Passage Retrieval for Information Extraction using Distant Supervision

In this paper, we propose a keyword-based passage retrieval algorithm for information extraction, trained by distant supervision. Our goal is to be able to extract attributes of people and organizations more quickly and accurately by first ranking all the potentially relevant passages according to their likelihood of containing the answer and then performing a traditional deeper, slower analysis of individual passages. Using Freebase as our source of known relation instances and Wikipedia as our text source, we collected a weighted set of keywords indicative of each relation and then use it to re-rank the passages retrieved by the Lemur search engine. Experiments show that our algorithm significantly outperforms stateof-the-art passage retrieval techniques in evaluations of both individual passage retrieval and end-to-end information extraction.