Datasets:
Tasks:
Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
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("en_core_web_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('porter').stem(w.lower()) for w in title_tokens] | |
abstract_stems = [Stemmer('porter').stem(w.lower()) for w in abstract_tokens] | |
keyphrases_stems = [] | |
for keyphrase in doc['keyphrases']: | |
keyphrases_stems.append(keyphrase.split()) | |
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)) | |