In this paper, we present a word-for-word glossing algorithm that requires only a source language corpus. To gloss a word, we first identify its similar words that occurred in the same context in a large corpus. We then determine the gloss by maximizing the similarity between the set of ...
tmp_list=[tt]print('=== 词库中距离“{}”的前5个最近的词是: ==='.format(tt))foriiinmodel.most_similar(tt, topn=5):#计算余弦距离最,得到最接近每个热词的前5个词print(ii)#前面的是词汇,后面的是相似度值,即余弦距离值tmp_list.append(ii[0]) most_similar_list.append(tmp_list)#第1列...
Need synonyms for likelihood? Here's a list of similar words from our thesaurus that you can use instead.Contexts ▼ The chance of something happening The quality of being convincing or believable The innate qualities or abilities that may lead to future success or usefulness A future event...
Using the DistBelief distributed framework, it should be possible to train the CBOW and Skip-gram models even on corpora with one trillion words, for basically unlimited size of the vocabulary. That is several orders of magnitude larger than the best previously published results for similar models...
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def get_similar_word(words, scope=nlp.vocab): guess_word = make_guess_word(words) similarities = [] for word in scope: if not word.has_vector: continue similarity = vector_similarity(guess_word, word.vector) similarities.append((word, similarity)) ...
2000. Word-for-word glossing of contextually similar words. In Proceedings of the Conference on Applied Natural Language Processing / 1st Meeting of the North American Chapter of the Association for Computational Linguistics (ANLP- NAACL-00), pages 78-85, Seattle, WA....
Compound Words: simply add the phrase compound words, or that are compound or similar addition to your search Origin: if you're searching for words of a specific root language, or origin. What is the origin? Example: scottish origins Language: For names only, you can include language in yo...
min_count : int, optional Ignores all words with total frequency lower than this. workers : int, optional Use these many worker threads to train the model (=faster training with multicore machines). sg : {0, 1}, optional Training algorithm: 1 for skip-gram; otherwise CBOW. hs : {0,...
words= [wforwinwordsifwnotineng_stopwords]returnwords sentences= [clean_text(s)forsinsentences]#这里可以说是最关键的,gensim需要的格式就是把每条评论弄成['with', 'all', 'this', 'stuff', 'going',...]的格式。#再次强调,这里最关键,格式不对则没法学习。