Use new wordlist generation logic

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+UNPUBLISHED,UNPUBLISHE,SPACY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
+METAPHYSICS,METAPHYSIC,SPACY
+TROUSERS,TROUSER,SPACY
+UNAFFECTED,UNAFFECTE,SPACY
+RENOWNED,RENOWNE,SPACY
+TALENTED,TALENTE,SPACY
+GREED,GREE,SPACY
+UNFINISHED,UNFINISHE,SPACY
+AESTHETICS,AESTHETIC,SPACY
+INFRARED,INFRARE,SPACY
+DISINTERESTED,DISINTERESTE,SPACY
+UNNOTICED,UNNOTICE,SPACY
+TING,TE,SPACY
+ANNALS,ANNAL,SPACY
+OUTSKIRTS,OUTSKIRT,SPACY
+DETEST,DET,SPACY
+FETTER,FETT,SPACY
+SIDEWAYS,SIDEWAY,SPACY
+ALMS,ALM,SPACY
+MEASLES,MEASLE,SPACY
+UNRESTRICTED,UNRESTRICTE,SPACY
+ARREARS,ARREAR,SPACY
+UNDEVELOPED,UNDEVELOPE,SPACY
+CARIES,CARIE,SPACY
+MORES,MORE,SPACY
+UNALTERED,UNALTERE,SPACY
+UNPROTECTED,UNPROTECTE,SPACY
+UNEDUCATED,UNEDUCATE,SPACY
+GALLOWS,GALLOW,SPACY
+UNDATED,UNDATE,SPACY
+UNNAMED,UNNAME,SPACY
+MONIES,MONIE,SPACY
+UNAIDED,UNAIDE,SPACY
+UNQUESTIONED,UNQUESTIONE,SPACY
+IMBED,IMBE,SPACY
+AMINES,AMINE,SPACY
+GRASSROOTS,GRASSROOT,SPACY
+ACCURSED,ACCURSE,SPACY
+UNDECIDED,UNDECIDE,SPACY
+UNCHECKED,UNCHECKE,SPACY
+UNCOMPLICATED,UNCOMPLICATE,SPACY
+BELATED,BELATE,SPACY
+UNDETERMINED,UNDETERMINE,SPACY
+EAVES,EAVE,SPACY
+DISAFFECTED,DISAFFECT,SPACY
+UNFULFILLED,UNFULFILLE,SPACY
+STRIATED,STRIATE,SPACY
+RICKETS,RICKET,SPACY
+BICEPS,BICEP,SPACY
+DEATHBED,DEATHBE,SPACY
+RABIES,RABIE,SPACY
+UNATTENDED,UNATTENDE,SPACY
+UNABATED,UNABATE,SPACY
+MANNERED,MANNERE,SPACY
+FAECES,FAECE,SPACY
+QUADRUPED,QUADRUPE,SPACY
+UNSTRUCTURED,UNSTRUCTURE,SPACY
+UNAVAILING,UNAVAILE,SPACY
+SUBSPECIES,SUBSPECIE,SPACY
+UNDETECTED,UNDETECTE,SPACY
+UNPLANNED,UNPLANNE,SPACY
+UNCONDITIONED,UNCONDITIONE,SPACY
+INBRED,INBREED,SPACY
+TONGS,TONG,SPACY
+BIGOTED,BIGOTE,SPACY
+ENTRAILS,ENTRAIL,SPACY
+UNEQUALLED,UNEQUALLE,SPACY
+ONCOMING,ONCOME,SPACY
+UNTHINKING,UNTHINKE,SPACY
+MENSES,MENSE,SPACY
+UNMITIGATED,UNMITIGATE,SPACY
+UNRECORDED,UNRECORDE,SPACY
+SEMIOTICS,SEMIOTIC,SPACY
+UNACKNOWLEDGED,UNACKNOWLEDGE,SPACY
+UNDISGUISED,UNDISGUISE,SPACY
+PYRITES,PYRITE,SPACY
+UNOBSTRUCTED,UNOBSTRUCTE,SPACY
+UNATTACHED,UNATTACHE,SPACY
+UNMATCHED,UNMATCHE,SPACY
+PLIERS,PLIER,SPACY
+ENAMORED,ENAMOR,SPACY
+PANTIES,PANTIE,SPACY
+BACKWOODS,BACKWOOD,SPACY
+UNPROVOKED,UNPROVOKE,SPACY
+TRICEPS,TRICEP,SPACY
+UNCHARTED,UNCHARTE,SPACY
+MALNOURISHED,MALNOURISH,SPACY
+MONEYED,MONEYE,SPACY
+UNTAMED,UNTAME,SPACY
+METHYLATED,METHYLATE,SPACY
+UNRELIEVED,UNRELIEVE,SPACY
