论文标题
使用LSTM架构进行提问的意图分类
Intent Classification in Question-Answering Using LSTM Architectures
论文作者
论文摘要
提问（QA）当然是自然语言处理（NLP）和人工智能（AI）中最著名的，也可能是最复杂的问题之一。由于解决通用答案问题的完整解决方案似乎仍然很远，因此最明智的事情是通过解决单个简单的部分来分解问题。假设解决问题的模块化方法，我们将研究仅限于意图分类以寻求答案。通过使用LSTM网络，我们展示了如何有效，有效地处理这种类型的分类，以及如何在基本原型响应者中正确使用它。
Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler parts. Assuming a modular approach to the problem, we confine our research to intent classification for an answer, given a question. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder.
