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from typing import Union, Tuple, List, Iterable, Dict
import collections
import string
import os
import json
import logging
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
import nltk
logger = logging.getLogger(__name__)
class PhraseTokenizer(WordTokenizer):
"""Tokenizes the text with respect to existent phrases in the vocab.
This tokenizers respects phrases that are in the vocab. Phrases are separated with 'ngram_separator', for example,
in Google News word2vec file, ngrams are separated with a _ like New_York. These phrases are detected in text and merged as one special token. (New York is the ... => [New_York, is, the])
"""
def __init__(self, vocab: Iterable[str] = [], stop_words: Iterable[str] = ENGLISH_STOP_WORDS, do_lower_case: bool = False, ngram_separator: str = "_", max_ngram_length: int = 5):
self.stop_words = set(stop_words)
self.do_lower_case = do_lower_case
self.ngram_separator = ngram_separator
self.max_ngram_length = max_ngram_length
self.set_vocab(vocab)
def get_vocab(self):
return self.vocab
def set_vocab(self, vocab: Iterable[str]):
self.vocab = vocab
self.word2idx = collections.OrderedDict([(word, idx) for idx, word in enumerate(vocab)])
# Check for ngram in vocab
self.ngram_lookup = set()
self.ngram_lengths = set()
for word in vocab:
if self.ngram_separator is not None and self.ngram_separator in word:
# Sum words might me malformed in e.g. google news word2vec, containing two or more _ after each other
ngram_count = word.count(self.ngram_separator) + 1
if self.ngram_separator + self.ngram_separator not in word and ngram_count <= self.max_ngram_length:
self.ngram_lookup.add(word)
self.ngram_lengths.add(ngram_count)
if len(vocab) > 0:
logger.info("PhraseTokenizer - Phrase ngram lengths: {}".format(self.ngram_lengths))
logger.info("PhraseTokenizer - Num phrases: {}".format(len(self.ngram_lookup)))
def tokenize(self, text: str) -> List[int]:
tokens = nltk.word_tokenize(text, preserve_line=True)
#phrase detection
for ngram_len in sorted(self.ngram_lengths, reverse=True):
idx = 0
while idx <= len(tokens) - ngram_len:
ngram = self.ngram_separator.join(tokens[idx:idx + ngram_len])
if ngram in self.ngram_lookup:
tokens[idx:idx + ngram_len] = [ngram]
elif ngram.lower() in self.ngram_lookup:
tokens[idx:idx + ngram_len] = [ngram.lower()]
idx += 1
#Map tokens to idx, filter stop words
tokens_filtered = []
for token in tokens:
if token in self.stop_words:
continue
elif token in self.word2idx:
tokens_filtered.append(self.word2idx[token])
continue
token = token.lower()
if token in self.stop_words:
continue
elif token in self.word2idx:
tokens_filtered.append(self.word2idx[token])
continue
token = token.strip(string.punctuation)
if token in self.stop_words:
continue
elif len(token) > 0 and token in self.word2idx:
tokens_filtered.append(self.word2idx[token])
continue
return tokens_filtered
def save(self, output_path: str):
with open(os.path.join(output_path, 'phrasetokenizer_config.json'), 'w') as fOut:
json.dump({'vocab': list(self.word2idx.keys()), 'stop_words': list(self.stop_words), 'do_lower_case': self.do_lower_case, 'ngram_separator': self.ngram_separator, 'max_ngram_length': self.max_ngram_length}, fOut)
@staticmethod
def load(input_path: str):
with open(os.path.join(input_path, 'phrasetokenizer_config.json'), 'r') as fIn:
config = json.load(fIn)
return PhraseTokenizer(**config)
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