ABSTRACT
Entity Linking (EL) is a task for mapping mentions in text to corresponding entities in knowledge base (KB). This task usually includes candidate generation (CG) and entity disambiguation (ED) stages. Recent EL systems based on neural network models have achieved good performance, but they still face two challenges: (i) Previous studies evaluate their models without considering the differences between candidate entities. In fact, the quality (gold recall in particular) of candidate sets has an effect on the EL results. So, how to promote the quality of candidates needs more attention. (ii) In order to utilize the topical coherence among the referred entities, many graph and sequence models are proposed for collective ED. However, graph-based models treat all candidate entities equally which may introduce much noise information. On the contrary, sequence models can only observe previous referred entities, ignoring the relevance between the current mention and its subsequent entities. To address the first problem, we propose a multi-strategy based CG method to generate high recall candidate sets. For the second problem, we design a Sequential Graph Attention Network (SeqGAT) which combines the advantages of graph and sequence methods. In our model, mentions are dealt with in a sequence manner. Given the current mention, SeqGAT dynamically encodes both its previous referred entities and subsequent ones, and assign different importance to these entities. In this way, it not only makes full use of the topical consistency, but also reduce noise interference. We conduct experiments on different types of datasets and compare our method with previous EL system on the open evaluation platform. The comparison results show that our model achieves significant improvements over the state-of-the-art methods.
- Yixin Cao, Lei Hou, Juanzi Li, and Zhiyuan Liu. 2018. Neural Collective Entity Linking. In Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018. 675–686.Google Scholar
- Lihan Chen, Jiaqing Liang, Chenhao Xie, and Yanghua Xiao. 2018. Short Text Entity Linking with Fine-grained Topics. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018. 457–466.Google ScholarDigital Library
- Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. 785–794.Google ScholarDigital Library
- Zheng Chen and Suzanne Tamang etc.2010. CUNY-BLENDER TAC-KBP2010 Entity Linking and Slot Filling System Description. In Proceedings of the Third Text Analysis Conference, TAC 2010, Gaithersburg, Maryland, USA, November 15-16, 2010.Google Scholar
- Marco Cornolti, Paolo Ferragina, and Massimiliano Ciaramita. 2013. A framework for benchmarking entity-annotation systems. In 22nd International World Wide Web Conference, WWW ’13, Rio de Janeiro, Brazil, May 13-17, 2013. 249–260.Google ScholarDigital Library
- Silviu Cucerzan. 2007. Large-Scale Named Entity Disambiguation Based on Wikipedia Data. In EMNLP-CoNLL 2007, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 28-30, 2007. 708–716.Google Scholar
- Mark Dredze, Paul McNamee, Delip Rao, Adam Gerber, and Tim Finin. 2010. Entity Disambiguation for Knowledge Base Population. In COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference, 23-27 August 2010, Beijing, China. 277–285.Google ScholarDigital Library
- Zheng Fang, Yanan Cao, Qian Li, Dongjie Zhang, Zhenyu Zhang, and Yanbing Liu. 2019. Joint Entity Linking with Deep Reinforcement Learning. In The World Wide Web Conference, WWW 2019. 438–447.Google Scholar
- Octavian-Eugen Ganea, Marina Ganea, Aurélien Lucchi, Carsten Eickhoff, and Thomas Hofmann. 2016. Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. In Proceedings of the 25th International Conference on World Wide Web, WWW 2016. 927–938.Google ScholarDigital Library
- Octavian-Eugen Ganea and Thomas Hofmann. 2017. Deep Joint Entity Disambiguation with Local Neural Attention. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017. 2619–2629.Google ScholarCross Ref
- Amir Globerson, Nevena Lazic, Soumen Chakrabarti, Amarnag Subramanya, Michael Ringgaard, and Fernando Pereira. 2016. Collective Entity Resolution with Multi-Focal Attention. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016.Google ScholarCross Ref
- Zhaochen Guo and Denilson Barbosa. 2018. Robust named entity disambiguation with random walks. Semantic Web 9, 4 (2018), 459–479.Google ScholarDigital Library
- Johannes Hoffart and Mohamed Amir Yosef etc.2011. Robust Disambiguation of Named Entities in Text. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011. 782–792.Google Scholar
- Matthew A Jaro. 1995. Probabilistic linkage of large public health data files. Statistics in medicine 14, 5-7 (1995), 491–498.Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017.Google Scholar
- Nikolaos Kolitsas, Octavian-Eugen Ganea, and Thomas Hofmann. 2018. End-to-End Neural Entity Linking. In Proceedings of the 22nd Conference on Computational Natural Language Learning, CoNLL 2018. 519–529.Google ScholarCross Ref
- Phong Le and Ivan Titov. 2018. Improving Entity Linking by Modeling Latent Relations between Mentions. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018. 1595–1604.Google ScholarCross Ref
- Phong Le and Ivan Titov. 2019. Boosting Entity Linking Performance by Leveraging Unlabeled Documents. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019. 1935–1945.Google ScholarCross Ref
- Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, Jacob Devlin, and Honglak Lee. 2019. Zero-Shot Entity Linking by Reading Entity Descriptions. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019. 3449–3460.Google ScholarCross Ref
- George A. Miller. 1995. WordNet: A Lexical Database for English. Commun. ACM 38, 11 (1995), 39–41.Google ScholarDigital Library