+UNLETTERED,UNLETTERE,SPACY
+HOTBED,HOTBE,SPACY
+UNIMPROVED,UNIMPROVE,SPACY
+LOPSIDED,LOPSIDE,SPACY
+LEGGING,LEGGE,SPACY
+UNFEIGNED,UNFEIGNE,SPACY
+UNTESTED,UNTESTE,SPACY
+UNBLEMISHED,UNBLEMISHE,SPACY
+TECHNICS,TECHNIC,SPACY
+UNTAINTED,UNTAINTE,SPACY
+UNREGISTERED,UNREGISTERE,SPACY
+UNFORMED,UNFORME,SPACY
+OVERWEENING,OVERWEENE,SPACY
+UNPROVIDED,UNPROVIDE,SPACY
+FOOTLIGHTS,FOOTLIGHT,SPACY
+UNCONVERTED,UNCONVERTE,SPACY
+OBSEQUIES,OBSEQUIE,SPACY
+PINCERS,PINCER,SPACY
+MALADJUSTED,MALADJUSTE,SPACY
+ISOSCELES,ISOSCELE,SPACY
+UNPROVED,UNPROVE,SPACY
+AMIDSHIPS,AMIDSHIP,SPACY
+SEMISKILLED,SEMISKILLE,SPACY
+UNDIRECTED,UNDIRECTE,SPACY
+TABES,TABE,SPACY
+UNCLAIMED,UNCLAIME,SPACY
+UNPOLISHED,UNPOLISHE,SPACY
+FARSIGHTED,FARSIGHTE,SPACY
+FAUCES,FAUCE,SPACY
+NONCOMMISSIONED,NONCOMMISSIONE,SPACY
+UNCHARGED,UNCHARGE,SPACY
+CONGERIES,CONGERIE,SPACY
+SCABIES,SCABIE,SPACY
+MALFORMED,MALFORME,SPACY
+INFORMATICS,INFORMATIC,SPACY
+INCREMENTED,INCREMENTE,SPACY
+UNDISTRIBUTED,UNDISTRIBUTE,SPACY
+HYDRODYNAMICS,HYDRODYNAMIC,SPACY
+ANTIPODES,ANTIPODE,SPACY
+UNDEREMPLOYED,UNDEREMPLOYE,SPACY
+BIPED,BIPE,SPACY
+ELECTRODYNAMICS,ELECTRODYNAMIC,SPACY
+FEEBLEMINDED,FEEBLEMINDE,SPACY
+SUDS,SUD,SPACY
+UNDERSIZED,UNDERSIZE,SPACY
+HUSTINGS,HUSTING,SPACY
+STOREYED,STOREYE,SPACY
+UNREFINED,UNREFINE,SPACY
+UNTURNED,UNTURNE,SPACY
+SERRIED,SERRIE,SPACY
+DOLDRUMS,DOLDRUM,SPACY
+STATS,STAT,SPACY
+GULES,GULE,SPACY
+UNDERPANTS,UNDERPANT,SPACY
+UNREWARDING,UNREWARDE,SPACY
+CALIPERS,CALIPER,SPACY
+ONESIDED,ONESIDE,SPACY
+UNABRIDGED,UNABRIDGE,SPACY
+UNFURNISHED,UNFURNISHE,SPACY
+UNREDEEMED,UNREDEEME,SPACY
+UNSEEING,UNSEEE,SPACY
+KNICKERS,KNICKER,SPACY
+UNLISTED,UNLISTE,SPACY
+INNARDS,INNARD,SPACY
+GLANDERS,GLANDER,SPACY
+OVEREXTENDED,OVEREXTEND,SPACY
+RELATIVIZED,RELATIVIZE,SPACY
+UNFUNDED,UNFUNDE,SPACY
+COLUMNED,COLUMNE,SPACY
+CALISTHENICS,CALISTHENIC,SPACY
+SUPERFICIES,SUPERFICIE,SPACY
+CASTELLATED,CASTELLATE,SPACY
+PUREBRED,PUREBRE,SPACY
+CORTES,CORTE,SPACY
+REDHEADED,REDHEADE,SPACY
+CASTANETS,CASTANET,SPACY
+UNPRACTISED,UNPRACTISE,SPACY
+UNEDITED,UNEDITE,SPACY
+LIVERIED,LIVERIE,SPACY
+NOSEBLEED,NOSEBLEE,SPACY
+UNDERFUNDED,UNDERFUNDE,SPACY
+UNGRADED,UNGRADE,SPACY
+UNREDUCED,UNREDUCE,SPACY
+SIDEBURNS,SIDEBURN,SPACY
+SICKBED,SICKBE,SPACY
+FASCES,FASCE,SPACY
+AVIONICS,AVIONIC,SPACY
+CRENELATED,CRENELATE,SPACY
+NATES,NATE,SPACY
+UNREGARDED,UNREGARDE,SPACY
+UNRECONSTRUCTED,UNRECONSTRUCTE,SPACY
+BRITCHES,BRITCHE,SPACY
+DEDANS,DEDAN,SPACY
