在自然语言处理中，分块的目的是什么?

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. 这篇关于在自然语言处理中，分块的目的是什么?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！