检索结果-内蒙古大学图书馆

2019 Conference on Empirical Methods in Natural language processing and 9th International Joint Conference on Natural language processing, EMNLP-IJCNLP 2019

作者： Wenbo, Wang Yang, Gao Heyan, Huang Yuxiang, Zhou School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9781950737901

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework. © 2019 Association for Computational Linguistics

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Failure Handling of E-Business Services in an Extensible Constraint Service Model

Failure Handling of E-Business Services in an Extensible Con...

引用

2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Ruoyu Chen Lejian Liao Zhi Fang Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications Beijing Key Laboratory of Intelligent Information TechnologySchool of Computer ScienceBeijing Institute of Technology

ISBN: (纸本)9781467349994

Failure handling of business transactions is essential in E-Business *** paper extends the service process specification proposed in a contract-centered constraint-based service modelling framework with failure handling *** failure in process model,generalized failure such as QoS violation can be modelled in *** semantics for failure handling in E-Business services as well as policies are discussed via a state transition system.A virtual machine is built for contracting and executing of services under this framework.

关键词： Services service Failure Handling of E-Business Services in an Extensible Constraint Service Model

来源：评论

学校读者我要写书评

暂无评论

Temporal enhanced sentence-level attention model for hashtag recommendation

引用

CAAI Transactions on Intelligence Technology 2018年第2期3卷 95-100页

作者： Jun Ma Chong Feng Ge Shi Xuewen Shi Heyang Huang Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications College of Computer Science Beijing Institute of Technology University 5 South Zhong Guan Cun Street of Haidian District Beijing People's Republic of China

Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.

关键词：分类器数据集计算机技术智能技术

来源：评论

学校读者我要写书评

暂无评论

Incorporating Option and Out-of-domain Knowledge for Multi-choice Machine Reading Comprehension 7

Incorporating Option and Out-of-domain Knowledge for Multi-c...

引用

7th IEEE International Conference on cloud computing and Intelligence Systems, CCIS 2021

作者： Xu, Yuan Shi, Shumin Huang, Heyan Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9781665441490

Multi-choice Machine Reading Comprehension (MRC) requires the model to select the correct answer from a set of answer candidates given the corresponding passage and question. Previous studies mainly focus on complex matching networks to model the relationship among options, passage and question. However, these models obtain little improvement over the powerful Pre-trained language Models (PLMs). In this paper, we propose a simple method to incorporate option knowledge from PLMs and introduce out-of-domain knowledge by multi-task learning skillfully. Our approach obtains state-of-the-art results on Chinese multi-choice MRC dataset ReCO and also effectively improves the performance on C3. © 2021 IEEE.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Augmenting Context Representation with Triggers Knowledge for Relation Extraction 12th

Augmenting Context Representation with Triggers Knowledge fo...

引用

12th IFIP TC 12 International Conference on Intelligent information processing, IIP 2022

作者： Li, En Shi, Shumin Yang, Zhikun Huang, He Yan School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031039478

Relation Extraction (RE) requires the model to classify the correct relation from a set of relation candidates given the corresponding sentence and two entities. Recent work mainly studies how to utilize more data or incorporate extra context information especially with Pre-trained language Models (PLMs). However, these models still face with the challenges of avoiding being affected by irrelevant or misleading words. In this paper, we propose a novel model to help alleviate such deficiency. Specifically, our model automatically mines the triggers of the sentence iteratively with the sentence itself from the previous iteration, and augment the semantics of the context representation from BERT with both entity pair and triggers skillfully. We conduct extensive experiments to evaluate the proposed model and effectively obtain empirical improvement in TACRED. © 2022, IFIP International Federation for information processing.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

引用

13th IFIP TC 12 International Conference on Intelligent information processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for information processing 2024.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Deep Hierarchical Attention Flow for Visual Commonsense Reasoning 9th

Deep Hierarchical Attention Flow for Visual Commonsense Reas...