+PARATROOPS,PARATROOP,SPACY
+FINEGRAINED,FINEGRAINE,SPACY
+GREAVES,GREAVE,SPACY
+SUBSCRIPTED,SUBSCRIPTE,SPACY
+LUES,LUE,SPACY
+BIOMETRICS,BIOMETRIC,SPACY
+ARIES,ARIE,SPACY
+GASWORKS,GASWORK,SPACY
+BULLETED,BULLETE,SPACY
+HEARTSTRINGS,HEARTSTRING,SPACY
+INCREMENTING,INCREMENTE,SPACY
+UNCLEARED,UNCLEARE,SPACY
+CONSOLS,CONSOL,SPACY
+MUDFLATS,MUDFLAT,SPACY
+BADLANDS,BADLAND,SPACY
+TALIPES,TALIPE,SPACY
+LANCINATING,LANCINATE,SPACY
+UNACCOMPLISHED,UNACCOMPLISHE,SPACY
+TESSELLATED,TESSELLATE,SPACY
+SEMIFINISHED,SEMIFINISHE,SPACY
+UNAVENGED,UNAVENGE,SPACY
+SOAPSUDS,SOAPSUD,SPACY
+YEATS,YEAT,SPACY
+TELEMATICS,TELEMATIC,SPACY
+UNNOTED,UNNOTE,SPACY
+MEALIES,MEALIE,SPACY
+PARIES,PARIE,SPACY
+AUROCHS,AUROCH,SPACY
+AGOING,AGOE,SPACY
+ODDMENTS,ODDMENT,SPACY
+CLEARHEADED,CLEARHEADE,SPACY
+REDOUBTED,REDOUBTE,SPACY
+IVIED,IVIE,SPACY
+PINNIPED,PINNIPE,SPACY
+DEFATTED,DEFATTE,SPACY
+DECAFFEINATED,DECAFFEINATE,SPACY
+NINEPINS,NINEPIN,SPACY
+CAMPHORATED,CAMPHORATE,SPACY
+GLASSWORKS,GLASSWORK,SPACY
+SORITES,SORITE,SPACY
+AFFOREST,AFFOR,SPACY
+DISSAVING,DISSAVE,SPACY
+UNADVISED,UNADVISE,SPACY
+UNRECLAIMED,UNRECLAIME,SPACY
+LARES,LARE,SPACY
+LEVELHEADED,LEVELHEADE,SPACY
+SWEATPANTS,SWEATPANT,SPACY
+LOTOS,LOTO,SPACY
+GIBLETS,GIBLET,SPACY
+UNBOWED,UNBOWE,SPACY
+UNPROMPTED,UNPROMPTE,SPACY
+ABSCESSED,ABSCESSE,SPACY
+NODULATED,NODULATE,SPACY
+RUBENS,RUBEN,SPACY
+UNPAGED,UNPAGE,SPACY
+CALENDS,CALEND,SPACY
+TRUNKED,TRUNKE,SPACY
+TROUSERED,TROUSERE,SPACY
+PENATES,PENATE,SPACY
+COMBINATORICS,COMBINATORIC,SPACY
+TRESSED,TRESSE,SPACY
+PARTICOLOURED,PARTICOLOURE,SPACY
+UNENCRYPTED,UNENCRYPTE,SPACY
+ASTRONAUTICS,ASTRONAUTIC,SPACY
+HYDROPONICS,HYDROPONIC,SPACY
+UNFORMATTED,UNFORMATTE,SPACY
+SEMIDETACHED,SEMIDETACHE,SPACY
+BONKERS,BONKER,SPACY
+UNDIES,UNDIE,SPACY
+EPS,EP,SPACY
+GIMBALS,GIMBAL,SPACY
+BALCONIED,BALCONIE,SPACY
+SALTWORKS,SALTWORK,SPACY
+UNPLEDGED,UNPLEDGE,SPACY
+PREDESIGNED,PREDESIGNE,SPACY
+NFS,NF,SPACY
+UNDERBRED,UNDERBRE,SPACY
+PRECOMPILED,PRECOMPILE,SPACY
+KALENDS,KALEND,SPACY
+LITOTES,LITOTE,SPACY
+INDIGESTED,INDIGESTE,SPACY
+CITS,CIT,SPACY
+UNPRICED,UNPRICE,SPACY
+PINCHERS,PINCHER,SPACY
+CANCELLATED,CANCELLATE,SPACY
+CHITTERLINGS,CHITTERLING,SPACY
+DIBS,DIB,SPACY
+RIGHTWARDS,RIGHTWARD,SPACY
+CONVENANCES,CONVENANCE,SPACY
+INTERALLIED,INTERALLIE,SPACY
+FLINDERS,FLINDER,SPACY
+CRANNIED,CRANNIE,SPACY
+HOMEBRED,HOMEBRE,SPACY
+HIGHBRED,HIGHBRE,SPACY
+UNRULED,UNRULE,SPACY
+FOREHANDED,FOREHANDE,SPACY
+PREPACKED,PREPACKE,SPACY
