Search

Your search keyword '"Xu, Jinan"' showing total 341 results
Start Over Author "Xu, Jinan"
341 results on '"Xu, Jinan"'

51. Faster Depth-Adaptive Transformers

Author

Liu, Yijin, Meng, Fandong, Zhou, Jie, Chen, Yufeng, and Xu, Jinan

Subjects
Computer Science - Computation and Language
Abstract

Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency. The main challenge is how to measure such hardness and decide the required depths (i.e., layers) to conduct. Previous works generally build a halting unit to decide whether the computation should continue or stop at each layer. As there is no specific supervision of depth selection, the halting unit may be under-optimized and inaccurate, which results in suboptimal and unstable performance when modeling sentences. In this paper, we get rid of the halting unit and estimate the required depths in advance, which yields a faster depth-adaptive model. Specifically, two approaches are proposed to explicitly measure the hardness of input words and estimate corresponding adaptive depth, namely 1) mutual information (MI) based estimation and 2) reconstruction loss based estimation. We conduct experiments on the text classification task with 24 datasets in various sizes and domains. Results confirm that our approaches can speed up the vanilla Transformer (up to 7x) while preserving high accuracy. Moreover, efficiency and robustness are significantly improved when compared with other depth-adaptive approaches., Comment: AAAI-2021. Code will appear at: https://github.com/Adaxry/Adaptive-Transformer

Published
2020

52. An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis

Author

Liang, Yunlong, Meng, Fandong, Zhang, Jinchao, Chen, Yufeng, Xu, Jinan, and Zhou, Jie

Subjects
Computer Science - Computation and Language
Abstract

Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner. However, previous approaches do not well exploit the interactive relations among three subtasks and do not pertinently leverage the easily available document-level labeled domain/sentiment knowledge, which restricts their performances. To address these issues, we propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. For one thing, through the interactive correlations between the ABSA subtasks, our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm, that is, any two of the three subtasks will help the third one. For another, our IMKTN pertinently transfers the document-level knowledge, i.e., domain-specific and sentiment-related knowledge, to the aspect-level subtasks to further enhance the corresponding performance. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach., Comment: Accepted to Findings of EMNLP 2021. arXiv admin note: substantial text overlap with: arXiv:2004.01935

Published
2020

53. A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis

Author

Liang, Yunlong, Meng, Fandong, Zhang, Jinchao, Xu, Jinan, Chen, Yufeng, and Zhou, Jie

Subjects
Computer Science - Computation and Language
Abstract

The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation.In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled. In this paper, we thus propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn). Additionally, we design a simple yet effective message-passing mechanism to ensure that our model learns from multiple related tasks in a multi-task learning framework. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach, which significantly outperforms existing state-of-the-art methods. Besides, we achieve further improvements by using BERT as an additional feature extractor., Comment: Code: https://github.com/XL2248/DREGCN

Published
2020

54. Depth-Adaptive Graph Recurrent Network for Text Classification

Author

Liu, Yijin, Meng, Fandong, Chen, Yufeng, Xu, Jinan, and Zhou, Jie

Subjects
Computer Science - Computation and Language
Abstract

The Sentence-State LSTM (S-LSTM) is a powerful and high efficient graph recurrent network, which views words as nodes and performs layer-wise recurrent steps between them simultaneously. Despite its successes on text representations, the S-LSTM still suffers from two drawbacks. Firstly, given a sentence, certain words are usually more ambiguous than others, and thus more computation steps need to be taken for these difficult words and vice versa. However, the S-LSTM takes fixed computation steps for all words, irrespective of their hardness. The secondary one comes from the lack of sequential information (e.g., word order) that is inherently important for natural language. In this paper, we try to address these issues and propose a depth-adaptive mechanism for the S-LSTM, which allows the model to learn how many computational steps to conduct for different words as required. In addition, we integrate an extra RNN layer to inject sequential information, which also serves as an input feature for the decision of adaptive depths. Results on the classic text classification task (24 datasets in various sizes and domains) show that our model brings significant improvements against the conventional S-LSTM and other high-performance models (e.g., the Transformer), meanwhile achieving a good accuracy-speed trade off., Comment: Code at: https://github.com/Adaxry/Depth-Adaptive-GRN

Published
2020

56. Improving Zero-Shot Cross-Lingual Dialogue State Tracking via Contrastive Learning

Author

Xiang, Yu, Zhang, Ting, Di, Hui, Huang, Hui, Li, Chunyou, Ouchi, Kazushige, Chen, Yufeng, Xu, Jinan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Qin, Bing, editor, Qiu, Xipeng, editor, Jing, Jiang, editor, Han, Xianpei, editor, Rao, Gaoqi, editor, and Chen, Yubo, editor

