Titlebook: Natural Language Processing and Chinese Computing; 6th CCF Internationa Xuanjing Huang,Jing Jiang,Yu Hong Conference proceedings 2018 Sprin

宗派

書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)




書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開度




書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing被引頻次




書目名稱Natural Language Processing and Chinese Computing被引頻次學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing年度引用




書目名稱Natural Language Processing and Chinese Computing年度引用學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing讀者反饋




書目名稱Natural Language Processing and Chinese Computing讀者反饋學(xué)科排名

下船

HACK

labyrinth

Natural Language Processing and Chinese Computing978-3-319-73618-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

Soliloquy

過剩

Conference proceedings 2018ina, in November 2017.. The 47 full papers and 39 short papers presented were carefully reviewed and selected from 252 submissions.? The papers are organized around the following topics: IR/search/bot; knowledge graph/IE/QA; machine learning; machine translation; NLP applications; NLP fundamentals; social networks; and text mining..

rheumatism

Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Informationsults on different datasets show that the proposed models outperform joint models without either hierarchical or contextual information. Besides, finding the balance between two loss functions of two subtasks is important to achieve best overall performances.

雜役

persistence

First Place Solution for NLPCC 2017 Shared Task Social Media User Modelingproach results, including user-based collaborative filtering (CF) and social-based CF to predict the locations. Subtask two is to predict the users’ gender. We divided the users into two groups, depending on whether the user has posted or not. We treat this task subtask as a classification task. Our results achieved first place in both subtasks.

MEET

A Chinese Question Answering System for Single-Relation Factoid Questions are used to choose the final predicted answers. Our approach achieved the F1-score of 47.23% on test data which obtained the first place in the contest of NLPCC 2017 Shared Task 5 (KBQA sub-task). Furthermore, there are also a series of experiments which can help other developers understand the contribution of every part of our system.