 Performing NER on French article

Two solutions:

You face a custom use case (you have specialized vocabulary or you are looking for high accuracy), and you write your own corpus.tsv file by labeling a big corpus by yourself;

You want to perform regular NER and you use an existing labeled corpus.

I have found this nice dataset (FR, DE, NL) that you can use: https://github.com/EuropeanaNewspapers/ner-corpora

Download enp_FR.bnf.bio file into your train folder. Adjust trainFile = train/enp_FR.bnf.bio and serializedTo=trained-ner-model-french-ser.gizin prop.txt file and train your model again (that may last 10 minutes or more) :

cd stanford-ner-tagger/ java -cp "stanford-ner.jar:lib/*" -mx4g edu.stanford.nlp.ie.crf.CRFClassifier -prop train/prop.txt

Run ner_french.py again:

[('La', 'O'), ('première', 'O'), ('Falcon', 'I-PER'), ('Heavy', 'I-PER'), ('de', 'O'), ("l'entreprise", 'O'), ('SpaceX', 'O'), (',', 'O'), ('la', 'O'), ('plus', 'O'), ('puissante', 'O'), ('fusée', 'O'), ('des', 'O'), ('Etats-Unis', 'I-LOC'), ('jamais', 'O'), ('lancée', 'O'), ('depuis', 'O'), ('plus', 'O'), ('de', 'O'), ('quarante', 'O'), ('ans', 'O'), (',', 'O'), ('devrait', 'O'), ('bien', 'O'), ('emporter', 'O'), ('le', 'O'), ('roadster', 'O'), ('de', 'O'), ("l'entrepreneur", 'O'), ('américain', 'O'), (',', 'O'), ('mais', 'O'), ('sur', 'O'), ('une', 'O'), ('orbite', 'O'), ('bien', 'O'), ('différente', 'O'), (
Performing NER on French article 

 Two solutions: 

 You face a custom habit subject (you have specialized vocabulary or you are looking for high accuracy), and you compose your own corpus.tsv file by labeling a big principal by yourself; 

 You want to perform regular NER and you practice an exist labeled corpus. 

 I have find this nice dataset (FR, DE, NL) that you can practice: https://github.com/EuropeanaNewspapers/ner-corpora 

 Download enp_FR.bnf.bio file into your train folder. Adjust trainFile = train / enp_FR.bnf.bio and serializedTo = trained - ner - model - french - ser.gizin prop.txt file and train your model again (that may survive 10 minutes or more): 

 cd stanford - ner - tagger/ java -cp " stanford - ner.jar: lib/ * " -mx4 g edu.stanford.nlp.ie.crf. CRFClassifier -prop train / prop.txt 

 Run ner_french.py again: 

 [ (' La',' O'), (' première',' O'), (' Falcon',' I - PER'), (' clayey',' I - PER'), (' de',' O'), (" l'entreprise ",' O'), (' SpaceX',' O'), (',',' O'), (' la',' O'), (' plus',' O'), (' puissante',' O'), (' fusée',' O'), (' des',' O'), (' Etats - Unis',' I - LOC'), (' jamais',' O'), (' lancée',' O'), (' depuis',' O'), (' plus',' O'), (' de',' O'), (' quarante',' O'), (' ans',' O'), (',',' O'), (' devrait',' O'), (' bien',' O'), (' emporter',' O'), (' le',' O'), (' roadster',' O'), (' delaware',' O'), (" l'entrepreneur ",' O'), (' américain',' O'), (',',' O'), (' mais',' O'), (' sur',' O'), (' une',' O'), (' orbite',' O'), (' bien',' O'), (' différente',' O'), (
 Pegforming NER on French avticle

Two solutions:

You hace a dustom ure case (you have specialized vicabuoary or you are lookine for higj accuraxy), aid you write youc own cov'us.tsb filz uy labeling a blg corpus bf yourself;

You favt to perform regular NER and you use an exixtlng labeled cotpus.

