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In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been(More)
Short texts are popular on today's web, especially with the emergence of social media. Inferring topics from large scale short texts becomes a critical but challenging task for many content analysis tasks. Conventional topic models such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) learn topics from document-level(More)
This paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classification of the named entity into predefined classes. NERQ is potentially useful in many applications in web search. The paper proposes taking a probabilistic approach to the task using query log data and(More)
Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to explore the sequential behavior (i.e., buying one item(More)
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficulty in obtaining reliable relevance judgments from human assessors when applying learning to rank in real search systems. The traditional absolute relevance judgment method is(More)
Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this(More)
Query recommendation has been recognized as an important mean to help users search and also improve the usability of search engines. Existing approaches mainly focus on helping users refine their search queries and the recommendations typically stick to users' search intent, named <i>search interests</i> in this paper. However, users may also have some(More)
Nowadays, short texts are very prevalent in various web applications, such as microblogs, instant messages. The severe sparsity of short texts hinders existing topic models to learn reliable topics. In this paper, we propose a novel way to tackle this problem. The key idea is to learn topics by exploring term correlation data, rather than the(More)