Named Entity Mining from Click-Through Data Using Weakly Supervised Latent Dirichlet Allocation

author: Shuang-Hong Yang, Twitter, Inc.
author: Gu Xu, McMaster University
published: Sept. 14, 2009,   recorded: July 2009,   views: 5781
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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.

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Reviews and comments:

Comment1 Ramesh Lavhe, February 8, 2011 at 5:05 a.m.:

plz send me source code of this WS-LDA algorithm

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