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Neural Collective Entity Linking [article]

Yixin Cao and Lei Hou and Juanzi Li and Zhiyuan Liu
2018 arXiv   pre-print
NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking.  ...  To address this issue, we propose a novel neural model for collective entity linking, named as NCEL.  ...  Acknowledgments The work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key project (U1736204, 61661146007), and THUNUS NExT Co-Lab.  ... 
arXiv:1811.08603v1 fatcat:jrk2sjc6mva4vhvb62tjfz5e4u

Neural Entity Linking: A Survey of Models Based on Deep Learning [article]

Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann
2021 arXiv   pre-print
The vast variety of modifications of this general neural entity linking architecture are grouped by several common themes: joint entity recognition and linking, models for global linking, domain-independent  ...  Our goal is to systemize design features of neural entity linking systems and compare their performance to the prominent classic methods on common benchmarks.  ...  The work of Artem Shelmanov in the current study (preparation of sections related to application of entity linking to neural language models, entity ranking, contextmention encoding, and overall harmonization  ... 
arXiv:2006.00575v3 fatcat:ra3kwc4tmbfhlmgtlevkcshcqq

Deep Joint Entity Disambiguation with Local Neural Attention [article]

Octavian-Eugen Ganea, Thomas Hofmann
2017 arXiv   pre-print
Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation.  ...  We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations.  ...  Acknowledgments We thank Aurelien Lucchi, Marina Ganea, Jason Lee, Florian Schmidt and Hadi Daneshmand for their comments and suggestions.  ... 
arXiv:1704.04920v3 fatcat:l7ct3h5a4vf5tbpos5hzkijzge

Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution

Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
We release the data and code of model DArtNet for future research.  ...  We jointly train the model link prediction and attribute prediction.  ...  Acknowledgments This work is (partially) supported by the Office of Naval Research grant N00014-19-1-2308. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2020/382 dblp:conf/ijcai/GuoFZUK20 fatcat:tb6uey3tmnb2fo536rfbpdw77i

Deep Joint Entity Disambiguation with Local Neural Attention

Octavian-Eugen Ganea, Thomas Hofmann
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation.  ...  We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations.  ...  Acknowledgments We thank Aurelien Lucchi, Marina Ganea, Jason Lee, Florian Schmidt and Hadi Daneshmand for their comments and suggestions.  ... 
doi:10.18653/v1/d17-1277 dblp:conf/emnlp/GaneaH17 fatcat:lkpshcfanzhk5ikyoxjar7yngq

A Sequence Learning Method for Domain-Specific Entity Linking

Emrah Inan, Oguz Dikenelli
2018 Proceedings of the Seventh Named Entities Workshop  
Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graphbased approaches.  ...  First, we match easy mentionentity pairs and using the domain information of this pair to filter candidate entities of closer mentions.  ...  Entity linking via joint encoding of types, descriptions, and context. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2681-2690.  ... 
doi:10.18653/v1/w18-2403 dblp:conf/aclnews/InanD18 fatcat:jec5nd4nnrcmhbzn33oob7lk64

Joint Entity Linking for Web Tables with Hybrid Semantic Matching [chapter]

Jie Xie, Yuhai Lu, Cong Cao, Zhenzhen Li, Yangyang Guan, Yanbing Liu
2020 Lecture Notes in Computer Science  
This model captures local semantics of the mentions and entities from different semantic aspects, and then makes full use of the information of previously referred entities for the subsequent entity disambiguation  ...  In this paper, we propose a novel model JHSTabEL, which converts table entity linking into a sequence decision problem and uses hybrid semantic features to disambiguate the mentions in web tables.  ...  [5] proposed a neural network method for cross-language table entity linking. It took some embedding features as inputs and used a two-layer fully connected network to perform entity linking.  ... 
doi:10.1007/978-3-030-50417-5_46 fatcat:tluh7cwnibdptmh7zqkd4iytq4

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, Xing Xie
2019 The World Wide Web Conference on - WWW '19  
In this paper, we propose a neural approach for CNER. First, we introduce a CNN-LSTM-CRF neural architecture to capture both local and long-distance contexts for CNER.  ...  Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming.  ...  Chiu and Nichols [3] proposed to learn word-and character-level features using LSTM and CNN networks for English NER.  ... 
doi:10.1145/3308558.3313743 dblp:conf/www/WuLWHX19 fatcat:22gxznga7nfjre3zcisxkfouny

