ner

来源： https://stackoverflow.com/questions/63297351/building-a-custom-named-entity-recognition-with-spacy-using-random-text-as-a-s

来源： https://stackoverflow.com/questions/59105346/longest-match-only-with-spacy-phrasematcher

来源： https://stackoverflow.com/questions/59105346/longest-match-only-with-spacy-phrasematcher

问题 I'm trying to evaluate my custom trained Spacy NER model. How to find the overall accuracy with confusion matrix for the model. I tried evaluating the model with spacy scorer which gives precision, recall and token accuracy with the below reference, Evaluation in a Spacy NER model I expect the output in confusion matrix instead of individual precision, recall and token accuracy. 回答1: Here is a good read for creating Confusion Matrices for Spacy NER models. It is based on the BILOU format used

问题 I used this official example code to train a NER model from scratch using my own training samples. When I predict using this model on new text, I want to get the probability of prediction of each entity. # test the saved model print("Loading from", output_dir) nlp2 = spacy.load(output_dir) for text, _ in TRAIN_DATA: doc = nlp2(text) print("Entities", [(ent.text, ent.label_) for ent in doc.ents]) print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc]) I am unable to find a method in

问题 I've been looking to use Hugging Face's Pipelines for NER (named entity recognition). However, it is returning the entity labels in inside-outside-beginning (IOB) format but without the IOB labels. So I'm not able to map the output of the pipeline back to my original text. Moreover, the outputs are masked in BERT tokenization format (the default model is BERT-large). For example: from transformers import pipeline nlp_bert_lg = pipeline('ner') print(nlp_bert_lg('Hugging Face is a French

问题 I've been looking to use Hugging Face's Pipelines for NER (named entity recognition). However, it is returning the entity labels in inside-outside-beginning (IOB) format but without the IOB labels. So I'm not able to map the output of the pipeline back to my original text. Moreover, the outputs are masked in BERT tokenization format (the default model is BERT-large). For example: from transformers import pipeline nlp_bert_lg = pipeline('ner') print(nlp_bert_lg('Hugging Face is a French

问题 My goal is to annotate author names from scientific articles with the entity PERSON. I am particularly interested with the names that match this format (authorname et al. date). For example I would like for this sentence (Minot et al. 2000 ) => to annotate Minot as a PERSON. I am using an adapted version of the code found in the official page of stanford nlp team: import stanfordnlp from stanfordnlp.server import CoreNLPClient # example text print('---') print('input text') print('') text =

问题 I am trying to solve this problem in this link, but using regexner from the stanford nlp library was not possible. (NB: I am using stanfordnlp library version 0.2.0, Stanford CoreNLP version 3.9.2, and Python 3.7.3) So I wanted to try a solution using TokenRegex. As a first attempt I tried to use the token regex file tokenrgxrules.rules from this solution: ner = { type: "CLASS", value: "edu.stanford.nlp.ling.CoreAnnotations$NamedEntityTagAnnotation" } $ORGANIZATION_TITLES = "/inc\.|corp\./"

问题 I have tried so many things to do name entity recognition on a column in my csv file, i tried ne_chunk but i am unable to get the result of my ne_chunk in columns like so ID STORY PERSON NE NP NN VB GE 1 Washington, a police officer James... 1 0 0 0 0 1 Instead after using this code, news=pd.read_csv("news.csv") news['tokenize'] = news.apply(lambda row: nltk.word_tokenize(row['STORY']), axis=1) news['pos_tags'] = news.apply(lambda row: nltk.pos_tag(row['tokenize']), axis=1) news['entityrecog'