Optimization of an integrated model for automatic reduction and expansion of long queries A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intraquery term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three largescale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on userxe2x80x99s interactive query term reduction and expansion.

Machine ComprehensionIncorporated Relevance Matching In current web search engines, the relevance between a query and web pages (i.e. documents) is measured by Text Matching (TM) models. The documents retrieved mainly focus on matching text queries themselves but fail to find target information towards the user intent (e.g. the direct answer to questionstyle queries). Thus the document containing target information that users want may not be ranked at the top 1 in the search result or even not be recalled. Besides, as voice search and voicepowered assistants are entering our life, queries tend to be long tail ones, which needs a search engine evolved into a higher level of semantic relevance matching. Therefore, it is necessary to build an intenttarget relevance matching model in modern search scenarios. This paper proposes a unified model of Machine Comprehensionincorporated Relevance Matching (MCRM). Totally, MCRM models how web users choose the relevant documents to read by observing the titles or summaries, and further look for target information from them. To accomplish that, we first formulate two tasks as Text Matching and Target Extracting. For learning each task, a Contextaugmented Matching network (ContMatch) and a Matchingfused machine Comprehension network (MatComprehend) are proposed. Then, they are integrated into an endtoend framework that can not only measure the semantic relevance but also extract the intentrelated target, by deeply comprehending the semantics hidden in queries and exploiting intenttarget relations between queries and documents. In MCRM, the two tasks are jointly learned by a multitask learning approach, where the semantic relevance measured by ContMatch, and the intenttarget relevance captured by MatComprehend, are combined and enhanced mutually for boosting the final performance. We conduct extensive experiments on realworld data. The experimental results demonstrate the superiority of MCRM against the stateoftheart relevance models.