是否有任何针对特定主题的预训练向量？例如“java”，所以我想要文件中与 java 相关的向量。意思是如果我给输入继承，那么余弦相似度只显示多态性和其他相关的东西！我使用语料库作为 GoogleNews-vectors-negative300.bin 和 Glove 向量。仍然没有得到相关的词。
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Yes, you can occasionally find other groups' pre-trained vectors for download, which may have better coverage of whatever problem domains they've been trained on: both more specialized words, and word-vectors matching the word sense in that domain.
For example, the GoogleNews word-vectors were trained on news articles circa 2012, so its vector for 'Java' may be dominated by stories of the Java island of Indosnesia as much as the programming language. And many other vector-sets are trained on Wikipedia text, which will be dominated by usages in that particular reference-style of writing. But there could be other sets that better emphasize the word-senses you need.
However, the best approach is often to train your own word-vectors, from a training corpus that closely matches the topics/documents you are concerned about. Then, the word-vectors are well-tuned to your domain-of-concern. As long as you have "enough" varied examples of a word used in context, the resulting vector will likely be better than generic vectors from someone else's corpus. ("Enough" has no firm definition, but is usually at least 5, and ideally dozens to hundreds, of representative, diverse uses.)
Let's consider your example goal – showing some similarity between the ideas of 'polymorphism' and 'input inheritance'. For that, you'd need a training corpus that discusses those concepts, ideally many times, from many authors, in many problem-contexts. (Textbooks, online articles, and Stack Overflow pages might be possible sources.)
You'd further need a tokenization strategy that manages to create a single word-token for the two-word concept 'input_inheritance' - which is a separate challenge, and might be tackled via (1) a hand-crafted glossary of multi-word-phrases that should be combined; (2) statistical analysis of word-pairs that seem to occur so often together, they should be combined; (3) more sophisticated grammar-aware phrase- and entity-detection preprocessing.
(The multiword phrases in the GoogleNews set were created via a statistical algorithm which is also available in the gensim Python library as the Phrases class. But, the exact parameters Google used have not, as far as I know, been revealed.And, good results from this algorithm can require a lot of data and tuning, and still result in some combinations that a person would consider nonsense, and missing others that a person would consider natural.)
于 2020-04-06T17:43:41.600 回答
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不确定我是否理解您的问题/问题陈述，但如果您想使用 java 源代码语料库，您可以使用code2vec，它提供预训练的词嵌入模型。看看：https ://code2vec.org/
于 2020-04-05T23:01:08.063 回答