动名超常搭配由于一般带有成分的省略或隐喻,给中文信息处理带来了难度。以往一般是通过整体打包进词库的形式来直接处理,而本文的处理思路是对汉语动名超常搭配进行解构还原的方式来进行处理。具体做法是,依托西方生成词库理论的思想构建一套专门的汉语词项描述体系,这一知识表示体系可以较为清晰的还原因省略或隐喻而造成的非常规搭配,进而解读出它们的组合机制和生成过程。然后本文通过补缺和替换的形式把动名非常规搭配还原为常规性搭配来进行处理。实验表明,这种思路处理动名超常搭配切实有效。

Most question answering (QA) systems are based on raw text and structured knowledge graph. However, raw text corpora are hard for QA system to understand, and structured knowledge graph needs intensive manual work, while it is relatively easy to obtain semi-structured tables from many sources directly, or build them automatically. In this paper, we build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. Our system answers queries by two steps. First, it finds the most similar tables. Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer. The system is evaluated with TabMCQ dataset, and gets a huge improvement compared to the state of the art.