Published
2023
Full Text
View/download PDF

57. CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding

Author

Liu, Yijin, Meng, Fandong, Zhang, Jinchao, Zhou, Jie, Chen, Yufeng, and Xu, Jinan

Subjects
Computer Science - Computation and Language
Abstract

Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing models fail to fully utilize co-occurrence relations between slots and intents, which restricts their potential performance. To address this issue, in this paper we propose a novel Collaborative Memory Network (CM-Net) based on the well-designed block, named CM-block. The CM-block firstly captures slot-specific and intent-specific features from memories in a collaborative manner, and then uses these enriched features to enhance local context representations, based on which the sequential information flow leads to more specific (slot and intent) global utterance representations. Through stacking multiple CM-blocks, our CM-Net is able to alternately perform information exchange among specific memories, local contexts and the global utterance, and thus incrementally enriches each other. We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a self-collected corpus (CAIS). Experimental results show that the CM-Net achieves the state-of-the-art results on the ATIS and SNIPS in most of criteria, and significantly outperforms the baseline models on the CAIS. Additionally, we make the CAIS dataset publicly available for the research community., Comment: Accepted as a long paper at EMNLP 2019. Code and data is available at: https://github.com/Adaxry/CM-Net

Published
2019

58. A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

Author

Liang, Yunlong, Meng, Fandong, Zhang, Jinchao, Xu, Jinan, Chen, Yufeng, and Zhou, Jie

Subjects
Computer Science - Computation and Language
Abstract

Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation. In this paper, we propose a novel Aspect-Guided Deep Transition model, named AGDT, which utilizes the given aspect to guide the sentence encoding from scratch with the specially-designed deep transition architecture. Furthermore, an aspect-oriented objective is designed to enforce AGDT to reconstruct the given aspect with the generated sentence representation. In doing so, our AGDT can accurately generate aspect-specific sentence representation, and thus conduct more accurate sentiment predictions. Experimental results on multiple SemEval datasets demonstrate the effectiveness of our proposed approach, which significantly outperforms the best reported results with the same setting., Comment: Accepted at EMNLP 2019 as a long paper. Code is available at https://github.com/XL2248/AGDT

Published
2019

59. GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling

Author

Liu, Yijin, Meng, Fandong, Zhang, Jinchao, Xu, Jinan, Chen, Yufeng, and Zhou, Jie

Subjects
Computer Science - Computation and Language
Abstract

Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs). However, the shallow connections between consecutive hidden states of RNNs and insufficient modeling of global information restrict the potential performance of those models. In this paper, we try to address these issues, and thus propose a Global Context enhanced Deep Transition architecture for sequence labeling named GCDT. We deepen the state transition path at each position in a sentence, and further assign every token with a global representation learned from the entire sentence. Experiments on two standard sequence labeling tasks show that, given only training data and the ubiquitous word embeddings (Glove), our GCDT achieves 91.96 F1 on the CoNLL03 NER task and 95.43 F1 on the CoNLL2000 Chunking task, which outperforms the best reported results under the same settings. Furthermore, by leveraging BERT as an additional resource, we establish new state-of-the-art results with 93.47 F1 on NER and 97.30 F1 on Chunking., Comment: Accepted as a long paper at ACL 2019. Code is available at: https://github.com/Adaxry/GCDT

Published
2019

60. Supervised Contrastive Learning for Cross-Lingual Transfer Learning

Author

Wang, Shuaibo, Di, Hui, Huang, Hui, Lai, Siyu, Ouchi, Kazushige, Chen, Yufeng, Xu, Jinan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Liu, Yang, editor, Che, Wanxiang, editor, Feng, Yang, editor, Qiu, Xipeng, editor, Rao, Gaoqi, editor, and Chen, Yubo, editor

Published
2022
Full Text
View/download PDF

61. Towards Making the Most of Pre-trained Translation Model for Quality Estimation

Author

Li, Chunyou, Di, Hui, Huang, Hui, Ouchi, Kazushige, Chen, Yufeng, Liu, Jian, Xu, Jinan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Liu, Yang, editor, Che, Wanxiang, editor, Feng, Yang, editor, Qiu, Xipeng, editor, Rao, Gaoqi, editor, and Chen, Yubo, editor

Published
2022
Full Text
View/download PDF

67. Explore Coarse-Grained Structures for Syntactically Controllable Paraphrase Generation

Author

Yang, Erguang, Liu, Mingtong, Xiong, Deyi, Zhang, Yujie, Meng, Yao, Hu, Changjian, Xu, Jinan, Chen, Yufeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lu, editor, Feng, Yansong, editor, Hong, Yu, editor, and He, Ruifang, editor