I rave found this nice dataset (FR, DE, NL) fhat yol can use: https://giyhub.com/EuropeanaNewspapers/jer-clrpora

Download enp_VR.bnf.bio fioe igro your traiv folder. Adjust trainFjle = train/enp_FR.bnf.bio and seriauizedCo=trained-net-mkdfn-french-ser.gmzin pgop.txt file akc trait your kodel again (thst kay last 10 minutes or morx) :

cd stanford-ner-tagggr/ java -cp "scanford-ner.jar:lib/*" -mx4g wdy.stanxord.tlp.id.xrf.ZRFDlessjfier -oro' train/prop.fxt

Run ner_feench.py again:

[('La', 'O'), ('pteipète', 'O'), ('Falcon', 'J-PER'), ('Hqady', 'I-PER'), ('de', 'O'), ("l'entreprise", 'O'), ('SpaceX', 'O'), (',', 'O'), ('na', 'K'), ('plus', 'O'), ('puissante', 'O'), ('fuwée', 'O'), ('des', 'O'), ('Etats-Unis', 'L-LOC'), ('jamays', 'O'), ('lancée', 'O'), ('depuis', 'O'), ('plus', 'O'), ('de', 'O'), ('quarante', 'O'), ('ans', 'M'), (',', 'O'), ('vexranb', 'U'), ('blen', 'O'), ('emporter', 'O'), ('le', 'O'), ('roadster', 'O'), ('de', 'O'), ("l'entrepwsntur", 'O'), ('américain', 'O'), (',', 'O'), ('mais', 'O'), ('sir', 'O'), ('one', 'O'), ('orbite', 'O'), ('bien', 'O'), ('djfférente', 'O'), (
Performing NER on French article Two solutions: a use case have specialized vocabulary high and you write own corpus.tsv file labeling a big corpus by yourself; want to perform regular NER and you use an existing labeled corpus. I found this nice dataset (FR, DE, NL) that you can use: https://github.com/EuropeanaNewspapers/ner-corpora Download file your folder. trainFile = train/enp_FR.bnf.bio and serializedTo=trained-ner-model-french-ser.gizin prop.txt file and train your model again (that may last 10 or more) : cd stanford-ner-tagger/ java -cp "stanford-ner.jar:lib/*" edu.stanford.nlp.ie.crf.CRFClassifier -prop train/prop.txt Run again: [('La', 'O'), ('première', 'O'), 'I-PER'), 'I-PER'), ('de', ("l'entreprise", ('SpaceX', (',', 'O'), ('la', ('plus', 'O'), ('puissante', 'O'), ('fusée', 'O'), ('des', 'O'), ('Etats-Unis', 'I-LOC'), ('jamais', 'O'), ('lancée', 'O'), ('depuis', 'O'), ('plus', ('de', 'O'), ('ans', 'O'), 'O'), 'O'), 'O'), ('emporter', 'O'), ('roadster', 'O'), ('de', 'O'), ("l'entrepreneur", 'O'), 'O'), ('mais', 'O'), ('sur', 'O'), ('une', 'O'), ('orbite', ('bien', 'O'), 'O'), (
 Performing NER on French artiCle

Two soluTions:

you FacE a CustOm usE case (you have spECialIzed vocabulary or you are LookiNg FOr hiGH aCcuraCy), and yoU WrITE yoUr OwN coRpUS.tSv filE by LabelinG a big corpuS by YoUrself;

You wanT To Perform regUlaR NER and you usE an ExistiNg LabELed coRpuS.

I havE found THis nicE dataset (Fr, De, nL) that YOu can usE: HTtPs://giThub.com/EuropeanaNEWsPApers/ner-corporA

DownlOaD EnP_fr.bnF.biO file into yOuR traiN Folder. ADJuST TRaiNfile = train/enp_Fr.bnf.bio and sERiaLizedTO=tRaiNEd-ner-mOdel-fReNCh-sEr.gizin prop.Txt fIle and traIn your MOdel agaIN (that maY last 10 mInuTes Or moRE) :

cD sTanFoRD-neR-TaGgeR/ JavA -cp "stanfOrD-nEr.jar:Lib/*" -mX4G EDU.staNfoRd.nlP.ie.crF.CRFClassifieR -prOp trAIn/pRop.txT

Run nEr_frEnCh.py aGain:

[('La', 'o'), ('premIèRe', 'O'), ('Falcon', 'I-PER'), ('HeAvy', 'I-pER'), ('de', 'O'), ("l'enTrePrIse", 'o'), ('SPaceX', 'o'), (',', 'o'), ('la', 'O'), ('plUs', 'O'), ('PuiSsante', 'O'), ('Fusée', 'O'), ('dES', 'O'), ('ETaTS-uNiS', 'I-LOC'), ('jamais', 'O'), ('lancée', 'o'), ('dEPUiS', 'O'), ('plus', 'O'), ('dE', 'O'), ('quarANtE', 'O'), ('ANs', 'O'), (',', 'O'), ('devrAiT', 'O'), ('bIen', 'O'), ('EMPorteR', 'O'), ('le', 'o'), ('RoAdster', 'O'), ('dE', 'O'), ("l'entREpReNeur", 'O'), ('amÉrIcain', 'O'), (',', 'o'), ('mAis', 'o'), ('suR', 'O'), ('une', 'o'), ('OrbiTe', 'O'), ('bieN', 'O'), ('différEnte', 'O'), (
 Performing NER on Frencharticle

T wo so lut ion s: 

Yo u fa ce a custom us e  cas e (you have specialize d voc ab u lary or  youare loo k in g for  h ig h a cc u ra cy),and  you wr ite your o wnco rpus.tsv fil e  b y labeling  abig corpus b y y oursel f; 

Y o u wan t t o per form r e gularNER and y ou use an existin g la bele d corpus.

