Information Retrieval

25th China Conference, CCIR 2019, Fuzhou, China, September 20¿22, 2019, Proceedings
Herausgegeben:Zhang, Qi; Liao, Xiangwen; Ren, Zhaochun

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed proceedings of the 25th China Conference on Information Retrieval, CCIR 2019, held in Fuzhou, China, in September 2019.

The 13 full papers presented were carefully reviewed and selected from 45 submissions.
Academic research is aimed at the precise acquisition of information and knowledge by human beings. The research results will support national strategic decision-making, promote the development of the Internet and IT fields, enhance the production efficiency of the industry, and have a major impact on various fields of social life.

Produktdetails

• Theoretical Computer Science and General Issues 11772
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-31623-5
• 1st ed. 2019
• Seitenzahl: 188
• Erscheinungstermin: 18. September 2019
• Englisch
• Abmessung: 235mm x 155mm x 11mm
• Gewicht: 295g
• ISBN-13: 9783030316235
• ISBN-10: 3030316238
• Artikelnr.: 57508663

Inhaltsangabe

Mining User Profiles from Query Log.- A User Effort Measurement for Query Selection.- Temporal Smoothing: Discriminatively Incorporating Various Temporal Profiles of Queries.- Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion.- Temporal Convolutional Networks for Popularity Prediction of Messages on Social Medias.- Selecting Paragraphs to Answer Questions for Multi-Passage Machine Reading Comprehension.- Document-level Named Entity Recognition by Incorporating Global and Neighbor Features.- Investigating Query Reformulation Behavior of Search Users.- A Method for User Avatar Authenticity Based on Multi-feature Fusion.- Multi-granularity Convolutional Neural Network with Feature Fusion and Refinement for User Profiling.- Ensemble System for Identification of Cited Text Spans: Based on Two Steps of Feature Selection.- LTRRS: A Learning to Rank Based Algorithm for Resource Selection in Distributed Information Retrieval.- Joint Learning forAspect Category Detection and Sentiment Analysis in Chinese Reviews.