Published
2021
Full Text
View/download PDF

68. Contrastive Learning for Machine Translation Quality Estimation

Author

Huang, Hui, Di, Hui, Liu, Jian, Chen, Yufeng, Ouchi, Kazushige, Xu, Jinan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lu, editor, Feng, Yansong, editor, Hong, Yu, editor, and He, Ruifang, editor

Published
2021
Full Text
View/download PDF

69. Improving Entity Linking by Encoding Type Information into Entity Embeddings

Author

Li, Tianran, Yang, Erguang, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Sheng, editor, Sun, Maosong, editor, Liu, Yang, editor, Wu, Hua, editor, Kang, Liu, editor, Che, Wanxiang, editor, He, Shizhu, editor, and Rao, Gaoqi, editor

Published
2021
Full Text
View/download PDF

70. Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising

Author

Zhu, Wenjing, Liu, Jian, Xu, Jinan, Chen, Yufeng, Zhang, Yujie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Sheng, editor, Sun, Maosong, editor, Liu, Yang, editor, Wu, Hua, editor, Kang, Liu, editor, Che, Wanxiang, editor, He, Shizhu, editor, and Rao, Gaoqi, editor

Published
2021
Full Text
View/download PDF

71. LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering

Author

Wan, Zhang, Chen, Keming, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Sheng, editor, Sun, Maosong, editor, Liu, Yang, editor, Wu, Hua, editor, Kang, Liu, editor, Che, Wanxiang, editor, He, Shizhu, editor, and Rao, Gaoqi, editor

Published
2021
Full Text
View/download PDF

73. A Joint Model for Graph-Based Chinese Dependency Parsing

Author

Li, Xingchen, Liu, Mingtong, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Li, Sujian, editor, Zhang, Yue, editor, Liu, Yang, editor, He, Shizhu, editor, and Rao, Gaoqi, editor

Published
2020
Full Text
View/download PDF

76. Cabin Placement Layout Optimisation Based on Systematic Layout Planning and Genetic Algorithm

Author

Su Shaojuan, Zheng Yasai, Xu Jinan, and Wang Tianlin

Subjects
cabin placement layout, systematic layout planning, genetic algorithm, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

Cabin placement layout is an important part of ship cabin layout design. A good cabin placement layout can improve the efficiency of the ship’s cabin arrangement. However, optimisation of the layout of cabin placement is not widely studied and more often relies on the experience of the staff. Thus, a novel methodology combining systematic layout planning and a genetic algorithm to optimise the cabin placement is presented in this paper. First key elements are converted by a systematic planning method that is often applied in factory layout, and a preliminary cabin placement layout model is established according to these key elements. Then the circulation strength and adjacency strength are taken as sub-objectives to establish a mathematical model, and an improved genetic algorithm is used to optimise the model. The result of the optimisation is compared with the initial schemes to verify the validity of the algorithm. Finally, the human factors are introduced according to the actual situation. The AHP method is used to select the layout scheme of the cabin that is most likely to be applied in the actual cabin layout.

Published
2020
Full Text
View/download PDF

77. Outdated Issue Aware Decoding for Factual Knowledge Editing

Author

Sun, Zengkui, Liu, Yijin, Wang, Jiaan, Meng, Fandong, Xu, Jinan, Chen, Yufeng, Zhou, Jie, Sun, Zengkui, Liu, Yijin, Wang, Jiaan, Meng, Fandong, Xu, Jinan, Chen, Yufeng, and Zhou, Jie

Abstract

Recently, Knowledge Editing has received increasing attention, since it could update the specific knowledge from outdated ones in pretrained models without re-training. However, as pointed out by recent studies, existing related methods tend to merely memorize the superficial word composition of the edited knowledge, rather than truly learning and absorbing it. Consequently, on the reasoning questions, we discover that existing methods struggle to utilize the edited knowledge to reason the new answer, and tend to retain outdated responses, which are generated by the original models utilizing original knowledge. Nevertheless, the outdated responses are unexpected for the correct answers to reasoning questions, which we named as the outdated issue. To alleviate this issue, in this paper, we propose a simple yet effective decoding strategy, i.e., outDated ISsue aware deCOding (DISCO), to enhance the performance of edited models on reasoning questions. Specifically, we capture the difference in the probability distribution between the original and edited models. Further, we amplify the difference of the token prediction in the edited model to alleviate the outdated issue, and thus enhance the model performance w.r.t the edited knowledge. Experimental results suggest that applying DISCO could enhance edited models to reason, e.g., on reasoning questions, DISCO outperforms the prior SOTA method by 12.99 F1 scores, and reduces the ratio of the outdated issue to 5.78% on the zsRE dataset., Comment: ACL2024 Findings, Codes are at https://github.com/Acerkoo/DISCO

Published
2024

79. Improving Performance of NMT Using Semantic Concept of WordNet Synset

Author

Liu, Fangxu, Xu, JinAn, Miao, Gouyi, Chen, Yufeng, Zhang, Yujie, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Chen, Jiajun, editor, and Zhang, Jiajun, editor

