Learning Topic-Oriented Word Embedding for Query Classification

In this paper, we propose a topic-oriented word embedding approach to address the query classification problem. First, the topic information is encoded to generate query categories. Then, the user click-through information is also incorporated in the modified word embedding algorithms. After that, the short and ambiguous queries are enriched to be classified in a supervised learning way. The unique contributions are that we present four neural network strategies based on the proposed model. The experiments are designed on two open data sets, namely Baidu and Sogou, which are two famous commercial search companies. Our evaluation results show that the proposed approach is promising on both large data sets. Under the four proposed strategies, we achieve the high performance as 95.73% in terms of Precision, 97.79% in terms of the F1 measure.