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Improving Web Search Relevance with Semantic Features

Improving Web Search Relevance with Semantic Features,Yumao Lu,Fuchun Peng,Gilad Mishne,Xing Wei,Benoît Dumoulin

Improving Web Search Relevance with Semantic Features   (Citations: 2)
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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.
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    • ...The problem of improving predictive performance by incorporating structured knowledge has been addressed in previous work, especially in the fields of text classification and information retrieval [1, 2, 3, 4, 5, 6, 7, 8]. Despite a number of interesting results, the conclusions drawn from these works are multi-faceted, i.e., sometimes the structured knowledge is of benefit, and sometimes, even with carefully tuned NLP procedures and ...,Much of the related work done in the area of information retrieval has been in the context of query expansion techniques [3, 7]. Recent work by Lu et al. [6] proposes document and query-side processing to extract entity-oriented features...

    Weiwei Chenget al. Automated feature generation from structured knowledge

    • ...As previous research shows, these differences severely limit the applicability of standard NLP techniques for annotating query corpora [1, 3, 15]...
    • ...The literature on query annotation includes query segmentation [3, 12, 9, 20], part-of-speech and semantic tagging [1, 17], named-entity recognition [8, 15, 19, 18], abbreviation disambiguation [21] and stopword detection [14, 11]...

    Michael Benderskyet al. Structural annotation of search queries using pseudo-relevance feedbac...

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