Datasets:
Tasks:
Text Generation
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Language Creators:
unknown
Annotations Creators:
unknown
Tags:
License:
| # -*- coding: utf-8 -*- | |
| import sys | |
| import json | |
| import spacy | |
| from nltk.stem.snowball import SnowballStemmer as Stemmer | |
| nlp = spacy.load("fr_core_news_sm") | |
| # https://spacy.io/usage/linguistic-features#native-tokenizer-additions | |
| from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER | |
| from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS | |
| from spacy.util import compile_infix_regex | |
| # Modify tokenizer infix patterns | |
| infixes = ( | |
| LIST_ELLIPSES | |
| + LIST_ICONS | |
| + [ | |
| r"(?<=[0-9])[+\-\*^](?=[0-9-])", | |
| r"(?<=[{al}{q}])\.(?=[{au}{q}])".format( | |
| al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES | |
| ), | |
| r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA), | |
| # ✅ Commented out regex that splits on hyphens between letters: | |
| # r"(?<=[{a}])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS), | |
| r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA), | |
| ] | |
| ) | |
| infix_re = compile_infix_regex(infixes) | |
| nlp.tokenizer.infix_finditer = infix_re.finditer | |
| def contains(subseq, inseq): | |
| return any(inseq[pos:pos + len(subseq)] == subseq for pos in range(0, len(inseq) - len(subseq) + 1)) | |
| def find_pmru(tok_title, tok_text, tok_kp): | |
| """Find PRMU category of a given keyphrase.""" | |
| # if kp is present | |
| if contains(tok_kp, tok_title) or contains(tok_kp, tok_text): | |
| return "P" | |
| # if kp is considered as absent | |
| else: | |
| # find present and absent words | |
| present_words = [w for w in tok_kp if w in tok_title or w in tok_text] | |
| # if "all" words are present | |
| if len(present_words) == len(tok_kp): | |
| return "R" | |
| # if "some" words are present | |
| elif len(present_words) > 0: | |
| return "M" | |
| # if "no" words are present | |
| else: | |
| return "U" | |
| if __name__ == '__main__': | |
| data = [] | |
| # read the dataset | |
| with open(sys.argv[1], 'r') as f: | |
| # loop through the documents | |
| for line in f: | |
| doc = json.loads(line.strip()) | |
| print(doc['id']) | |
| title_spacy = nlp(doc['title']) | |
| abstract_spacy = nlp(doc['abstract']) | |
| title_tokens = [token.text for token in title_spacy] | |
| abstract_tokens = [token.text for token in abstract_spacy] | |
| title_stems = [Stemmer('french').stem(w.lower()) for w in title_tokens] | |
| abstract_stems = [Stemmer('french').stem(w.lower()) for w in abstract_tokens] | |
| keyphrases_stems = [] | |
| for keyphrase in doc['keyphrases']: | |
| keyphrase_spacy = nlp(keyphrase) | |
| keyphrase_tokens = [token.text for token in keyphrase_spacy] | |
| keyphrase_stems = [Stemmer('french').stem(w.lower()) for w in keyphrase_tokens] | |
| keyphrases_stems.append(keyphrase_stems) | |
| prmu = [find_pmru(title_stems, abstract_stems, kp) for kp in keyphrases_stems] | |
| if doc['prmu'] != prmu: | |
| print("PRMU categories are not identical!") | |
| doc['prmu'] = prmu | |
| data.append(json.dumps(doc)) | |
| # write the json | |
| with open(sys.argv[2], 'w') as o: | |
| o.write("\n".join(data)) | |