Named entity mining from click-through data using weakly supervised latent dirichlet allocation

  • Authors:

  • Gu Xu;Shuang-Hong Yang;Hang Li

  • Affiliations:

  • Microsoft Research Asia, Beijing, China;Georgia Institute of Technology, Atlanta, GA, USA;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2009

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Abstract

This paper addresses Named Entity Mining (NEM), in which we mine knowledge about named entities such as movies, games, and books from a huge amount of data. NEM is potentially useful in many applications including web search, online advertisement, and recommender system. There are three challenges for the task: finding suitable data source, coping with the ambiguities of named entity classes, and incorporating necessary human supervision into the mining process. This paper proposes conducting NEM by using click-through data collected at a web search engine, employing a topic model that generates the click-through data, and learning the topic model by weak supervision from humans. Specifically, it characterizes each named entity by its associated queries and URLs in the click-through data. It uses the topic model to resolve ambiguities of named entity classes by representing the classes as topics. It employs a method, referred to as Weakly Supervised Latent Dirichlet Allocation (WS-LDA), to accurately learn the topic model with partially labeled named entities. Experiments on a large scale click-through data containing over 1.5 billion query-URL pairs show that the proposed approach can conduct very accurate NEM and significantly outperforms the baseline.