Yumao Lu

Context sensitive synonym discovery for web search queries

We propose a simple yet effective approach to context sensitive synonym discovery for Web search queries based on co-click analysis; i.e., analyzing queries leading to clicking same documents. In addition to deriving word based synonyms, we also derive concept based synonyms with the help of query segmentation. Evaluation results show that this approach dramatically outperforms the thesaurus based synonym replacement method in keeping search intent, from accuracy of 40% to above 80%.

Personalize web search results with user's location

We build a probabilistic model to identify implicit local intent queries, and leverage users physical location to improve Web search results for these queries. Evaluation on commercial search engine shows significant improvement on search relevance and user experience.

Improving Web Search Relevance with Semantic Features

Most existing information retrieval (IR) systems do not take much advantage of natural language processing (NLP) techniques 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 framework 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 machine 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.