Abstract
Semantic understanding of web queries is a challenging problem as web queries are short, noisy and usually do not observe the grammar of a written language. In this paper, we specifically study the user web search queries with local intent on Bing. Local intent queries deal with searching for local businesses and services in a location. Hence, local query parsing translates into the classical problem of Named Entity Recognition (NER) in NLP. State-of-the-art NER systems rely heavily on hand-crafted features and domain-specific knowledge to effectively learn from the small, supervised training corpora that is available. In this paper, we use deep learnt neural model that relies solely on features extracted from word embeddings learnt in an unsupervised way, using search logs. We propose a novel technique for generating domain specific embeddings and show that they significantly improve the performance of existing models for the NER task. Our model outperforms the existing CRF based parser currently used in production.
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Mittal, S., Agarwal, M.K. (2019). Named Entity Recognition in Local Intent Web Search Queries. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_31
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DOI: https://doi.org/10.1007/978-3-030-27615-7_31
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