问题描述
我有兴趣了解有关自然语言处理 (NLP) 的更多信息，并且很好奇目前是否有任何策略可以识别文本中不基于字典识别的专有名词?另外，任何人都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 方面的权威专家或有关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
确定文本中某个单词的正确词性的任务称为 部分语音标记.例如，Brill 标注器 混合使用了字典(词汇)单词和上下文规则.我相信这个任务的一些重要的初始词典词是停用词.一旦你的单词有(大部分正确)词性，你就可以开始构建更大的结构.这本面向行业的书籍区分识别名词短语 (NPs) 和识别命名实体.关于教科书:Allen's Natural Language Understanding很好，但有点过时了，书.统计自然语言处理基础是对统计 NLP.语音和语言处理更严格一些，也许更权威.计算语言学协会是计算语言学领域的领先科学团体.
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.
这篇关于在 NLP 中识别专有名词的策略的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！