在自然语言处理中，分块的目的是什么? [英] In Natural language processing, what is the purpose of chunking?

问题描述

在自然语言处理中，分块的目的是什么?

In Natural language processing, what is the purpose of chunking?

推荐答案

组块也称为浅层解析，它基本上是对语音部分和短短语(例如名词短语)的识别. 部分词性标记告诉您单词是否是名词，动词，形容词等，但事实并非如此.无法为您提供有关句子结构或句子中短语的任何线索.有时，拥有更多信息而不仅仅是单词的词性是有用的，但是您并不需要解析所能得到的完整的解析树.

Chunking is also called shallow parsing and it's basically the identification of parts of speech and short phrases (like noun phrases). Part of speech tagging tells you whether words are nouns, verbs, adjectives, etc, but it doesn't give you any clue about the structure of the sentence or phrases in the sentence. Sometimes it's useful to have more information than just the parts of speech of words, but you don't need the full parse tree that you would get from parsing.

命名实体识别是一个比较合适的例子.在NER中，您的目标是找到命名实体，这些命名实体通常是名词短语(尽管并非总是如此)，因此您想知道总统巴拉克·奥巴马在下面的句子中:

An example of when chunking might be preferable is Named Entity Recognition. In NER, your goal is to find named entities, which tend to be noun phrases (though aren't always), so you would want to know that President Barack Obama is in the following sentence:

总统巴拉克·奥巴马批评保险公司和银行，他敦促支持者向国会施加压力，要求其支持他修改医疗体系和改革金融法规的行动. (源)

President Barack Obama criticized insurance companies and banks as he urged supporters to pressure Congress to back his moves to revamp the health-care system and overhaul financial regulations. (source)

但是您不一定在乎他是句子的主题.

But you wouldn't necessarily care that he is the subject of the sentence.

分组处理也已相当普遍地用作其他任务的预处理步骤，例如基于示例的机器翻译，自然语言理解，语音生成等.

Chunking has also been fairly commonly used as a preprocessing step for other tasks like example-based machine translation, natural language understanding, speech generation, and others.

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