在 word2vec Gensim 中获取二元组和三元组

Question

我目前在我的 word2vec 模型中使用 uni-gram，如下所示。

 def review_to_sentences( review, tokenizer, remove_stopwords=False ):
    #Returns a list of sentences, where each sentence is a list of words
    #
    #NLTK tokenizer to split the paragraph into sentences
    raw_sentences = tokenizer.tokenize(review.strip())

    sentences = []
    for raw_sentence in raw_sentences:
        # If a sentence is empty, skip it
        if len(raw_sentence) > 0:
            # Otherwise, call review_to_wordlist to get a list of words
            sentences.append( review_to_wordlist( raw_sentence, \
              remove_stopwords ))
    #
    # Return the list of sentences (each sentence is a list of words,
    # so this returns a list of lists
    return sentences

但是，我会错过数据集中重要的二元组和三元组。

 E.g.,
"team work" -> I am currently getting it as "team", "work"
"New York" -> I am currently getting it as "New", "York"

因此，我想在我的数据集中捕获重要的二元组、三元组等，并将其输入到我的 word2vec 模型中。

我是 wordvec 的新手并且正在努力如何去做。请帮我。

原文由 user8566323 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

首先，您应该使用 gensim 的 Phrases 类来获取双字母组，其工作原理与文档中指出的一样

>>> bigram = Phraser(phrases)
>>> sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
>>> print(bigram[sent])
[u'the', u'mayor', u'of', u'new_york', u'was', u'there']

要得到 trigrams 等等，你应该使用你已经拥有的 bigram 模型并再次对其应用 Phrases，等等。例子：

 trigram_model = Phrases(bigram_sentences)

还有一个很好的笔记本和视频，解释了如何使用它…. 笔记本， 视频

它最重要的部分是如何在现实生活中使用它，如下所示：

 // to create the bigrams
bigram_model = Phrases(unigram_sentences)

// apply the trained model to a sentence
 for unigram_sentence in unigram_sentences:
            bigram_sentence = u' '.join(bigram_model[unigram_sentence])

// get a trigram model out of the bigram
trigram_model = Phrases(bigram_sentences)

希望这对您有所帮助，但下次请向我们提供有关您使用的内容等的更多信息。

PS：现在你编辑了它，你没有做任何事情来获得二元组只是分裂它，你必须使用短语来获得像纽约这样的词作为二元组。

原文由 nitheism 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 2

from gensim.models import Phrases

from gensim.models.phrases import Phraser

documents = [
  "the mayor of new york was there",
  "machine learning can be useful sometimes",
  "new york mayor was present"
  ]

sentence_stream = [doc.split(" ") for doc in documents]
print(sentence_stream)

bigram = Phrases(sentence_stream, min_count=1, threshold=2, delimiter=b' ')

bigram_phraser = Phraser(bigram)

print(bigram_phraser)

for sent in sentence_stream:
    tokens_ = bigram_phraser[sent]

    print(tokens_)

原文由 BrB 发布，翻译遵循 CC BY-SA 4.0 许可协议