短文本理解研究

Short Text Understanding:A Survey

短文本理解是一项对于机器智能至关重要但又充满挑战的任务.这项任务有益于众多应用场景,如搜索引擎、自动问答、广告和推荐系统.完成这些应用的首要步骤是将输入文本转化为机器可以诠释的形式,即帮助机器"理解"短文本的含义.基于这一目标,许多方法利用外来知识源来解决短文本中语境信息不足的问题.通过总结短文本理解领域的相关工作,介绍了基于向量的短文本理解框架.同时,探讨了短文本理解领域未来的研究方向.

Short text understanding is an important but challenging task relevant for machine intelligence. The task can potentially benefit various online applications, such as search engines, automatic question-answering, online advertising and recommendation systems. In all these applications, the necessary first step is to transform an input text into a machine-interpretable representation, namely to “understand” the short text. To achieve this goal, various approaches have been proposed to leverage external knowledge sources as a complement to the inadequate contextual information accompanying short texts. This survey reviews current progress in short text understanding with a focus on the vector based approaches, which aim to derive the vectorial encoding for a short text. We also explore a few potential research topics in the field of short text understanding.