+UNWISHED,UNWISHE,SPACY
+ENTREMETS,ENTREMET,SPACY
+ESTOVERS,ESTOVER,SPACY
+ANGELES,ANGELE,SPACY
+DAISIED,DAISIE,SPACY
+UPRATED,UPRATE,SPACY
+THIGHED,THIGHE,SPACY
+TURPS,TURP,SPACY
+WEAZENED,WEAZENE,SPACY
+EFFING,EFF,SPACY
+HOLS,HOL,SPACY
+JIGGERED,JIGGERE,SPACY
+SOCRATES,SOCRATE,SPACY
+AUDITORIES,AUDITORIE,SPACY
+AMBAGES,AMBAGE,SPACY
+DOITED,DOITE,SPACY
+BIONICS,BIONIC,SPACY
+UNREFERENCED,UNREFERENCE,SPACY
+EXEQUIES,EXEQUIE,SPACY
+CERASTES,CERASTE,SPACY
+SEMIMANUFACTURES,SEMIMANUFACTURE,SPACY
+GALLUSES,GALLUS,SPACY
+RERECORDED,RERECORDE,SPACY
+TELESALES,TELESALE,SPACY
+MICROGRAPHICS,MICROGRAPHIC,SPACY
+SIEMENS,SIEMEN,SPACY
+ZOUNDS,ZOUND,SPACY
+SEMIFIXED,SEMIFIXE,SPACY
+UNDIVERTED,UNDIVERTE,SPACY
+SANIES,SANIE,SPACY
+BREECHING,BREECHE,SPACY
+MENTHOLATED,MENTHOLATE,SPACY
+PANTALETS,PANTALET,SPACY
+CRUDITES,CRUDITE,SPACY
+TRAPES,TRAPE,SPACY
+PIXILATED,PIXILATE,SPACY
+BOOTES,BOOTE,SPACY
+UNPOSTED,UNPOSTE,SPACY
+HANTS,HANT,SPACY
+UNDETAILED,UNDETAILE,SPACY
+HAVINGS,HAVING,SPACY
+OUTGIVING,OUTGIVE,SPACY
+UNCOMPLEMENTED,UNCOMPLEMENTE,SPACY
+PRATIES,PRATIE,SPACY
+ELEVENSES,ELEVENSE,SPACY
+UNENLIVENED,UNENLIVENE,SPACY
+NANTES,NANTE,SPACY
+AFFINED,AFFINE,SPACY
+NONNESTED,NONNESTE,SPACY
+FALLOWING,FALLOWE,SPACY
+HYDROMECHANICS,HYDROMECHANIC,SPACY
+CLIVERS,CLIVER,SPACY
+UNICES,UNICE,SPACY
+GRAMMATICS,GRAMMATIC,SPACY
+PRAPS,PRAP,SPACY
+INTERWORKING,INTERWORKE,SPACY
+HERCULES,HERCULE,SPACY
+BIGHEADED,BIGHEADE,SPACY
+KIES,KY,SPACY
+NETHERLANDS,NETHERLAND,SPACY
+UNBOOKED,UNBOOKE,SPACY
+QUINS,QUIN,SPACY
+CANNES,CANNE,SPACY
+UNNURTURED,UNNURTURE,SPACY
+WEDGIES,WEDGIE,SPACY
+HANDWORKED,HANDWORKE,SPACY
+ANALECTS,ANALECT,SPACY
+HERTS,HERT,SPACY
+ORLEANS,ORLEAN,SPACY
+PESCADORES,PESCADORE,SPACY
+ULCERED,ULCERE,SPACY
+MISCREATED,MISCREATE,SPACY
+UNPRIZED,UNPRIZE,SPACY
+SLYBOOTS,SLYBOOT,SPACY
+RUNTED,RUNTE,SPACY
+REATTRIBUTED,REATTRIBUTE,SPACY
+HOUSETRAINED,HOUSETRAINE,SPACY
+SOBERSIDES,SOBERSIDE,SPACY
+COLESEED,COLESEE,SPACY
+BLUCHERS,BLUCHER,SPACY
+MUGGINS,MUGGIN,SPACY
+UNCRIPPLED,UNCRIPPLE,SPACY
+HEPPED,HEPPE,SPACY
+WITHINDOORS,WITHINDOOR,SPACY
+BEESTINGS,BEESTING,SPACY
+FLANDERS,FLANDER,SPACY
+DIOGENES,DIOGENE,SPACY
+COSMONAUTICS,COSMONAUTIC,SPACY
+WHOLEGRAINS,WHOLEGRAIN,SPACY
+NEEDMENTS,NEEDMENT,SPACY
+ACHATES,ACHATE,SPACY
+PRECOMPILING,PRECOMPILE,SPACY
+BALUSTERED,BALUSTERE,SPACY
+JUGGINS,JUGGIN,SPACY
+UNCONFIGURED,UNCONFIGURE,SPACY
+SLUGABED,SLUGABE,SPACY
+CHARGRILLED,CHARGRILLE,SPACY
