โ Scribed by Xue, Xiaobing; Yin, Xiaoxin
No coin nor oath required. For personal study only.
Named entities are observed in a large portion of web search queries (named entity queries), where each entity can be associated with many different query terms that refer to various aspects of this entity. Organizing these query terms into topics helps understand major search intents about entities and the discovered topics are useful for applications such as query suggestion. Furthermore, we notice that named entities can often be organized into categories and those from the same category share many generic topics. Therefore, working on a category of named entities instead of individual ones helps avoid the problems caused by the sparsity and noise in the data. In this paper, Named Entity Topic Model (NETM) is proposed to discover generic topics for a category of named entities, where the quality of the generic topics is improved through the model design and the parameter initialization. Experiments based on query log data show that NETM discovers high-quality topics and outperforms the state-of-the-art techniques by 12.8% based on F1 measure.
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