A Route-aware Model for Entity Recognition with Diverse Structures
Authors: 
Shukai Liao, Bin Dong, Yuan DongAuthors Info & Claims
Shukai Liao, Bin Dong, Yuan DongAuthors Info & ClaimsPages 372 - 378
Abstract
Summary: Discontinuous entities are widely existing in actual texts. However, most named entity recognition (NER) systems deal only with the flat entities and ignore others. Some NER models claim to recognize these entities but suffer from some level of ambiguity. To address this issue, we propose a novel route-aware model to unambiguously extract entities with all kinds of structures. We identify the span of all the entities and the adjacent matrix of the sentence respectively and decode all the routes within the extracted span. Experiments show that our route-aware model achieves 40.2 F1 scores in CADEC dataset and 36.8 in DDI dataset on NER with complex structures and comparable performance on NER with all the entities.
References
[1]
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2 (ACL '09). Association for Computational Linguistics, USA, 1003–1011.
[2]
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014. Relation classification via convolutional deep neural network. In Proceedings of the International Conference on Computational Linguistics, Dublin, Ireland, 2335–2344.
[3]
Nut Limsopatham and Nigel Collier. 2016. Normalising medical concepts in social media texts by learning semantic representation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 1014–1023.
[4]
Nitish Gupta, Sameer Singh, and Dan Roth. 2017. Entity linking via joint encoding of types, descriptions, and context. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, 2671–2680.
[5]
Pavlina Fragkou. 2017. Applying named entity recognition and co-reference resolution for segmenting english texts. In Proceedings of the Artificial Intelligence, 325–346.
[6]
Wee Meng Soon, Hwee Tou Ng, and Daniel Chung Yong Lim. 2001. A machine learning approach to coreference resolution of noun phrases. Comput. Linguist. 27, 4 (December 2001), 521–544.
[7]
George R Doddington, Alexis Mitchell, Mark A Przybocki, Lance A Ramshaw, Stephanie Strassel, and Ralph M Weischedel. 2004. The automatic content extraction (ace) program-tasks, data, and evaluation. In Proceedings of LREC.
[8]
Beatrice Alex, Barry Haddow, and Claire Grover. 2007. Recognising nested named entities in biomedical text. In Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing (BioNLP '07). Association for Computational Linguistics, USA, 65–72.
[9]
Bailin Wang, Wei Lu, Yu Wang, and Hongxia Jin. 2018. A neural transition-based model for nested mention recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 1011–1017.
[10]
Lin, Hongyu, Yaojie Lu, Xianpei Han, and Le Sun. 2019. Sequence-to-nuggets: Nested entity mention detection via anchor-region networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 5182-5192
[11]
Fisher, Joseph, and Andreas Vlachos. 2019. Merge and Label: A Novel Neural Network Architecture for Nested NER. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 5840-5850.
[12]
Alejandro Metke-Jimenez and Sarvnaz Karimi. 2016. Concept identification and normalisation for adverse drug event discovery in medical forums. Proceedings of the BMDID@ISWC, Kobe, Japan.
[13]
Lu, Wei, and Dan Roth. 2015. Joint mention extraction and classification with mention hypergraphs. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 857-867.
[14]
Aldrian Obaja Muis and Wei Lu. 2016. Learning to recognize discontiguous entities. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, 75–84.
[15]
Bailin Wang and Wei Lu. 2019. Combining spans into entities: A neural two-stage approach for recognizing discontiguous entities. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, China, pages 6215–6223.
[16]
Dai, Xiang, Sarvnaz Karimi, Ben Hachey, and Cecile Paris. 2020. An Effective Transition-based Model for Discontinuous NER. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5860-5870.
[17]
Timothy Dozat and Christopher Manning. 2017. Deep biaffine attention for neural dependency parsing. In Proceedings of 5th International Conference on Learning Representations (ICLR).
[18]
Kenton, Jacob Devlin Ming-Wei Chang, and Lee Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT, 4171-4186.
[19]
Yu, Juntao, Bernd Bohnet, and Massimo Poesio. 2020. Named Entity Recognition as Dependency Parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6470-6476.
[20]
Ji, Tao, Yuanbin Wu, and Man Lan. 2019. Graph-based dependency parsing with graph neural networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2475-2485.
[21]
Sarvnaz Karimi, Alejandro Metke-Jimenez, Madonna Kemp, and Chen Wang. 2015a. CADEC: A corpus of adverse drug event annotations. J Biomed Inform, 55:73–81.
[22]
Travis R. Goodwin, Dina Demner-Fushman, Kin Wah Fung, and Phong Do. 2019. Overview of the tac 2019 track on drug-drug interaction extraction from drug labels. In Proceedings of Text Analysis Conference.
Recommendations
Arabic Named Entity Recognition from Diverse Text Types
GoTAL '08: Proceedings of the 6th international conference on Advances in Natural Language ProcessingName identification has been worked on quite intensively for the past few years, and has been incorporated into several products. Many researchers have attacked this problem in a variety of languages but only a few limited researches have focused on ...
Learning multilingual named entity recognition from Wikipedia
We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify ...
Two-stage approach to named entity recognition using Wikipedia and DBpedia
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and CommunicationIn natural language understanding, extraction of named entity (NE) mentions in given text and classification of the mentions into pre-defined NE types are important processes. Most NE recognition (NER) relies on resources such as a training corpus or NE ...
Comments
Information & Contributors
Information
Published In
April 2021
498 pages
ISBN:9781450389501
DOI:10.1145/3467707
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 September 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCAI '21
ICCAI '21: 2021 7th International Conference on Computing and Artificial Intelligence
April 23 - 26, 2021
Tianjin, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 40Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Jul 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format