diff options
Diffstat (limited to 'terms.py')
| -rw-r--r-- | terms.py | 114 |
1 files changed, 98 insertions, 16 deletions
@@ -2,13 +2,24 @@ from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem import PorterStemmer from nltk.stem import WordNetLemmatizer -from nltk.util import ngrams -import collections +from nltk.tokenize import word_tokenize +from nltk.util import ngrams +from nltk import pos_tag +import collections +import itertools import documents import database +import random +import nltk +import time import bs4 +import os import re +import spacy +from spacy import displacy +nlp = spacy.load("en_core_web_sm") + STOPWORDS = set(stopwords.words('english')).difference({ "how", "because", "through", "or", "as", "about", "not", "no", "who", "of", "can", "over", "you" @@ -17,31 +28,102 @@ LEM = WordNetLemmatizer() def main(): numdocs = documents.get_num_documents() - for document_id in range(1, numdocs): - parse_document(document_id, documents.get_document_name_by_id(document_id), numdocs) + docid = random.randint(1, numdocs) + parse_document(docid, documents.get_document_name_by_id(docid), numdocs) + + # for document_id in range(1, numdocs): + # parse_document(document_id, documents.get_document_name_by_id(document_id), numdocs) + + # # break + +def parse_region(raw_text, region_weight, document_id): + terms = word_tokenize(raw_text) + terms = [re.sub(r"[^a-zA-Z0-9\s]", "", term).rstrip().lower() for term in terms] + terms = [LEM.lemmatize(i) for i in terms if i != "" and i not in STOPWORDS] + + processed = nlp(raw_text) + linked_words = [] + for ent in processed.ents: + words = [ + re.sub(r"[^a-zA-Z0-9\s]", "", word).rstrip().lower() + for word in word_tokenize(ent.text) + if re.sub(r"[^a-zA-Z0-9\s]", "", word).rstrip().lower() != "" + ] + if len(words) > 1: + linked_words.append(words) + + return append_region(terms, linked_words, region_weight, document_id) + +def append_region(terms, linked_words, region_weight, document_id): + flattened_linked_words = set(itertools.chain.from_iterable(linked_words)) + with database.Database() as db: + db.append_terms(flattened_linked_words.union(set(terms))) + ids = db.get_vocabulary_ids(flattened_linked_words) + + linked_words_ids = [str([ids[j] for j in i])[1:-1].replace(" ", "") for i in linked_words] + db.append_merged_terms(linked_words_ids) - # break + weighted_terms = {i[0]:i[1] * region_weight for i in collections.Counter(terms).items()} + weighted_linked_terms = {i[0]:i[1] * region_weight for i in collections.Counter(linked_words_ids).items()} + + return weighted_terms, weighted_linked_terms def parse_document(document_id, document_path, numdocs): + starttime = time.time() with open(document_path, "r") as f: soup = bs4.BeautifulSoup(f.read(), "lxml") + weighted_terms = collections.Counter() + weighted_linked_terms = collections.Counter() + + # parse the file name, it has a weight of 100 + filenametext = os.path.splitext(os.path.split(document_path)[-1])[0] + region_weighted_terms, region_linked_terms = parse_region(filenametext, 100, document_id) + weighted_terms += region_weighted_terms + weighted_linked_terms += region_linked_terms + + # parse the main text, it has a weight of 1 text = [e.text for e in soup.find("div", {"class": "mw-parser-output"}).findChildren(recursive = True)] - text = [re.sub(r"[^a-zA-Z\s]", "", i).rstrip().lower() for i in text] - - terms = [] - for i in text: - terms += re.split(r"\s+|\n", i) + region_weighted_terms, region_linked_terms = parse_region(" ".join(text), 1, document_id) + weighted_terms += region_weighted_terms + weighted_linked_terms += region_linked_terms + + # parse html headers + elemtexts = [] + try: + elemtexts += [e.text for e in soup.h1.findChildren(recursive = True)] + except AttributeError: + pass - terms = [LEM.lemmatize(i) for i in terms if i != "" and i not in STOPWORDS] - terms_counted = collections.Counter(terms) + try: + elemtexts += [e.text for e in soup.h2.findChildren(recursive = True)] + except AttributeError: + pass + + region_weighted_terms, region_linked_terms = parse_region(re.sub(r"edit|Contents|source", "", " ".join(elemtexts)), 50, document_id) + weighted_terms += region_weighted_terms + weighted_linked_terms += region_linked_terms + + # parse html link elements texts, has a weight of 10 + a_texts = [e.text for e in soup.select("a") if e.text != "" and e.text != "edit" and e.text != "edit source"] + region_weighted_terms, region_linked_terms = parse_region(" ".join(a_texts), 10, document_id) + weighted_terms += region_weighted_terms + weighted_linked_terms += region_linked_terms with database.Database() as db: - db.append_terms(terms) - print("[%f%%] Added %d terms from docid: %d" % ((document_id/numdocs)*100, len(terms_counted), document_id)) + db.append_document_term_weights(weighted_terms, document_id) + db.append_document_linked_term_weights(weighted_linked_terms, document_id) + + print("[%.3f%%] {%.1fs} %d terms (weight %d), %d linked terms (weight %d) - %s" % ( + (document_id/numdocs)*100, + time.time() - starttime, + len(weighted_terms), + sum(weighted_terms.values()), + len(weighted_linked_terms), + sum(weighted_linked_terms.values()), + document_path + )) - db.append_terms_in_document(document_id, terms_counted) - print("Appended term frequency too") if __name__ == "__main__": main()
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