文件名称：Applied Text Analysis with Python
文件大小：2.56MB
文件格式：EPUB
更新时间：2021-04-24 02:38:35
NLP
自然语言处理，使用Python As we explored the architecture of language in the previous chapter, we began to see that it is possible to model natural language in spite of its complexity and flexibility. And yet, the best language models are often highly constrained and application-specific. Why is it that models trained in a specific field or domain of the language would perform better than ones trained on general language? Consider that the term “bank” is very likely to be an institution that produces fiscal and monetary tools in an economics, financial, or political domain, whereas in an aviation or vehicular domain it is more likely to be a form of motion that results in the change of direction of an aircraft. By fitting models in a narrower context, the prediction space is smaller and more specific, and therefore better able to handle the flexible aspects of language.