PCFG与SR解析器 [英] PCFG vs SR Parser

问题描述

看起来stanfordnlp拥有这些SR模型已有一段时间了. 我对NLP真的很陌生，但是我们目前正在使用PCFG解析器，并且遇到了严重的性能问题(将解析长度减少到35)

It looks like stanfordnlp has these SR models for some time. I am really new to NLP but we are currently using PCFG parser and we are having serious performance issues( that we cut down the parse length to 35)

1. 我在考虑是否可以尝试使用SR.我用斯坦福的POS标记器尝试过(english-left3words-distsim.tagger) 您是否知道SR与PCFG相比精度如何? 我还发现SR和dep parse的句子词根检测问题:示例:
   迈克尔·杰弗里·乔丹(Michael Jeffrey Jordan)，也以他的首字母缩写MJ着称，他是美国前职业篮球运动员，企业家，并且是夏洛特山猫队的现任多数股权和董事长 PCFG的根确实非常准确，并且可以将播放器检测为根.
2. 也将对人们使用NN的一些见解表示赞赏，例如(

1. I was thinking if we could try using SR. I tried it with POS tagger from stanford(english-left3words-distsim.tagger) Would you know how SR is on accuracy vs PCFG? I also find sentence root detection issues with SR and dep parse: Example:
   Michael Jeffrey Jordan, also known by his initials, MJ, is an American former professional basketball player, entrepreneur, and current majority owner and chairman of the Charlotte Bobcats The PCFG is really accurate with the root and detects player as the root.
2. Would also appreciate a little insight on the NN people use e.g.(https://mailman.stanford.edu/pipermail/java-nlp-user/2014-November/006513.html) in above post. Do I need to use another tagger like - left3words with this? I am sorry if this sounds a little naive. But all I want is a correct sentence root and its dependencies. Does POS tagging upfront make it fast?

非常感谢.

推荐答案

1. CoreNLP附带的英文版的shift-reduce解析器实际上比我们的测试数据上的PCFG解析器稍好.您可以在 shift-reduce解析器主页的底部看到性能指标.
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2. 我已在上面的评论中要求澄清.

1. The English shift-reduce parser shipped with CoreNLP is actually slightly better than the PCFG parser on our test data. You can see performance metrics at the bottom of the shift-reduce parser homepage.
2. I've asked for clarification in a comment above.

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