Aggregated Semantic Matching for Short Text Entity Linking

Feng Nie, Shuyan Zhou, Jing Liu, Jinpeng Wang, Chin-Yew Lin, Rong Pan
2018 Proceedings of the 22nd Conference on Computational Natural Language Learning  
between the local context and the candidate entity are captured via representationbased and interaction-based neural semantic matching models, and then two matching signals work jointly for disambiguation  ...  The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base. Entity linking in long text has been well studied in previous work.  ...  Acknowledgement We thank the anonymous reviewers for their helpful comments. We also thank Jin-Ge Yao, Zhirui Zhang, Shuangzhi Wu and Yin Lin for helpful conversations and comments on the work.  ... 
doi:10.18653/v1/k18-1046 dblp:conf/conll/NieZLWLP18 fatcat:62ouhf7herbh7h2ogom4v3dfoq

BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks [article]

Di Jin, Xiangchen Song, Zhizhi Yu, Ziyang Liu, Heling Zhang, Zhaomeng Cheng, Jiawei Han
2021 arXiv   pre-print
We first transform the original text-rich network into an augmented bi-typed heterogeneous network, capturing both the global node-level information and the local text-sequence information from texts.  ...  We propose BiTe-GCN, a novel GCN architecture with bidirectional convolution of both topology and features on text-rich networks to solve these limitations.  ...  Science Foundation IIS-19-56151, IIS-17-41317, IIS 17-04532, and IIS 16-18481, and DTRA HDTRA11810026.  ... 
arXiv:2010.12157v2 fatcat:fuuemdemu5bfvkoihykkiipbje

Bilinear joint learning of word and entity embeddings for Entity Linking

Hui Chen, Baogang Wei, Yonghuai Liu, Yiming Li, Jifang Yu, Wenhao Zhu
2018 Neurocomputing  
Entity Linking (EL) is the task of resolving mentions to referential entities in a knowledge base, which facilitates applications such as information retrieval, question answering, and knowledge base population  ...  We treat EL as a ranking problem, and utilize a pairwise learning-to-rank framework with features constructed with learned embeddings as well as conventional EL features.  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China  ... 
doi:10.1016/j.neucom.2017.11.064 fatcat:uaafptfy35gehfbicjkl34b56e

Review on Graph Feature Learning and Feature Extraction Techniques for Link Prediction [article]

Ece C. Mutlu, Toktam A. Oghaz, Amirarsalan Rajabi, Ivan Garibay
2020 arXiv   pre-print
models, and learning-based methods.  ...  Extensive studies have examined this problem from different aspects and proposed various methods, some of which might work very well for a specific application but not as a global solution.  ...  Link prediction in these applications have been mostly investigated through unsupervised graph representation and feature learning methods based on node (local) or path (global) similarity metrics that  ... 
arXiv:1901.03425v4 fatcat:o4mg2dopjrhe3kesmzfg3zegui

Boosting Collective Entity Linking via Type-Guided Semantic Embedding [chapter]

Weiming Lu, Yangfan Zhou, Haijiao Lu, Pengkun Ma, Zhenyu Zhang, Baogang Wei
2018 Lecture Notes in Computer Science  
We use Bidirectional Long Short-Term Memory (BiLSTM) and dynamic convolutional neural network (DCNN) to model the mention and the entity respectively.  ...  Then, we build a graph with the semantic relatedness of mentions and entities for the collective entity linking.  ...  LY17F020015), the Chinese Knowledge Center of Engineering Science and Technology (CKCEST), and the Fundamental Research Funds for the Central Universities (No. 2017FZA5016).  ... 
doi:10.1007/978-3-319-73618-1_45 fatcat:5blxsgtabrc4diwcqv6uvauqka

Joint Entity Linking with Deep Reinforcement Learning [article]

Zheng Fang, Yanan Cao, Dongjie Zhang, Qian Li, Zhenyu Zhang, Yanbing Liu
2019 arXiv   pre-print
Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models.  ...  To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective.  ...  Entity Linking Entity linking falls broadly into two major approaches: local and global disambiguation.  ... 
arXiv:1902.00330v1 fatcat:zk7nzkypuzhjxld3bcvx3ol4si

Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference [article]

Lu Liu, Zhenqiao Song, Xiaoqing Zheng
2020 arXiv   pre-print
Mentions are linked to each other via the edges modeling how likely two linked mentions point to the same entity.  ...  We propose a graph neural network-based coreference resolution method that can capture the entity-centric information by encouraging the sharing of features across all mentions that probably refer to the  ...  For the coreference resolution, mentions are linked to each other via the edges modeling how likely two linked mentions refer to the same entity.  ... 
arXiv:2009.04639v1 fatcat:vg2qnjyhafgrlm3or4eh6spv4y
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