Paper: Scaling Textual Inference to the Web

ACL ID D08-1009
Title Scaling Textual Inference to the Web
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008

Most Web-based Q/A systems work by find- ing pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sen- tences, possibly on different pages. To solve this problem, we introduce the HOLMES sys- tem, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the lit- erature on textual entailment) has been ap- plied to paragraph-sized texts, HOLMES uti- lizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, HOLMES doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, HOLMES’s runtime is linear in the size of its input corpus due to a surprising property...