- David N. Milne and Ian H. Witten. 2008. Learning to link with wikipedia. In Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008. 509–518.Google Scholar
- Andrea Moro, Alessandro Raganato, and Roberto Navigli. 2014. Entity Linking meets Word Sense Disambiguation: a Unified Approach. TACL 2(2014), 231–244.Google ScholarCross Ref
- Thien Huu Nguyen, Nicolas R. Fauceglia, Mariano Rodriguez-Muro, Oktie Hassanzadeh, Alfio Massimiliano Gliozzo, and Mohammad Sadoghi. 2016. Joint Learning of Local and Global Features for Entity Linking via Neural Networks. In COLING 2016. 2310–2320.Google Scholar
- Feng Nie, Yunbo Cao, Jinpeng Wang, Chin-Yew Lin, and Rong Pan. 2018. Mention and Entity Description Co-Attention for Entity Disambiguation. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18). 5908–5915.Google Scholar
- Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, and Chenliang Li. 2017. NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017. 1667–1676.Google ScholarDigital Library
- Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, and Chenliang Li. 2019. Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All. IEEE Trans. Knowl. Data Eng. 31, 7 (2019), 1383–1396.Google ScholarCross Ref
- Francesco Piccinno and Paolo Ferragina. 2014. From TagME to WAT: a new entity annotator. In ERD’14, Proceedings of the First ACM International Workshop on Entity Recognition & Disambiguation, July 11, 2014. 55–62.Google ScholarDigital Library
- Francesco Piccinno and Paolo Ferragina. 2014. From TagME to WAT: a new entity annotator. In Proceedings of the first international workshop on Entity recognition & disambiguation. ACM, 55–62.Google ScholarDigital Library
- Chenwei Ran, Wei Shen, and Jianyong Wang. 2018. An Attention Factor Graph Model for Tweet Entity Linking. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23-27, 2018. 1135–1144.Google ScholarDigital Library
- Lev-Arie Ratinov, Dan Roth, Doug Downey, and Mike Anderson. 2011. Local and Global Algorithms for Disambiguation to Wikipedia. In The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19-24 June, 2011. 1375–1384.Google ScholarDigital Library
- Wei Shen, Jianyong Wang, and Jiawei Han. 2014. Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering 27, 2(2014), 443–460.Google ScholarCross Ref
- Wei Shen, Jianyong Wang, and Jiawei Han. 2015. Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions. IEEE Trans. Knowl. Data Eng. 27, 2 (2015), 443–460.Google ScholarCross Ref
- Wei Shen, Jianyong Wang, Ping Luo, and Min Wang. 2012. LINDEN: linking named entities with knowledge base via semantic knowledge. In Proceedings of the 21st World Wide Web Conference 2012, WWW 2012. 449–458.Google ScholarDigital Library
- Valentin I. Spitkovsky and Angel X. Chang. 2012. A Cross-Lingual Dictionary for English Wikipedia Concepts. In Proceedings of the Eighth International Conference on Language Resources and Evaluation, LREC 2012. 3168–3175.Google Scholar
- Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web, WWW 2007. 697–706.Google ScholarDigital Library
- Chuanqi Tan, Furu Wei, Pengjie Ren, Weifeng Lv, and Ming Zhou. 2017. Entity Linking for Queries by Searching Wikipedia Sentences. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017. 68–77.Google ScholarCross Ref
- Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. 2006. Fast Random Walk with Restart and Its Applications. In Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong, China. 613–622.Google ScholarDigital Library
- Ricardo Usbeck and Michael Röder etc.2015. GERBIL: General Entity Annotator Benchmarking Framework. In Proceedings of the 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, May 18-22, 2015. 1133–1143.Google ScholarDigital Library
- Vasudeva Varma and Praveen Bysani etc.2010. IIIT Hyderabad in Guided Summarization and Knowledge Base Population. In Proceedings of the Third Text Analysis Conference, TAC 2010.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017. 5998–6008.Google ScholarDigital Library
- Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018,.Google Scholar
- Mengge Xue, Weiming Cai, Jinsong Su, Linfeng Song, Yubin Ge, Yubao Liu, and Bin Wang. 2019. Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019. 5327–5333.Google ScholarCross Ref
- Lei Zhang and Achim Rettinger. 2014. X-LiSA: Cross-lingual Semantic Annotation. PVLDB 7, 13 (2014), 1693–1696.Google ScholarDigital Library
Index Terms
- High Quality Candidate Generation and Sequential Graph Attention Network for Entity Linking
Recommendations
Entity linking by focusing DBpedia candidate entities
ERD '14: Proceedings of the first international workshop on Entity recognition & disambiguationRecently, Entity Linking and Retrieval turned out to be one of the most interesting tasks in Information Extraction due to its various applications. Entity Linking (EL) is the task of detecting mentioned entities in a text and linking them to the ...
Recency-based candidate selection for efficient entity linking
iiWAS '17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & ServicesEntity Linking is the task of assigning a corresponding entity in a knowledge base to each mention in text. Typically, entity linking is a 3-step process: (1) candidate selection, (2) candidate evaluation, and (3) linking decision. Many existing methods ...
Re-ranking for joint named-entity recognition and linking
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge ManagementRecognizing names and linking them to structured data is a fundamental task in text analysis. Existing approaches typically perform these two steps using a pipeline architecture: they use a Named-Entity Recognition (NER) system to find the boundaries of ...
Comments