Context Retrieval for Web Tables

Many modern knowledge bases are built by extracting information from millions of web pages. Though existing extraction methods primarily focus on web pages' main text, a huge amount of information is embedded within other web structures, such as web tables. Previous studies have shown that linking web page tables and textual context is beneficial for extracting more information from web pages. However, using the text surrounding each table without carefully assessing its relevance introduces noise in the extracted information, degrading its accuracy. To the best of our knowledge, we provide the first systematic study of the problem of table-related context retrieval: given a table and the sentences within the same web page, determine for each sentence whether it is relevant to the table. We define the concept of relevance and introduce a Table-Related Context Retrieval system (TRCR) in this paper. We experiment with different machine learning algorithms, including a recently developed algorithm that is robust to biases in the training data, and show that our system retrieves table-related context with F1=0.735.