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