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Titlebook: Information Retrieval; 25th China Conferenc Qi Zhang,Xiangwen Liao,Zhaochun Ren Conference proceedings 2019 Springer Nature Switzerland AG

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A User Effort Measurement for Query Selection about user effort in search. However, they all take only query result browsing efforts into account. Few of them measures the effort of query selection, or the effort to choose a suitable query. This paper shows that query selection effort is a significant part of overall user effort, almost as imp
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Temporal Smoothing: Discriminatively Incorporating Various Temporal Profiles of Queriesraditional approaches using the same corpus language model for every document, the mainstream of the current methods introduce an additional smoothing item that can reflect the content of each document. However, these methods would either ignore the temporal characteristics of queries, or handle tem
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Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completionly a small subset of what is true in the world. Knowledge Graph Completion (KGC) task aims to findin missing or errant relationships with the goal of improving the general quality of KGs. Recent years have witnessed great advance of represent learning (RL) based KGC models, which represent entities
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Document-Level Named Entity Recognition by Incorporating Global and Neighbor Featuresy to cause tagging inconsistency problems for long text documents. In this paper, we first propose to use the neural network to encode global consistency and neighbor relevance among occurrences of a particular token within a document. We first encode sentences within a document independently by a s
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Joint Learning for Aspect Category Detection and Sentiment Analysis in Chinese Reviewsview texts. Aspect-category sentiment analysis is a sub-task of ABSA, which mainly focus on aspect category detection and aspect category polarity identifying. Most of the previous methods employ a pipeline strategy, regarding aspect category detection and category sentiment analysis as two separate
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