piskvorky/gensim

potential Doc2Vec feature: reverse inference, to synthesize doc/summary words

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#2,459 创建于 2019年4月21日

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 (5 评论) (0 反应) (0 负责人)Python (15,144 star) (4,349 fork)batch import
Hacktoberfestdifficulty mediumfeaturegood first issuewishlist

描述

Motivated by the SO question: https://stackoverflow.com/questions/55768598/interpret-the-doc2vec-vectors-clusters-representation/55779049#55779049

Doc2Vec could plausibly have a function that's reverse-inference: take a doc-vector, return a (ranked) list of words most-predicted by that input vector. It'd work highly analogously to Word2Vec.predict_output_word(). Such a list of words might be useful as a sort-of summary or label for a doc-vector.

贡献者指南

技术栈
python
领域
machine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-2 days
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
mostly clear
前置要求
Pythongensim basicsWord2Vec.predict output word method
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
40
研究方向
The issue requests a reverse inference method for Doc2Vec, analogous to Word2Vec's predict output word. Start by examining the source code for predict output word in gensim's Word2Vec implementation, then adapt it for Doc2Vec using the doc vector as input. The issue comments and the linked StackOverflow question may provide additional context and usage examples. Ensure the implementation returns a ranked list of words most likely predicted by a given doc vector.