什么是用于释义的好的自然语言库? [英] What's a good natural language library to use for paraphrasing?

问题描述

我正在寻找一个现有的库来汇总或解释内容(我的目标是博客文章)-对现有的自然语言处理库有任何经验吗?

I'm looking for an existing library to summarize or paraphrase content (I'm aiming at blog posts) - any experience with existing natural language processing libraries?

我可以使用多种语言，因此我对功能&准确性.

I'm open to a variety of languages, so I'm more interested in the abilities & accuracy.

推荐答案

对Grok进行了一些讨论.现在，它已作为OpenCCG受到支持，并且还将在OpenNLP中重新实现.

There was some discussion of Grok. This is now supported as OpenCCG, and will be reimplemented in OpenNLP as well.

您可以在 http://openccg.sourceforge.net/中找到OpenCCG.我还建议在此处提供Curran和Clark CCG解析器: http://svn.ask.it.usyd.edu.au/trac/candc/wiki

You can find OpenCCG at http://openccg.sourceforge.net/. I would also suggest the Curran and Clark CCG parser available here: http://svn.ask.it.usyd.edu.au/trac/candc/wiki

基本上，对于释义，您需要做的是写一些东西，首先解析博客文章的句子，提取这些文章的语义，然后搜索词汇的空间，从而构成具有相同的语义，然后选择与当前句子不匹配的语义.这将花费很长时间，并且可能没有任何意义.不要忘记，要做到这一点，您将需要近乎完美的回指解析度以及获得话语级推论的能力.

Basically, for paraphrase, what you're going to need to do is write up something that first parses sentences of blog posts, extracts the semantic meaning of these posts, and then searches through the space of vocab words which will compositionally create the same semantic meaning, and then pick one that doesn't match the current sentence. This will take a long time and it might not make a lot of sense. Don't forget that in order to do this, you're going to need near-perfect anaphora resolution and the ability to pick up discourse-level inferences.

如果您只是想撰写没有机器可识别的重复内容的博客文章，则始终可以仅使用主题和焦点转换以及WordNet同义词.肯定有一些网站以前是通过AdWords赚钱的.

If you're just looking to make blog posts that don't have machine-identifiable duplicate content, you can always just use topic and focus transformations and WordNet synonyms. There have definitely been sites which have made money off of AdWords that have done this before.

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