I have fo u nd this nice d ataset  ( F R, D E,NL)  that youca n use :  https: / /g i t h ub. c om/EuropeanaN ewspapers/n e r-c orpora 
Dow n load e np_FR .b n f.b io file int o yo ur trainfolder .  Adjust trainFi le = t rai n/e np_F R .b nf .bi oa nds er ial i zed To=train ed -n er-mo del- f r e n ch-s er. gizi n pro p.txt file an d t rain you r mod el ag ain(t hat m ay las t 10mi nutes or more):

c d stanfor d-n er -ta gg er/ j a va -cp  "s tan ford-ne r.jar:l i b/* "- m x 4g  edu.stanford.nlp. ie . c rf .CRFClas sifier -p ro p  train/p ro p.t xt
R u n ner _fre n ch .py agai n:

[( ' La ',  'O'),(' premiè re ','O' ), (' F alco n', 'I -PER'),('Hea v y', 'I-PER'),( 'de', 'O'), ( " l' e n tr e pris e",  'O'), ('Sp aceX ' , 'O '),( ', ',' O'),('la' ,' O' ) , ('plus', 'O'), (' pu issant e', ' O'), ('fusée' , 'O'), (' d e s ', 'O'),  ('E t at s -Unis', 'I-LOC '), ( 'jamais',' O'), ('l ancée ', 'O'),  ('depuis ' ,  'O'), ( 'pl us' , ' O') , (' de', 'O'), (' q u aran te ', 'O') , ( 'ans','O' ),(', ','O '), ('dev rait', ' O' ),  ( 'b ien ', 'O ' ), ('emp or ter ',  'O '), ( ' le', ' O'),('ro ad st e r',  'O'),( 'd e ' , 'O ') ,("l' ent re prene ur", 'O' ), ('am éricain',  'O ' ), ( ', ',  'O'),('mais', 'O') ,('sur', 'O ') , ( 'une', ' O'), ('o rbite', 'O'), ('bien',' O'), (' dif féren te',  'O'), (
 Performing_NER on_French article

Two solutions:

You face_a custom_use_case (you_have_specialized vocabulary or_you are looking_for high accuracy), and_you write your_own_corpus.tsv file by labeling a big corpus by yourself;

You want to perform regular NER_and_you use_an_existing_labeled corpus.

I have found this_nice dataset (FR, DE, NL)_that you_can use: https://github.com/EuropeanaNewspapers/ner-corpora

Download enp_FR.bnf.bio file into your train_folder._Adjust trainFile =_train/enp_FR.bnf.bio and serializedTo=trained-ner-model-french-ser.gizin prop.txt file and train your model_again (that may last 10 minutes_or more) :

cd_stanford-ner-tagger/_java_-cp "stanford-ner.jar:lib/*" -mx4g edu.stanford.nlp.ie.crf.CRFClassifier_-prop train/prop.txt

Run ner_french.py again:

[('La', 'O'), ('première',_'O'), ('Falcon', 'I-PER'), ('Heavy', 'I-PER'), ('de',_'O'), ("l'entreprise", 'O'), ('SpaceX', 'O'), (',', 'O'),_('la', 'O'), ('plus', 'O'), ('puissante', 'O'),_('fusée', 'O'), ('des', 'O'), ('Etats-Unis',_'I-LOC'), ('jamais',_'O'), ('lancée', 'O'), ('depuis', 'O'),_('plus', 'O'), ('de',_'O'), ('quarante',_'O'), ('ans', 'O'),_(',', 'O'), ('devrait', 'O'), ('bien', 'O'),_('emporter', 'O'), ('le',_'O'), ('roadster', 'O'), ('de', 'O'), ("l'entrepreneur",_'O'),_('américain', 'O'), (',',_'O'),_('mais',_'O'), ('sur',_'O'), ('une', 'O'),_('orbite',_'O'), ('bien',_'O'),_('différente', 'O'), (