使用Word2Vec解决多义问题 [英] Using Word2Vec for polysemy solving problems
问题描述
我对Word2Vec有一些疑问:
I have some questions about Word2Vec:
-
什么决定结果模型向量的维?
What determines the dimension of the result model vectors?
该向量的元素是什么?
如果我已经掌握了单词的每种含义的文本，可以使用Word2Vec来解决多义性问题(状态=管理单位vs状态=条件)吗?
Can I use Word2Vec for polysemy solving problems (state = administrative unit vs state = condition), if I already have texts for every meaning of words?
推荐答案
(1)选择所需的维数作为模型的元参数.时间充裕的严格项目可能会尝试不同的规模，以了解哪种方法最适合其定性评估.
(1) You pick the desired dimensionality, as a meta-parameter of the model. Rigorous projects with enough time may try different sizes, to see what works best for their qualitative evaluations.
(2)在普通word2vec中，每个单词向量(浮点数)的单个维度/元素不容易解释.只有作为整体的单词排列才有用–将相似的单词彼此靠近放置，并使相对方向(例如，从国王"向女王"迈进")与人类对类别/连续属性的直觉相匹配.而且，由于算法使用显式随机化，并且优化的多线程操作将线程调度的随机性引入了训练顺序示例，因此，即使是完全相同的数据也可能导致运行时的向量坐标不同(但效果都很好)运行.
(2) Individual dimensions/elements of each word-vector (floating-point numbers), in vanilla word2vec are not easily interpretable. It's only the arrangement of words as a whole that has usefulness – placing similar words near each other, and making relative directions (eg "towards 'queen' from 'king'") match human intuitions about categories/continuous-properties. And, because the algorithms use explicit randomization, and optimized multi-threaded operation introduces thread-scheduling randomness to the order-of-training-examples, even the exact same data can result in different (but equally good) vector-coordinates from run-to-run.
(3)基本的word2vec修复起来并不容易，但是向量中有一堆多义性的暗示，并且研究工作正在做更多工作来消除对比感.
(3) Basic word2vec doesn't have an easy fix, but there's a bunch of hints of polysemy in the vectors, and research work to do more to disambiguate contrasting senses.
例如，通常，多义词的词缀以词向量结束，这些词向量是它们的多种意义的组合，并且(通常)比多义词的词的幅值更小.
For example, generally more-polysemous word-tokens wind up with word-vectors that are some combination of their multiple senses, and (often) of a smaller-magnitude than less-polysemous words.
此早期论文每个单词使用了多种表示形式来帮助发现一词多义.类似的后来论文，例如此使用上下文聚类发现多义词，然后重新标记它们以给出每个人都有自己的载体.
This early paper used multiple representations per word to help discover polysemy. Similar later papers like this one use clustering-of-contexts to discover polysemous words then relabel them to give each sense its own vector.
本文通过对正常word2vec向量进行后处理来检测替代感官.
This paper manages an impressive job of detecting alternate senses via postprocessing of normal word2vec vectors.
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