引用

9th CCF International Conference on Natural language processing and Chinese computing, NLPCC 2020

作者： Song, Yuansheng Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Bejing China

ISBN: (纸本)9783030604493

Visual Commonsense Reasoning (VCR) requires a thoroughly understanding general information connecting language and vision, as well as the background world knowledge. In this paper, we introduce a novel yet powerful deep hierarchical attention flow framework, which takes full advantage of text information in the query and candidate responses to perform reasoning over the image. Moreover, inspired by the success of machine reading comprehension, we also model the correlation among candidate responses to obtain better response representations. Extensive quantitative and qualitative experiments are conducted to evaluate the proposed model. Empirical results on the benchmark VCR1.0 show that the proposed model outperforms existing strong baselines, which demonstrates the effectiveness of our method. © 2020, Springer Nature Switzerland AG.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Identifying suspected cybermob on Tieba 15th

Identifying suspected cybermob on Tieba

引用

15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural language processing Based on Naturally Annotated Big Data, NLP-NABD 2016

作者： Shi, Shumin Zhou, Xinyu Zhao, Meng Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783319476735

This paper describes an approach to identify suspected cybermob on social media. Many researches involve making predictions of group emotion on Internet (such as quantifying sentiment polarity), but this paper instead focuses on the origin of information diffusion, namely back to its makers and contributors. According our previous findings that have shown, at the level of Tieba’s contents, the negative information or emotions spread faster than positive ones, we centre on the maker of negative message in this paper, so-called cybermobs who post aggressive, provocative or insulting remarks on social websites. We explore the different characteristics between suspected cybermobs and general netizens and then extract relative unique features of suspected cybermobs. We construct real system to identify suspected cybermob automatically using machine learning method with above features, including other common features like user/content-based ones. Empirical results show that our approach can detect suspected cybermob correctly and efficiently as we evaluate it with benchmark models, and apply it to actual cases. © Springer International Publishing AG 2016.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method 20

Reducing Length Bias in Scoring Neural Machine Translation v...

引用

20th China National Conference on Computational Linguistics, CCL 2021

作者： Shi, Xuewen Huang, Heyan Jian, Ping Tang, Yi-Kun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing100081 China

ISBN: (纸本)9783030841850

Neural machine translation (NMT) usually employs beam search to expand the searching space and obtain more translation candidates. However, the increase of the beam size often suffers from plenty of short translations, resulting in dramatical decrease in translation quality. In this paper, we handle the length bias problem through a perspective of causal inference. Specifically, we regard the model generated translation score S as a degraded true translation quality affected by some noise, and one of the confounders is the translation length. We apply a Half-Sibling Regression method to remove the length effect on S, and then we can obtain a debiased translation score without length information. The proposed method is model agnostic and unsupervised, which is adaptive to any NMT model and test dataset. We conduct the experiments on three translation tasks with different scales of datasets. Experimental results and further analyses show that our approaches gain comparable performance with the empirical baseline methods. © 2021, Springer Nature Switzerland AG.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

Exploiting Knowledge Embedding to Improve the Description for Image Captioning 5th

Exploiting Knowledge Embedding to Improve the Description fo...

引用

5th China Conference on Knowledge Graph, and Semantic computing, CCKS 2020

作者： Song, Dandan Peng, Cuimei Yang, Huan Liao, Lejian Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789811619632

Most existing methods for image captioning are based on the encoder-decoder framework which directly translates visual features into sentences, without exploiting commonsense knowledge available in the form of knowledge graph. Inspired by the success of information retrieval and question answering systems that leverage prior knowledge, we explore a knowledge embedding approach for image captioning. In this paper, we propose a Knowledge Embedding with Attention on Attention (KE-AoA) method for image captioning, which judges whether or how well the objects are related and augments semantic correlations and constraints between them. The KE-AoA method combines knowledge base method (TransE) and text method (Skip-gram), adding external knowledge graph information (triplets) into the language model to guide the learning of word vectors as the regularization term. Then it employs the AoA module to model the relations among different objects. As more inherent relations and commonsense knowledge are learned, the model can generate better image descriptions. The experiments on MSCOCO data sets achieve a significant improvement on the existing methods and validate the effectiveness of our prior knowledge-based approach. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：