+GANGES,GANGE,SPACY
+FLATWAYS,FLATWAY,SPACY
+CHAMPERS,CHAMPER,SPACY
+GOLDILOCKS,GOLDILOCK,SPACY
+REIMS,REIM,SPACY
+REIMPORTING,REIMPORTE,SPACY
+EMOTIONED,EMOTIONE,SPACY
+AIRBED,AIRBE,SPACY
+GIGAFLOPS,GIGAFLOP,SPACY
+YONKS,YONK,SPACY
+CASALS,CASAL,SPACY
+ROCKIES,ROCKIE,SPACY
+ORESTES,ORESTE,SPACY
+REMAPPING,REMAPPE,SPACY
+EBONICS,EBONIC,SPACY
+BRUGES,BRUGE,SPACY
+JANKERS,JANKER,SPACY
+NOTTS,NOTT,SPACY
+PROCRUSTES,PROCRUSTE,SPACY
+MULTISCALED,MULTISCALE,SPACY
+AGROTECHNICS,AGROTECHNIC,SPACY
+WAYGOING,WAYGOE,SPACY
+GENDERING,GENDERE,SPACY
+TELEMECHANICS,TELEMECHANIC,SPACY
+DEGATING,DEGATE,SPACY
+THAMES,THAME,SPACY
+LOWLIVED,LOWLIVE,SPACY
+REEDING,REEDE,SPACY
+INTERCROSSING,INTERCROSSE,SPACY
+UNDEDUCTED,UNDEDUCTE,SPACY
+AGOGICS,AGOGIC,SPACY
+UNATTENDING,UNATTENDE,SPACY
+OVERMASTED,OVERMASTE,SPACY
+GILES,GILE,SPACY
+NONCOPYRIGHTED,NONCOPYRIGHTE,SPACY
+LUDDITES,LUDDITE,SPACY
+SCURVIED,SCURVIE,SPACY
+REBREAKING,REBREAKE,SPACY
+KEATS,KEAT,SPACY
+CERVANTES,CERVANTE,SPACY
+UNCONDONED,UNCONDONE,SPACY
+DESCARTES,DESCARTE,SPACY
+BEJABERS,BEJABER,SPACY
+VIDEOGRAPHICS,VIDEOGRAPHIC,SPACY
+EURIPIDES,EURIPIDE,SPACY
+UNPERJURED,UNPERJURE,SPACY
+LAERTES,LAERTE,SPACY
+OVERCOLLECTED,OVERCOLLECTE,SPACY
+AMPHIBRACHYS,AMPHIBRACHY,SPACY
+CHEOPS,CHEOP,SPACY
+CHALONS,CHALON,SPACY
+VERSICOLOURED,VERSICOLOURE,SPACY
+SUBPARTITIONED,SUBPARTITIONE,SPACY
+BALBUTIES,BALBUTIE,SPACY
+ARCHIMEDES,ARCHIMEDE,SPACY
+GATELEGGED,GATELEGGE,SPACY
+POITIERS,POITIER,SPACY
+HAVERING,HAVERE,SPACY
+THEBES,THEBE,SPACY
+SEVRES,SEVRE,SPACY
+PERICLES,PERICLE,SPACY
+LIMOGES,LIMOGE,SPACY
+EVENTING,EVENTE,SPACY
+FATBITS,FATBIT,SPACY
+HUTTING,HUTTE,SPACY
+DOGSHORES,DOGSHORE,SPACY
+OVERBADING,OVERBADE,SPACY
+AZORES,AZORE,SPACY
+BLEWITS,BLEWIT,SPACY
+HIPOCRATES,HIPOCRATE,SPACY
+AMIENS,AMIEN,SPACY
+GUTTING,GUTTE,SPACY
+GLADYS,GLADY,SPACY
+CHADDED,CHADDE,SPACY
+EUPHRATES,EUPHRATE,SPACY
+TROWING,TROWE,SPACY
+LACEUPS,LACEUP,SPACY
+ALIPED,ALIPE,SPACY
+TALIPED,TALIPE,SPACY
+RAMSES,RAMSE,SPACY
+CENTRONICS,CENTRONIC,SPACY
+BANTING,BANTE,SPACY
+TELEPHOTOLENS,TELEPHOTOLEN,SPACY
+ARAKS,ARAK,SPACY
+DONETS,DONET,SPACY
+CEROPLASTICS,CEROPLASTIC,SPACY
+BAYNETWORKS,BAYNETWORK,SPACY
+NORWARDS,NORWARD,SPACY
+HAPPING,HAPPE,SPACY
+BARENTS,BARENT,SPACY
+ABLINGS,ABLING,SPACY
+CELLING,CELLE,SPACY
+CELEBES,CELEBE,SPACY
+NENETS,NENET,SPACY
+IMPING,IMPE,SPACY
+LINARES,LINARE,SPACY
+VAILING,VAILE,SPACY
+HABDABS,HABDAB,SPACY
+RELISTING,RELISTE,SPACY
+HOUGHING,HOUGHE,SPACY

