NLP中识别专有名词的策略

本文介绍了NLP中识别专有名词的策略的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有兴趣了解有关自然语言处理(NLP)的更多信息，我是否对此感到好奇当前有没有不基于字典识别的策略来识别文本中的专有名词?另外，有人可以解释或链接到解释当前基于字典的方法的资源吗?谁是NLP的权威专家，或者关于该主题的权威资源是什么?

I'm interested in learning more about Natural Language Processing (NLP) and am curious if there are currently any strategies for recognizing proper nouns in a text that aren't based on dictionary recognition? Also, could anyone explain or link to resources that explain the current dictionary-based methods? Who are the authoritative experts on NLP or what are the definitive resources on the subject?

推荐答案

为文本中的单词确定正确词性的任务称为语音标记的一部分.例如， Brill标记器使用了词典(词汇)词和上下文规则的混合体.我相信，用于此任务的一些重要的初始词典词是停用词. 一旦您的单词具有(大部分正确的)词性，就可以开始构建更大的结构. 这本面向行业的书区分了识别名词短语(NP)和识别命名实体. 关于教科书:艾伦的自然语言理解很好，但是有点过时了，书. 统计自然语言处理基础是对统计NLP. 语音和语言处理较为严格，也许更权威. 计算语言学协会是关于计算语言学的领先科学界.

The task of determining the proper part of speech for a word in a text is called Part of Speech Tagging. The Brill tagger, for example, uses a mixture of dictionary(vocabulary) words and contextual rules. I believe that some of the important initial dictionary words for this task are the stop words. Once you have (mostly correct) parts of speech for your words, you can start building larger structures. This industry-oriented book differentiates between recognizing noun phrases (NPs) and recognizing named entities. About textbooks: Allen's Natural Language Understanding is a good, but a bit dated, book. Foundations of Statistical Natural Language Processing is a nice introduction to statistical NLP. Speech and Language Processing is a bit more rigorous and maybe more authoritative. The Association for Computational Linguistics is a leading scientific community on computational linguistics.