Optimizing scoring functions and indexes for proximity search in type-annotated corpora

We introduce a new, powerful class of text proximity queries: find an instance of a given "answer type" (person, place, distance) near "selector" tokens matching given literals or satisfying given ground predicates. An example query is type=distance NEAR Hamburg Munich. Nearness is defined as a flexible, trainable parameterized aggregation function of the selectors, their frequency in the corpus, and their distance from the candidate answer. Such queries provide a key data reduction step for information extraction, data integration, question answering, and other text-processing applications. We describe the architecture of a next-generation information retrieval engine for such applications, and investigate two key technical problems faced in building it. First, we propose a new algorithm that estimates a scoring function from past logs of queries and answer spans. Plugging the scoring function into the query processor gives high accuracy: typically, an answer is f...

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Soumen Chakrabarti, Kriti Puniyani, Sujatha Das
• Keywords: Document Trec Corpus, Internet Technology, Text Proximity Queries, TREC Queries, WWW 2006