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Neural Entity Recognition with Gazetteer based Fusion [article]

Qing Sun, Parminder Bhatia
2021 arXiv   pre-print
Our gazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on novel entity mentions never presented during  ...  Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain.  ...  Introduction Named entity recognition (NER) (Lample et al., 2016; Ma and Hovy, 2016) aims to identify text mentions of specific entity types.  ... 
arXiv:2105.13225v1 fatcat:34nlcprfinbjrkm675l7a2po3a

Stanford at TAC KBP 2017: Building a Trilingual Relational Knowledge Graph

Arun Tejasvi Chaganty, Ashwin Paranjape, Jason Bolton, Matthew Lamm, Jinhao Lei, Abigail See, Kevin Clark, Yuhao Zhang, Peng Qi, Christopher D. Manning
2017 Text Analysis Conference  
We also experimented with data fusion with entity linking systems from entrants in the TAC KBP Entity Discovery and Linking challenge.  ...  This new Spanish system is a simple system that uses CRFbased entity recognition supplemented by gazettes followed by several ruled-based relation extractors, some using syntactic structure.  ...  Neural NER Model In addition to the improvements in entity recognition we proposed in Section 3, we also trained a new neural entity linking model for English.  ... 
dblp:conf/tac/ChagantyPBLLSCZ17 fatcat:sfqef7ewv5fzhg7y2oowxeqny4

Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text

Edwin Aldana-Bobadilla, Alejandro Molina-Villegas, Ivan Lopez-Arevalo, Shanel Reyes-Palacios, Victor Muñiz-Sanchez, Jean Arreola-Trapala
2020 Remote Sensing  
In this paper, we propose an extensible geoparsing approach including geographic entity recognition based on a neural network model and disambiguation based on what we have called dynamic context disambiguation  ...  Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing, which includes two important tasks: geographic entity recognition and toponym  ...  Geographic-Named Entity Recognition We have obtained the semantic features based on word embeddings obtained with word2vec [29] .  ... 
doi:10.3390/rs12183041 doaj:2a94e8c05d16492f856aa3ed81fb4916 fatcat:odfrdlic2fa37ahqtpx624c7wa

DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets

Canwen Xu, Jing Li, Xiangyang Luo, Jiaxin Pei, Chenliang Li, Donghong Ji
2019 The World Wide Web Conference on - WWW '19  
In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets.  ...  In this paper, we propose DLocRL, a new deep learning pipeline for fine-grained location recognition and linking in tweets, and verify its effectiveness on a real-world Twitter dataset.  ...  [25] proposed a neural network for entity linking with LSTM and attention mechanism.  ... 
doi:10.1145/3308558.3313491 dblp:conf/www/XuLLPLJ19 fatcat:y5jbc2wjsnfgpgwvzkazufkcnm

A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection [article]

Mingbin Xu, Hui Jiang
2016 arXiv   pre-print
In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing.  ...  Afterwards, a simple feedforward neural network is used to reject or predict entity label for each individual fragment.  ...  In the past few years, neural networks based deep learning approaches have achieved huge successes in many other applications, ranging from speech recognition to image classification.  ... 
arXiv:1611.00801v1 fatcat:xysrynd3yfgylkaae3lmwbisge

A Mixed Semantic Features Model for Chinese NER with Characters and Words [chapter]

Ning Chang, Jiang Zhong, Qing Li, Jiang Zhu
2020 Lecture Notes in Computer Science  
In this paper, we introduce the self-attention mechanism into the BiLSTM-CRF neural network structure for Chinese named entity recognition with two embedding.  ...  Named Entity Recognition (NER) is an essential part of many natural language processing (NLP) tasks.  ...  And [5] also propose a neural multi-digraph model with the information of gazetteers. Self-attention. Vaswani et al.  ... 
doi:10.1007/978-3-030-45439-5_24 fatcat:rrp52ui4u5hvbcpo7hcimtm3gq

USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition [article]

Beiduo Chen, Jun-Yu Ma, Jiajun Qi, Wu Guo, Zhen-Hua Ling, Quan Liu
2022 arXiv   pre-print
The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them.  ...  After adaptation, these two networks are then integrated for backend supervised named entity recognition (NER) training.  ...  Introduction Named Entity Recognition (NER) is a core natural language processing (NLP) task, which aims at finding entities and recognizing their type in a text sequence.  ... 
arXiv:2203.03216v2 fatcat:3xrhcrfnbjfwhcqsxfws7r5brq

Towards Continual Entity Learning in Language Models for Conversational Agents [article]

