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Task4Part1.py
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Task4Part1.py
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from __future__ import print_function
import pysolr
from nltk.parse import stanford
import sys
import nltk
import glob
import os
nltk.internals.config_java("C:/Program Files (x86)/Java/jdk1.7.0_55/bin/java.exe")
from nltk.corpus import wordnet
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.tokenize import WordPunctTokenizer
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.stem import PorterStemmer
from nltk.wsd import lesk
from nltk.corpus import stopwords
os.environ['CLASSPATH'] = 'C:/stanford-corenlp-full-2017-06-09'
os.environ['STANFORD_PARSER'] = 'C:/stanford-parser-full-2014-08-27'
os.environ['STANFORD_MODELS'] = 'C:/stanford-parser-full-2014-08-27'
path_to_model = "C:/stanford-english-corenlp-2017-06-09-models.jar"
path_to_jar = "C:/stanford-parser-full-2014-08-27/stanford-parser.jar"
# Splits the corpus into articles. Further splits each article into sentences and tokenizes each sentence.
# The words (excuding stopwords) in each sentence is stored as a vector of its words,pos tags, lemmas, stems, hypernyms, hyponyms,
# meronyms, holonyms, dependency parse relations and is indexed using solr
#
# Authors: Nanditha Valsaraj, Shreya Vishwanath Rao, Ramya Elangovan
# Version 1.0: 12/5/2017
if(sys.argv):
datafile=open(sys.argv[1], 'r');
def getWordsSet(listOfWords):
list=[]
stopWords=set(stopwords.words('english'))
for word in listOfWords:
l=[]
if word not in stopWords:
list.append(word)
return list
text =datafile.read()
# Setup a Solr instance. The timeout is optional.
solr = pysolr.Solr('http://localhost:8983/solr/NewTask4', timeout=10000)
#split to articles
a=text.split('\n\n')
for i in range(0,len(a)):
lemma = []
c = []
stem = []
b = []
hyper = []
hypernyms = []
hypo = []
hyponyms = []
mero = []
meronyms = []
holo = []
holonyms = []
NP = []
phrases = []
depList = []
id = []
Synonyms=[]
#split into sentences
sent_all = sent_tokenize(a[i])
words = WordPunctTokenizer()
lmtzr = WordNetLemmatizer()
porter_stemmer = PorterStemmer()
for j in range(0,len(sent_all)):
name = "A" + str(i) + " S" + str(j);
id.append(name);
wrds=words.tokenize(sent_all[j])
for k in (wrds):
c.append(lmtzr.lemmatize(k))
b.append(porter_stemmer.stem(k))
bestSynonym = lesk(wrds, k)
if bestSynonym is not None:
Synonyms.append(bestSynonym);
for hypernym in bestSynonym.hypernyms():
hyper.append(hypernym)
for hyponym in bestSynonym.hyponyms():
hypo.append(hyponym)
for meronym in bestSynonym.part_meronyms():
mero.append(meronym)
for holonym in bestSynonym.part_holonyms():
holo.append(holonym)
lemma.append(c);
stem.append(b);
hypernyms.append(hyper);
hyponyms.append(hypo);
meronyms.append(mero);
holonyms.append(holo);
c=[]
b = []
hyper = []
hypo = []
mero = []
holo = []
for j in range(0,len(sent_all)):
parser = stanford.StanfordParser(path_to_model, path_to_jar)
x = list(parser.raw_parse(sent_all[j]))
for i in x:
for j in i.subtrees():
if j.label() == 'NP' or j.label()=='VP' or j.label()=='PP':
NP.append(j.leaves())
phrases.append(NP)
NP=[]
for j in range(0, len(sent_all)):
depparser = stanford.StanfordDependencyParser(path_to_model, path_to_jar)
result = depparser.raw_parse(sent_all[j])
newResult = result.__next__()
dep=list(newResult.triples())
depList.append(dep)
#adding to solr
for j in range(0,len(sent_all)):
solr.add([{"ID":id[j],"CONTENT":words.tokenize(sent_all[j]),"POS_TAG":nltk.pos_tag(words.tokenize(sent_all[j])),
"LEMMA":lemma[j],"STEM":stem[j],"HYPERNYM":hypernyms[j],"HYPONYM":hyponyms[j],"MERONYMS":meronyms[j],
"HOLONYMS":holonyms[j],"PHRASES":phrases[j],"DEPENDENCYPARSE":depList[j],"SYNSETS":Synonyms[j]}])