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feature_extract.py
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feature_extract.py
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#==============================================================================
# Lexicosyntatic, semantic and other Feature Extraction
#==============================================================================
import os
import nltk
import data_process as dp
import argparse
import numpy as np
import pandas as pd
import math
from scipy import spatial
from nltk.corpus import brown
from collections import Counter
from nltk.stem.wordnet import WordNetLemmatizer
lmtzr = WordNetLemmatizer()
# POS TAGS
#NN noun, singular 'desk', #NNS noun plural 'desks', #NNP proper noun, singular 'Harrison', #NNPS proper noun, plural 'Americans'
#PRP personal pronoun I, he, she, #PRP$ possessive pronoun my, his, hers,
#VB verb, base form take, #VBD verb, past tense took, #VBG verb, gerund/present participle taking
#VBN verb, past participle taken, #VBP verb, sing. present, non-3d take, #VBZ verb, 3rd person sing. present takes
#RB adverb very, silently,#RBR adverb, comparative better, #RBS adverb, superlative best
#CC coordinating conjunction, #IN preposition/subordinating conjunction
#TO to go 'to' the store, #RP particle give up, #MD modal could, will
#CD cardinal digit, #LS list marker 1), #FW foreign word, #UH interjection errrrrrrrm
#DT determiner, #PDT predeterminer 'all the kids'
#EX existential there (like: "there is" ... think of it like "there exists")
#JJ adjective 'big', #JJR adjective, comparative 'bigger', #JJS adjective, superlative 'biggest'
#POS possessive ending parent's
#WDT wh-determiner which, #WP wh-pronoun who, what
#WP$ possessive wh-pronoun whose, #WRB wh-abverb where, when
def similarity(content, POS_tag):
temp_info = nltk.pos_tag(nltk.word_tokenize(content))
temp_fd = nltk.FreqDist(tag for (word, tag) in temp_info)
tot_pos = sum([temp_fd[tag] for tag in POS_tag]) #sum(temp_fd.values())
local_pos_vec = []
for tag in POS_tag:
if tag in list(temp_fd.keys()):
local_pos_vec.append(temp_fd[tag]/tot_pos)
else:
local_pos_vec.append(0)
return local_pos_vec
def get_tag_info(input):
# ----------------- Initialize ---------------
input_text = input[0]
data = nltk.word_tokenize(input_text) # the string produced by process_string separated into a list of words
data_tag_info = []
feature_set = []
ttr = {}
features = {'prp_count': 0, 'VP_count': 1, 'NP_count': 2, 'prp_noun_ratio': 3, 'word_sentence_ratio': 4,
'count_pauses': 5, 'count_unintelligible': 6, 'count_repetitions': 7,
'ttr': 8, 'R': 9, 'ARI': 10, 'CLI': 11}
feature_set = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
feature_set[5] = input[1]
feature_set[6] = input[2]
feature_set[7] = input[4]
# --------------- Noun tag and Verb tag lists ---------------
noun_list = ['NN', 'NNS', 'NNP', 'NNPS']
verb_list = ['VB', 'VBD', 'VBG', 'VBN', 'VBP']
# ---------- Define production rules / VP, NP definition ----------
grammar = r"""
DTR: {<DT><DT>}
NP: {<DT>?<JJ>*<NN.*>}
PP: {<IN><NP>}
VPG: {<VBG><NP | PP>}
VP: {<V.*><NP | PP>}
CLAUSE: {<NP><VP>}
"""
# ---------- Distribution feature ----------
text = brown.words(categories='news')
tag_info = nltk.pos_tag(text)
tag_fd = nltk.FreqDist(tag for (word, tag) in tag_info)
del_key = []
for key in tag_fd.keys():
if not key.isalpha():