Ravi Teja Gadde, Ivan Bulyko
2021 arXiv   pre-print
Neural language models (LM) trained on diverse corpora are known to work well on previously seen entities, however, updating these models with dynamically changing entities such as place names, song titles  ...  We show significant perplexity improvements on task-oriented dialogue datasets, especially on long-tailed utterances, with an ability to continually adapt to new entities (to an extent).  ...  Thus taskoriented dialogue systems still rely on finite state transducers [24] and gazetteers [23] in conjunction with neural models for Automatic Speech Recognition (ASR) [18, 45, 11] and Natural  ... 
arXiv:2108.00082v2 fatcat:zf4ojiq3v5gmnnbetk7r4crzbi

Fine-Grained Named Entity Recognition Using a Multi-Stacked Feature Fusion and Dual-Stacked Output in Korean

Hongjin Kim, Harksoo Kim
2021 Applied Sciences  
Named entity recognition (NER) is a natural language processing task to identify spans that mention named entities and to annotate them with predefined named entity classes.  ...  The proposed model is based on multi-stacked long short-term memories (LSTMs) with a multi-stacked feature fusion layer for acquiring multilevel embeddings and a dual-stacked output layer for predicting  ...  ., POS tags imply a grammatical level of linguistic knowledge, and entities in a gazetteer imply a semantic level of domain knowledge).  ... 
doi:10.3390/app112210795 fatcat:lbsmshrbi5e5zknexg7njixyqi

A Survey on Recent Advances in Sequence Labeling from Deep Learning Models [article]

Zhiyong He, Zanbo Wang, Wei Wei, Shanshan Feng, Xianling Mao, Sheng Jiang
2020 arXiv   pre-print
., 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

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter [article]

Wei Liu, Xiyan Fu, Yue Zhang, Wenming Xiao
2021 arXiv   pre-print
Compared with the existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT.  ...  Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech tagging, show that LEBERT achieves the state-of-the-art results.  ...  Neural architectures for named entity recognition.  ... 
arXiv:2105.07148v3 fatcat:drc6vw5iave2plo6ndu4dhvfle

Anyuak Language Named Entity Recognition Using Deep Learning Approach

Birhanu Gardie, Mizan-Tepi University, Ethiopia, Smegnew Asemie, Kassahun Azezew
2021 Indian Journal of Science and Technology  
MAF-CNER:A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion. Complexity. 2021;2021(2):1–9.  ...  Addis Ababa Institute of Technology School of Electrical and Computer Engineering Amharic Named Entity Recognition Using Neural Word Embedding as a Feature Amharic Named Entity Recognition Using Neural  ... 
doi:10.17485/ijst/v14i39.1163 fatcat:4dskpclywbeglmbi5fn5upquza

Learning adaptive representations for entity recognition in the biomedical domain

Ivano Lauriola, Fabio Aiolli, Alberto Lavelli, Fabio Rinaldi
2021 Journal of Biomedical Semantics  
To this end, we use a hybrid architecture for biomedical entity recognition which integrates dictionary look-up (also known as gazetteers) with machine learning techniques.  ...  Background Named Entity Recognition is a common task in Natural Language Processing applications, whose purpose is to recognize named entities in textual documents.  ...  Fig. 5 5 Contribution of base representations in the BioNER Abbreviations NER: Named entity recognition; BNER: Biomedical named entity recognition; CAT: Computed aided tomography; HIV: Human immunodeficiency  ... 
doi:10.1186/s13326-021-00238-0 pmid:34001263 fatcat:bw76dwrw3rcfxjj676mw5rdg2y

Enhancing Neural Sequence Labeling with Position-Aware Self-Attention [article]

Wei Wei, Zanbo Wang, Xianling Mao, Guangyou Zhou, Pan Zhou, Sheng Jiang
2019 arXiv   pre-print
Extensive experiments on three classical tasks in sequence labeling domain, i.e., part-of-speech (POS) tagging, named entity recognition (NER) and phrase chunking, demonstrate our proposed model outperforms  ...  Specifically, we propose an innovative and well-designed attention-based model (called position-aware self-attention, i.e., PSA) within a neural network architecture, to explore the positional information  ...  ., POS tagging, phrase chunking, named entity recognition (NER) and etc.  ... 
arXiv:1908.09128v1 fatcat:6mv762ancrdn7mgyifpzvipy6a

Named Entity Recognition in Multi-level Contexts

Yubo Chen, Chuhan Wu, Tao Qi, Zhigang Yuan, Yongfeng Huang
2020 International Joint Conference on Natural Language Processing  
We jointly train our model in entity recognition and the auxiliary classification task via multi-task learning.  ...  Named entity recognition is a critical task in the natural language processing field. Most existing methods for this task can only exploit contextual information within a sentence.  ...  Introduction Named Entity Recognition (NER) is defined as automatically identifying and classifying named entities into specific categories (e.g., person, location, organization) in text.  ... 
dblp:conf/ijcnlp/ChenWQYH20 fatcat:3d7frrbf2jg43kzqjcapjyxvvy
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