Paper: Improving Web Search Relevance with Semantic Features

ACL ID D09-1068
Title Improving Web Search Relevance with Semantic Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

Most existing information retrieval (IR) systems do not take much advantage of natural language processing (NLP) tech- niques due to the complexity and limited observed effectiveness of applying NLP to IR. In this paper, we demonstrate that substantial gains can be obtained over a strong baseline using NLP techniques, if properly handled. We propose a frame- work for deriving semantic text matching features from named entities identified in Web queries; we then utilize these features in a supervised machine-learned ranking approach, applying a set of emerging ma- chine learning techniques. Our approach is especially useful for queries that contain multiple types of concepts. Comparing to a major commercial Web search engine, we observe a substantial 4% DCG5 gain over the affected queries.