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from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
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")
nlp.max_length = 100000000
STOPWORDS = set(stopwords.words('english')).difference({
"how", "because", "through", "or", "as", "about", "not",
"no", "who", "of", "can", "over", "you"
}).union({chr(i) for i in range(98, 123)})
STOPWORDS.remove("a")
LEM = WordNetLemmatizer()
def main():
numdocs = documents.get_num_documents()
with database.Database() as db:
startat = db.get_max_linked_terms() - 1
#docid = random.randint(1, numdocs)
#parse_document(docid, documents.get_document_name_by_id(docid), numdocs)
for document_id in range(startat, numdocs):
parse_document(document_id, documents.get_document_name_by_id(document_id), numdocs)
#break
def parse_region(raw_text, region_weight, document_id):
print("d: %d; w: %d; len = %d" % (document_id, region_weight, len(raw_text)))
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)
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 = " ".join([e.text for e in soup.find("div", {"class": "mw-parser-output"}).findChildren(recursive = True)])
# split large texts into more manageable chunks
for splittext in [text[i:i+99999] for i in range(0, len(text), 99999)]:
region_weighted_terms, region_linked_terms = parse_region(splittext, 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
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_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
))
if __name__ == "__main__":
main()
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