del_key.append(key)
while not (del_key == []):
tag_fd.pop(del_key.pop(), None)
POS_tag = ['NN', 'IN', 'DT', 'VBD', 'VBFG', 'VBG', 'PRP', 'JJ', 'NNP', 'RB', 'NNS', 'CC']
tot_pos = sum([tag_fd[tag] for tag in POS_tag]) #sum(tag_fd.values())
global_pos_vec = []
for tag in POS_tag:
if tag in list(tag_fd.keys()):
global_pos_vec.append(tag_fd[tag]/tot_pos)
else:
global_pos_vec.append(0)
# ---------------------tagging information -------------------
for i in range(len(data)):
text = data
# ========= LEXICOSYNTACTIC FEATURES =========
# ------- POS tagging -------
tag_info = np.array(nltk.pos_tag(text))
tag_fd = nltk.FreqDist(tag for i, (word, tag) in enumerate(tag_info))
freq_tag = tag_fd.most_common()
data_tag_info.append(freq_tag)
# ------- Lemmatize each word -------
#text_root = []
text_root = [lmtzr.lemmatize(j) for indexj, j in enumerate(text)]
for indexj, j in enumerate(text):
if tag_info[indexj,1] in noun_list:
text_root[indexj] = lmtzr.lemmatize(j)
elif tag_info[indexj,1] in verb_list:
text_root[indexj] = lmtzr.lemmatize(j,'v')
# ------- Phrase type -------
sentence = nltk.pos_tag(text)
cp = nltk.RegexpParser(grammar)
phrase_type = cp.parse(sentence)
# ------- Pronoun frequency -------
prp_count = sum([pos[1] for pos in freq_tag if pos[0]=='PRP' or pos[0]=='PRP$'])
feature_set[features['prp_count']] = prp_count
# ------- Noun frequency -------
noun_count = sum([pos[1] for pos in freq_tag if pos[0] in noun_list])
# ------- Gerund frequency -------
vg_count = sum([pos[1] for pos in freq_tag if pos[0]=='VBG'])
# ------- Pronoun-to-Noun ratio -------
if noun_count != 0:
prp_noun_ratio = prp_count/noun_count
else:
prp_noun_ratio = prp_count
feature_set[features['prp_noun_ratio']] = prp_noun_ratio
# Noun phrase, Verb phrase, Verb gerund phrase frequency
NP_count = 0
VP_count = 0
VGP_count = 0
for index_t, t in enumerate(phrase_type):
if not isinstance(phrase_type[index_t],tuple):
if phrase_type[index_t].label() == 'NP':
NP_count = NP_count + 1
elif phrase_type[index_t].label() == 'VP':
VP_count = VP_count + 1
elif phrase_type[index_t].label() == 'VGP':
VGP_count = VGP_count + 1
feature_set[features['NP_count']] = NP_count
feature_set[features['VP_count']] = VP_count
# ------- TTR type-to-token ratio -------
numtokens = len(text)
freq_token_type = Counter(text) # or len(set(text)) # text_root
v = len(freq_token_type)
ttr = float(v)/numtokens
feature_set[features['ttr']] = ttr
# ------- Honore's statistic -------
freq_token_root = Counter(text_root)
occur_once = 0
for j in freq_token_root:
if freq_token_root[j] == 1:
occur_once = occur_once + 1
v1 = occur_once
R = 100 * math.log(numtokens / (1 - (v1/v)))
feature_set[features['R']] = R
# ------- Automated readability index -------
num_char = len([c for c in input_text if c.isdigit() or c.isalpha()])
num_words = len([word for word in input_text.split(' ') if not word=='' and not word=='.'])
num_sentences = input_text.count('.') + input_text.count('?')
ARI = 4.71*(num_char/num_words) + 0.5*(num_words/num_sentences) - 21.43
feature_set[features['ARI']] = ARI
# ------- Coleman–Liau index -------
L = (num_char/num_words)*100
S = (num_sentences/num_words)*100
CLI = 0.0588*L - 0.296*S - 15.8
feature_set[features['CLI']] = CLI
# ------- Word-to-sentence_ratio -------
word_sentence_ratio = num_words/num_sentences
feature_set[features['word_sentence_ratio']] = word_sentence_ratio
return feature_set