Published
2019
Full Text
View/download PDF

80. The Impact of Named Entity Translation for Neural Machine Translation

Author

Yan, Jinghui, Zhang, Jiajun, Xu, JinAn, Zong, Chengqing, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Chen, Jiajun, editor, and Zhang, Jiajun, editor

Published
2019
Full Text
View/download PDF

81. Attention-Based Gated Convolutional Neural Networks for Distant Supervised Relation Extraction

Author

Li, Xingya, Chen, Yufeng, Xu, Jinan, Zhang, Yujie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Huang, Xuanjing, editor, Ji, Heng, editor, Liu, Zhiyuan, editor, and Liu, Yang, editor

Published
2019
Full Text
View/download PDF

82. Syntax-Aware Attention for Natural Language Inference with Phrase-Level Matching

Author

Liu, Mingtong, Wang, Yasong, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Huang, Xuanjing, editor, Ji, Heng, editor, Liu, Zhiyuan, editor, and Liu, Yang, editor

Published
2019
Full Text
View/download PDF

83. A Top-Down Model for Character-Level Chinese Dependency Parsing

Author

Chen, Yuanmeng, Liu, Hang, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Huang, Xuanjing, editor, Ji, Heng, editor, Liu, Zhiyuan, editor, and Liu, Yang, editor

Published
2019
Full Text
View/download PDF

84. A Sequence-to-Action Architecture for Character-Based Chinese Dependency Parsing with Status History

Author

Liu, Hang, Zhang, Yujie, Chen, Meng, Xu, Jinan, Chen, Yufeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
Full Text
View/download PDF

85. Improved Quality Estimation of Machine Translation with Pre-trained Language Representation

Author

Miao, Guoyi, Di, Hui, Xu, Jinan, Yang, Zhongcheng, Chen, Yufeng, Ouchi, Kazushige, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
Full Text
View/download PDF

90. Improved Character-Based Chinese Dependency Parsing by Using Stack-Tree LSTM

Author

Liu, Hang, Liu, Mingtong, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Zhang, Min, editor, Ng, Vincent, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2018
Full Text
View/download PDF

91. A Semantic Concept Based Unknown Words Processing Method in Neural Machine Translation

Author

Li, Shaotong, Xu, Jinan, Miao, Guoyi, Zhang, Yujie, Chen, Yufeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Xuanjing, editor, Jiang, Jing, editor, Zhao, Dongyan, editor, Feng, Yansong, editor, and Hong, Yu, editor

Published
2018
Full Text
View/download PDF

92. Attention-Based Convolutional Neural Networks for Chinese Relation Extraction

Author

Wu, Wenya, Chen, Yufeng, Xu, Jinan, Zhang, Yujie, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sun, Maosong, editor, Liu, Ting, editor, Wang, Xiaojie, editor, Liu, Zhiyuan, editor, and Liu, Yang, editor

Published
2018
Full Text
View/download PDF

93. Addressing Domain Adaptation for Chinese Word Segmentation with Instances-Based Transfer Learning

Author

Zhang, Yanna, Xu, Jinan, Miao, Guoyi, Chen, Yufeng, Zhang, Yujie, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sun, Maosong, editor, Liu, Ting, editor, Wang, Xiaojie, editor, Liu, Zhiyuan, editor, and Liu, Yang, editor

Published
2018
Full Text
View/download PDF

94. An Unknown Word Processing Method in NMT by Integrating Syntactic Structure and Semantic Concept

Author

Miao, Guoyi, Xu, Jinan, Li, Yancui, Li, Shaotong, Chen, Yufeng, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Wong, Derek F., editor, and Xiong, Deyi, editor

Published
2017
Full Text
View/download PDF

95. A Method of Unknown Words Processing for Neural Machine Translation Using HowNet

Author

Li, Shaotong, Xu, JinAn, Zhang, Yujie, Chen, Yufeng, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Wong, Derek F., editor, and Xiong, Deyi, editor

Published
2017
Full Text
View/download PDF

98. Chinese Paraphrases Acquisition Based on Random Walk N Step

Author

Ma, Jun, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lin, Chin-Yew, editor, Xue, Nianwen, editor, Zhao, Dongyan, editor, Huang, Xuanjing, editor, and Feng, Yansong, editor

Published
2016
Full Text
View/download PDF

99. Iterative Integration of Unsupervised Features for Chinese Dependency Parsing

Author

Luo, Te, Zhang, Yujie, Xu, Jinan, Chen, Yufeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lin, Chin-Yew, editor, Xue, Nianwen, editor, Zhao, Dongyan, editor, Huang, Xuanjing, editor, and Feng, Yansong, editor

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results