Human-machine dialogue intention system based on Fasttext
基于Fasttext的人机对话意图系统

摘要

对话意图识别是问答系统研究热点之一，意图识别是问答系统给出答案的前提。本文首先构建Fasttext意图识别模型，并进行模型的优化，进行多重分类研究算法效果。为了减小短文本，词语重复率较低，词共现并不适用，语言规范性不强的文档集的关键词抽取问题，在Fasttext模型输入阶段使用TextRank算法和TF-IDF算法提取文本的关键子句输入训练模型,同时采用TF-IDF提取文本的关键词作为特征补充,提高文本分类的效果。获取用户问题，输入到Fasttext模型进行意图分类，返回最终答案给用户。

Abstract

Dialogue intent recognition is one of the research hotspots in question answering systems, and intent recognition is the premise for question answering systems to give answers. This paper firstly builds the Fasttext intent recognition model, optimizes the model, and conducts multi-classification to study the effect of the algorithm. In order to reduce short text, the word repetition rate is low, the word co-occurrence is not suitable, and the keyword extraction problem of the document set with low language norm is used in the input stage of the Fasttext model to use the TextRank algorithm and the TF-IDF algorithm to extract the key. The clause is input to the training model, and TF-IDF is used to extract the keywords of the text as a feature supplement to improve the effect of text classification. Get the user question, input it into the Fasttext model for intent classification, and return the final answer to the user.