Lexicon Modeling for Query Understanding

Lexicons are important resources for semantic tagging. However,

commonly used lexicons collected from entity databases suffer

from multiple problems, such as ambiguity, limited coverage and

lack of relative importance. In this work we present a lexicon

modeling technique that automatically expands the lexicon and

assigns weights to its elements. For lexicon expansion, we use a

generative model to extract patterns from query logs using known

lexicon seeds, and discover new lexicon elements using the learned

patterns. For lexicon weighting, we propose two approaches based

on generative and discriminative models to learn the relative

importance of lexicon elements from user click statistics.

Experiments on text queries in multiple domains show that our

lexicon modeling technique can significantly improve semantic

tagging performance.

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Publisher  IEEE


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