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检索条件"机构=Research Center of High Volume Language Information Processing and Cloud Computing Applications"
120 条 记 录,以下是1-10 订阅
排序:
A novel unsupervised method for new word extraction
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Science China(information Sciences) 2016年 第9期59卷 11-21页
作者: Lili MEI Heyan HUANG Xiaochi WEI Xianling MAO Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Department of Computer Science and TechnologyBeijing Institute of Technology
New words could benefit many NLP tasks such as sentence chunking and sentiment analysis. However, automatic new word extraction is a challenging task because new words usually have no fixed language pattern, and even ... 详细信息
来源: 评论
A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation
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Tsinghua Science and Technology 2018年 第6期23卷 660-670页
作者: Lerong Ma Lejian Liao DANDan Song Jingang Wang Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University
Knowledge Bases (KBs) are valuable resources of human knowledge which contribute to many applications. However, since they are manually maintained, there is a big lag between their contents and the upto-date informa... 详细信息
来源: 评论
Knowledge-enriched joint-learning model for implicit emotion cause extraction
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CAAI Transactions on Intelligence Technology 2023年 第1期8卷 118-128页
作者: Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina
Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit em... 详细信息
来源: 评论
A Hybrid Method of Domain Lexicon Construction for Opinion Targets Extraction Using Syntax and Semantics
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Journal of Computer Science & Technology 2016年 第3期31卷 595-603页
作者: Chun Liao Chong Feng Sen Yang He-Yan Huang Department of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing 100081 China
Opinion targets extraction of Chinese microblogs plays an important role in opinion mining. There has been a significant progress in this area recently, especially the method based on conditional random field (CRF).... 详细信息
来源: 评论
PSVM: a preference-enhanced SVM model using preference data for classification
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Science China(information Sciences) 2017年 第12期60卷 165-178页
作者: Lerong MA Dandan SONG Lejian LIAO Jingang WANG Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University Search Business Department Alibaba Group
Classification is an essential task in data mining, machine learning and pattern recognition *** classification models focus on distinctive samples from different categories. There are fine-grained differences between... 详细信息
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When Factorization Meets Heterogeneous Latent Topics: An Interpretable Cross-Site Recommendation Framework
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Journal of Computer Science & Technology 2015年 第4期30卷 917-932页
作者: 辛欣 林钦佑 魏骁驰 黄河燕 Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing School of Computer Science Beijing Institute of Technology Beijing 100081 China Microsoft Research Asia Beijing 100080 China
Data sparsity is a well-known challenge in recommender systems. Previous studies alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we solve this challen... 详细信息
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Improving Parallel Corpus Quality for Chinese-Vietnamese Statistical Machine Translation
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Journal of Beijing Institute of Technology 2018年 第1期27卷 127-136页
作者: Huu-anh Tran Yuhang Guo Ping Jian Shumin Shi Heyan Huang Department of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Application Beijing Institute of Technology
The performance of a machine translation system heavily depends on the quantity and quality of the bilingual language resource. However,getting a parallel corpus,which has a large scale and is of high quality,is a ver... 详细信息
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Incorporating target language semantic roles into a string-to-tree translation model
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Frontiers of information Technology & Electronic Engineering 2017年 第10期18卷 1534-1542页
作者: Chao SU Yu-hang GUO He-yan HUANG Shu-min SHI Chong FENG School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing 100081China Beijing Advanced Innovation Center for Imaging Technology Capital Normal UniversityBeijing 100048China
The string-to-tree model is one of the most successful syntax-based statistical machine translation(SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic... 详细信息
来源: 评论
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
PSST: A Benchmark for Evaluation-driven Text Public-Speaking...
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2024 Conference on Empirical Methods in Natural language processing, EMNLP 2024
作者: Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China
language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potent... 详细信息
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BIT at SemEval-2017 Task 1: Using Semantic information Space to Evaluate Semantic Textual Similarity  11
BIT at SemEval-2017 Task 1: Using Semantic Information Space...
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11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
作者: Wu, Hao Huang, Heyan Jian, Ping Guo, Yuhang Su, Chao Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science Beijing Institute of Technology Beijing China
This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task. One is an unsupervised system and the other two are supervised systems which simply employ the unsupervised ... 详细信息
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