Question Answering over Implicitly Structured Web Content

Abstract

Implicitly structured content on the Web such as HTML tables and lists can be extremely valuable for web search, question answering, and information retrieval, as the implicit structure in a page often reflects the underlying semantics of the data. Unfortunately, exploiting this information presents significant challenges due to the immense amount of implicitly structured content on the web, lack of schema information, and unknown source quality. We present TQA, a web-scale system for automatic question answering that is often able to find answers to real natural language questions from the implicitly structured content on the web. Our experiments over more than 200 million structures extracted from a partial web crawl demonstrate the promise of our approach.