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Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition
[article]
2022
arXiv
pre-print
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Nested named entity recognition (NER) aims to identify the entity boundaries and recognize categories of the named entities in a complex hierarchical sentence. ...
In this paper, we propose a trigger-based graph neural network (Trigger-GNN) to leverage the nested NER. ...
INTRODUCTION Named entity recognition (nested-NER) aims to identify the entity boundaries and recognize the categories of named entities in a sentence [1] , [2] . ...
arXiv:2204.05518v2
fatcat:5zwqaktriffeba56wmnti36en4
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation
2019
The World Wide Web Conference on - WWW '19
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2019 pre-print arXiv:1905.01964v1 fatcat:llou5jm2vjagvazjxo5nsioshy
Experiments on two benchmark datasets show that our approach can effectively improve the performance of Chinese named entity recognition, especially when training data is insufficient. ...
Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly context-dependent. ...
CONCLUSION In this paper we propose a neural approach for Chinese named entity recognition. ...
doi:10.1145/3308558.3313743
dblp:conf/www/WuLWHX19
fatcat:22gxznga7nfjre3zcisxkfouny
Multi-grained Named Entity Recognition
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be nonoverlapping or totally nested. ...
named entities without explicitly assuming non-overlapping or totally nested structures. ...
GPE GPE To tackle the aforementioned drawbacks, we propose a novel neural framework, named MGNER, for Multi-Grained Named Entity Recognition. ...
doi:10.18653/v1/p19-1138
dblp:conf/acl/XiaZYLDWFMY19
fatcat:44cyxbfn7jafvdnxmqrktuuh2y
Myanmar named entity corpus and its use in syllable-based neural named entity recognition
2020
International Journal of Electrical and Computer Engineering (IJECE)
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This work also contributes the first evaluation of various deep neural network architectures on Myanmar Named Entity Recognition. ...
This work also aims to discover the effectiveness of neural network approaches to textual processing for Myanmar language as well as to promote future research works on this understudied language. ...
INTRODUCTION Named Entity Recognition (NER) is the process of automatically tagging, identifying or labeling different named entities (NE) in text in accordance with the predefined sets of NE categories ...
doi:10.11591/ijece.v10i2.pp1544-1551
fatcat:ijmmsb7qnrfffmzetnnlgpcb7q
BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling
2022
Applied Sciences
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Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. ...
Two nested named-entity-recognition datasets, i.e., GENIA and GermEval 2014, were used for the experiment, with four and two levels of annotation, respectively. ...
Source Used Approach F1-Score [13] Neural-network-based (boundary aware Bi-LSTM) 71.7 [53] Neural-network-based (feed forward, Bi-LSTM, Win-bi-LSTM) 76.12 [54] Neural-network-based (Bi-LSTM-CRF) 75.3 [ ...
doi:10.3390/app12030976
fatcat:ics5x5znkvhuziuydjcsvbcscm
Chemlistem: chemical named entity recognition using recurrent neural networks
2018
Journal of Cheminformatics
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We present here several chemical named entity recognition systems. ...
Chemical named entity recognition (NER) has traditionally been dominated by conditional random fields (CRF)-based approaches but given the success of the artificial neural network techniques known as " ...
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
doi:10.1186/s13321-018-0313-8
pmid:30523437
pmcid:PMC6755713
fatcat:2mi6zdehqfahpibaxww6mw3h6q
An Algorithm of Vocabulary Enhanced Intelligent Question Answering Based on FLAT1
[chapter]
2021
Frontiers in Artificial Intelligence and Applications
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In recent years, the lexical enhancement structure of word nodes combined with word nodes has been proved to be an effective method for Chinese named entity recognition. ...
Among them, the entity recognition part is one of the key points. ...
The deep neural network model has become a research trend in named entity recognition tasks because it does not require manual feature engineering and expert knowledge in related fields [4] . ...
doi:10.3233/faia210460
fatcat:vdhu3vyqzbhgngiuryagzof2vi
Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM-CRF
2021
Wireless Communications and Mobile Computing
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In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of interest (i.e., herb ...
Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. ...
Compared with the HMM model, the LSTM model uses a deeper and more complex neural network. ...
doi:10.1155/2021/6696205
fatcat:75gubhmcc5dsvd4mot263zllte
Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
2021
Applied Sciences
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Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. ...
Recently, deep neural networks (NNs) with the attention mechanism yield excellent performance in NER by taking advantage of character-level and word-level representation learning. ...
[6] proposed a neural model by incorporating the word-level and entity-level contextualized representations, entity-aware self-attention, and bidirectional transformer, which obtain state-of-the-art ...
doi:10.3390/app11199038
fatcat:xlkuygvsk5c2nc7knvdni3qcsq
A Survey on Recent Advances in Sequence Labeling from Deep Learning Models
[article]
2020
arXiv
pre-print
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., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc. ...
In this paper, we aim to present a comprehensive review of existing deep learning-based sequence labeling models, which consists of three related tasks, e.g., part-of-speech tagging, named entity recognition ...
[113] employ stacked Gated Convolutional Neural Networks(GCNN) for named entity recognition, which extend the convolutional layer with gating mechanism. ...
arXiv:2011.06727v1
fatcat:lbephd7kdjh6libg2v5xju7lri
Multi-Directional Heuristic Search
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding an optimal meeting location for multiple agents. ...
Traditionally, the task of extracting semantic relations between entities is decoupled into a pipeline of two separated * Corresponding Author † Corresponding Author subtasks, namely named entity recognition ...
We use the strict evaluation: the boundary and type of extracted entities should be both correct for NER; named entities and the type of their relationships should be both correct for RE. ...
doi:10.24963/ijcai.2020/558
dblp:conf/ijcai/ZhaoHC020
fatcat:ijjx26naczchnj253oxqgsnphe
Bipartite Flat-Graph Network for Nested Named Entity Recognition
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph ...
Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. ...
Nested named entity recognition requires to identity all the entities in texts that may be nested with each other. ...
arXiv:2005.00436v1
fatcat:rqsgcxs5jnc2zgzot2pdgwfsky
Improving Graph Convolutional Networks Based on Relation-aware Attention for End-to-End Relation Extraction
2020
IEEE Access
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In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them. ...
To consider the complete interaction between entities and relations, we propose a novel relation-aware attention mechanism to obtain the relation representation between two entity spans. ...
The pipeline method treats the task as two separate subtasks, namely named entity recognition (NER) and relation classification (RC). ...
doi:10.1109/access.2020.2980859
fatcat:ueeiv74fmndkpgthcofndyktta
Attention-Based LSTM with Filter Mechanism for Entity Relation Classification
2020
Symmetry
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In this paper, we present a novel model multi-head attention long short term memory (LSTM) network with filter mechanism (MALNet) to extract the text features and classify the relation of two entities ...
The noise caused by irrelevant words and the word distance between the tagged entities may affect the relation classification accuracy. ...
[29] proposed a model bidirectional LSTM networks with entity-aware attention to learn more semantic features. Yan et al. ...
doi:10.3390/sym12101729
fatcat:6c2chaucijf75muqyllmy2ssqu
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background ...
We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. ...
, such as parsing (Dyer et al., 2015) , named entity recognition (Lample et al., 2016) , and semantic role labeling (Zhou and Xu, 2015) ). ...
doi:10.18653/v1/p17-1132
dblp:conf/acl/YangM17
fatcat:ricckwiwizgappmuwxusjpl4wm
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