Scenario-based question answering (SQA) has attracted an increasing research interest. Compared with the well-studied machine reading comprehension (MRC), SQA is a more challenging task: scenario may contain not only textual passage to read but also structured data like tables, i.e., tabular based (TSQA). AI applications of TSQA such as multiple-choice questions in high-school exams require syn...

People often encounter situations where they need to recall past experiences from their daily life. In this paper, we aim construct a question answering system that enables human query over personal knowledge base. Previous works on base focus finding answers for answerable questions. the real world applications, however, people muddle up facts and ask those questions cannot be answered with Th...

Despite the severe impact of COVID-19 on humans has already decreased, people still need to be aware recent disease information. A continually updated Frequently Asked Questions (FAQ) system could help public get valid and relevant To maintain a FAQ manually needs much effort, hence an approach develop automatically is needed. Question Answering System (QAS) that can accept input in question se...

This paper describes the retrieval experiments for the main task and list task of the TREC-10 questionanswering track. The question answering system described automatically finds answers to questions in a large document collection. The system uses a two-stage retrieval approach to answer finding based on matching of named entities, linguistic patterns, and keywords. In answering a question, the...

This paper describes a question answering system that automatically finds answers to questions in a large collection of documents. The prototype CNLP question answering system was developed for participation in the TREC-9 question answering track. The system uses a two-stage retrieval approach to answer finding based on keyword and named entity matching. Results indicate that the system ranks c...