wordlist/01-lemmatized-words.csv

@@ -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

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(",")))
-
-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)]
-
-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:
+            for (lemmatized_word, lemmatizer) in lemmatized_words:
+                if word == lemmatized_word:
                     continue
-        if lemmatized_word not in all_words:
-            iter.write(f"{lemmatized_word} not in all_words")
 
-        f.write(f"{w},{lemmatized_word}\n")
+                if (word, lemmatized_word) in seen_lemmatizations:
+                    continue
+
+                seen_lemmatizations.add((word, lemmatized_word))
+
+                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")
+
+                outfile.write(f"{word},{lemmatized_word},{lemmatizer}\n".encode("ascii"))

wordlist/02-custom-lemmatizations.csv

@@ -0,0 +1,103 @@
+ADD,ADDS
+ADS,ADDS
+AFFECTED,EFFECT
+AFFECT,EFFECT
+AFFECTIONS,AFFECTION
+AFFECTIVE,EFFECT
+AFFECTS,EFFECT
+ALUMINIUM,ALUMINUM
+ALUMINUM,ALUMINIUM
+ANALYSE,ANALYZE
+ANALYSED,ANALYZE
+ANALYSES,ANALYZE
+AUX,OX
+BE,BEE
+BERRY,BARRY
+BLEW,BLUE
+BOT,BOUGHT
+BOULDER,BOLDER
+BRINGS,BRING
+BY,BYE
+CAPITOL,CAPITAL
+CENTS,SENSE
+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

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])

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

wordlist/wordlist-new2.ipynb

@@ -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
-}

wordlist/wordlist-new2.py

@@ -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")