words.append(m[0])\n", - " if m[1] not in all_words:\n", - " all_words.append(m[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "cd21bff5", - "metadata": {}, - "outputs": [], - "source": [ - "# Lemmatize all words (plural -> singular)\n", - "lemmatize_mappings = [(w, nlp(w)[0].lemma_) for w in all_words[:100]]\n", - "print(lemmatize_mappings[:100])\n", - "\n", - "# Add custom lemmatizations\n", - "for l in custom_maps:\n", - " if l in lemmatize_mappings:\n", - " print(f\"Warning: {l} is already lemmatized\")\n", - " else:\n", - " lemmatize_mappings.append(l)\n", - " \n", - "print(lemmatize_mappings[:100])\n", - "\n", - "lemmatize_mappings = [w for w in lemmatize_mappings if w[1] not in excluded_words]\n", - "print(lemmatize_mappings[:100])\n", - "\n", - "# Now, re-add all lematized words to the list of every word\n", - "for w in sum(lemmatize_mappings, ()):\n", - " if w not in all_words:\n", - " print(w)\n", - " all_words.append(w)\n", - " \n", - "lemmatize_mappings = {k: v for k, v in lemmatize_mappings}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0ee9af7d", - "metadata": {}, - "outputs": [], - "source": [ - "final_wordlist = []\n", - "seen_lemmatizations = set()\n", - "for w in all_words:\n", - " lemmatized = lemmatize_mappings.get(w) or w\n", - " if lemmatized in seen_lemmatizations:\n", - " # The lemmatized version of this word was already seen\n", - " continue\n", - " else:\n", - " # The lemmatized version hasn't been seen. We're good to add it\n", - " final_wordlist.append([\n", - " k\n", - " for k\n", - " in lemmatize_mappings.keys()\n", - " if lemmatize_mappings[k] == lemmatized\n", - " ])\n", - " seen_lemmatizations.add(lemmatized)\n", - "\n", - " if len(final_wordlist) >= WORDLIST_SIZE:\n", - " break\n", - "\n", - "# Now, convert it to the format (number, word)\n", - "final_wordlist = [\n", - " (idx, w)\n", - " for idx, words in enumerate(final_wordlist)\n", - " for w in words\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "07c1293c", - "metadata": {}, - "outputs": [], - "source": [ - "print(len(lemmatize_mappings))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19c255d0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/wordlist/wordlist-new2.py b/wordlist/wordlist-new2.py deleted file mode 100755 index ca84229..0000000 --- a/wordlist/wordlist-new2.py +++ /dev/null @@ -1,159 +0,0 @@ -#!/usr/bin/env python3 -# coding: utf-8 - -print("Step 1") - - -try: - _initialized -except: - # !pip install spacy - # !python -m spacy download en_core_web_trf - import spacy - from tqdm import tqdm - - nlp = spacy.load('en_core_web_trf', disable=['parser', 'ner']) - - _initialized=True - -import pandas as pd -import gzip -import re - - -print("Step 2") - - -def get_lines(filename): - with gzip.open(filename, 'r') as f: - ret = [] - for l in f: - if len(ret) > 30_000: - return ret - ret.append(str(l).lower()) - return ret - - - -WORDLIST_SIZE = 8192 + 3 -word_re = re.compile(r"^[A-Za-z]+$") - - -print("Step 3") - - -annotated_words=pd.read_excel("annotated_words.ods") - -excluded_words = list(annotated_words[annotated_words["keep"] != "Yes"]["word"].str.lower()) -excluded_words[0:10] - -custom_maps = annotated_words[annotated_words["maps_to"].notna()][["word","maps_to"]].assign(maps_to=lambda x: x["maps_to"].map(lambda y: y.split(","))) - -custom_maps = [ - (m[1]["word"].lower(), mapping.lower()) - for m in custom_maps.iterrows() - for mapping in m[1]["maps_to"] -] -custom_maps - - -print("Step 4") - - -# Start parsing the wordlist -all_words = get_lines("00-frequency-all.txt.gz") - -# Delete header line -all_words = all_words[1:] - -# Get only the word (fixed width) -all_words = [w[13:36].strip() for w in all_words] - -# Remove special characters -all_words = [w for w in all_words if word_re.search(w)] - -# Remove all removed words -all_words = [w for w in all_words if w not in excluded_words] - -# Add all custom mappings -for m in list(sum(custom_maps, ())): - if m[0] not in all_words: - all_words.append(m[0]) - if m[1] not in all_words: - all_words.append(m[1]) - - -print("Step 5") - - -# Lemmatize all words (plural -> singular) -lemmatize_mappings = [(w, nlp(w)[0].lemma_) for w in tqdm(all_words)] -print(lemmatize_mappings[:100]) - -# Add custom lemmatizations -for l in custom_maps: - if l in lemmatize_mappings: - print(f"Warning: {l} is already lemmatized") - else: - lemmatize_mappings.append(l) - -print(lemmatize_mappings[:100]) - -lemmatize_mappings = [w for w in lemmatize_mappings if w[1] not in excluded_words] -print(lemmatize_mappings[:100]) - -# Now, re-add all lematized words to the list of every word -for w in sum(lemmatize_mappings, ()): - if w not in all_words: - print(w) - all_words.append(w) - -lemmatize_mappings = {k: v for k, v in lemmatize_mappings} - - -print("Step 6") - - -final_wordlist = [] -seen_lemmatizations = set() -for w in all_words: - lemmatized = lemmatize_mappings.get(w) or w - if lemmatized in seen_lemmatizations: - # The lemmatized version of this word was already seen - continue - else: - # The lemmatized version hasn't been seen. We're good to add it - final_wordlist.append([ - k - for k - in lemmatize_mappings.keys() - if lemmatize_mappings[k] == lemmatized - ]) - seen_lemmatizations.add(lemmatized) - - if len(final_wordlist) >= WORDLIST_SIZE: - break - -# Now, convert it to the format (number, word) -final_wordlist = [ - (idx, w) - for idx, words in enumerate(final_wordlist) - for w in words -] - - -print("Step 7") - -print(len(lemmatize_mappings)) - -print("Step 8") - -with open("01-generated-wordlist.csv", "w") as f: - f.write("word,number\n") - - for w in final_wordlist: - lemmatized = "" if not w[1] else w[1] - f.write(f"{w[1].upper()},{w[0]}") - f.write("\n") - -print("Done")