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Zero-Resource Cross-Domain Named Entity Recognition [article]

Zihan Liu, Genta Indra Winata, Pascale Fung
2020 arXiv   pre-print
We then introduce a framework called Mixture of Entity Experts (MoEE) to improve the robustness for zero-resource domain adaptation.  ...  Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains.  ...  Acknowledgments This work is partially funded by ITF/319/16FP and MRP/055/18 of the Innovation Technology Commission, the Hong Kong SAR Government.  ... 
arXiv:2002.05923v2 fatcat:waj3ssskzbgidl74uowht3jroq

Unsupervised Vocabulary Adaptation for Morph-based Language Models

André Mansikkaniemi, Mikko Kurimo
2012 North American Chapter of the Association for Computational Linguistics  
Over-segmented foreign entity names are restored to their base forms in the morph-segmented in-domain text for easier and more reliable modeling and recognition.  ...  In this paper we present an unsupervised vocabulary adaptation method for morph-based speech recognition.  ...  In-domain text Find FENs Stemmer The adaptation framework will be compared to a supervised method where the adaptation steps are done manually.  ... 
dblp:conf/naacl/MansikkaniemiK12 fatcat:7yeb5bpttzfk3dfxn6gywodqxi

Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling [article]

Zihan Liu, Genta Indra Winata, Peng Xu, Pascale Fung
2020 arXiv   pre-print
Furthermore, our model can also be applied to the cross-domain named entity recognition task, and it achieves better adaptation performance than other existing baselines.  ...  As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available.  ...  Acknowledgments This work is partially funded by ITF/319/16FP and MRP/055/18 of the Innovation Technology Commission, the Hong Kong SAR Government.  ... 
arXiv:2004.11727v1 fatcat:k2xrxo4hsjcadj7kgqvvw7qv6q

CRFs-Based Named Entity Recognition Incorporated with Heuristic Entity List Searching

Fan Yang, Jun Zhao, Bo Zou
2008 International Joint Conference on Natural Language Processing  
Chinese Named entity recognition is one of the most important tasks in NLP.  ...  We also incorporate an efficient heuristic named entity list searching process into the framework of statistical model in order to improve both the performance and the adaptability of the statistical NER  ...  Introduction Named Entity Recognition (NER) is one of the most important tasks in NLP, and acts as a critical role in some language processing applications, such as Information Extraction and Integration  ... 
dblp:conf/ijcnlp/YangZZ08 fatcat:t3pkwvivxrdojbgtmrnh2cx5wq

Multi-task Domain Adaptation for Sequence Tagging

Nanyun Peng, Mark Dredze
2017 Proceedings of the 2nd Workshop on Representation Learning for NLP  
We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition.  ...  We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation.  ...  Experimental Setup We test the effectiveness of the multi-task domain adaptation framework on two sequence tagging problems: Chinese word segmentation (CWS) and named entity recognition (NER).  ... 
doi:10.18653/v1/w17-2612 dblp:conf/rep4nlp/PengD17 fatcat:r2g43hr4pfad5irxwbyrqt4ibu

Results of the WNUT16 Named Entity Recognition Shared Task

Benjamin Strauss, Bethany Toma, Alan Ritter, Marie-Catherine de Marneffe, Wei Xu
2016 Workshop on Noisy User-generated Text  
This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating.  ...  We outline the shared task, annotation process and dataset statistics, and provide a high-level overview of the participating systems for each shared task.  ...  Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.  ... 
dblp:conf/aclnut/StraussTRMX16 fatcat:tdg73bfj75bqlnhw7zh3ipmafe

Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models [chapter]

Hendrik ter Horst, Matthias Hartung, Philipp Cimiano
2017 Lecture Notes in Computer Science  
In this paper we present a probabilistic system based on undirected graphical models that jointly addresses both the entity recognition and the linking task.  ...  Our framework considers the span of mentions of entities as well as the corresponding knowledge base identifier as random variables and models the joint assignment using a factorized distribution.  ...  Acknowledgments This work has been funded by the Federal Ministry of Education and Research (BMBF, Germany) in the PSINK project (project number 031L0028A).  ... 
doi:10.1007/978-3-319-59888-8_15 fatcat:3bgd5rivfnen7jx2txmncdf2la

Multi-task Domain Adaptation for Sequence Tagging [article]

Nanyun Peng, Mark Dredze
2017 arXiv   pre-print
We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition.  ...  We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation.  ...  Initialization Experimental Setup We test the effectiveness of the multi-task domain adaptation framework on two sequence tagging problems: Chinese word segmentation (CWS) and named entity recognition  ... 
arXiv:1608.02689v2 fatcat:rhp3xb64irhv3ghzu3jwkwa5cq

CrossNER: Evaluating Cross-Domain Named Entity Recognition [article]

Zihan Liu, Yan Xu, Tiezheng Yu, Wenliang Dai, Ziwei Ji, Samuel Cahyawijaya, Andrea Madotto, Pascale Fung
2020 arXiv   pre-print
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.  ...  Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the  ...  This work is partially funded by ITF/319/16FP and MRP/055/18 of the Innovation Technology Commission, the Hong Kong SAR Government.  ... 
arXiv:2012.04373v2 fatcat:axipzzh7unfxphxg5cr6gkxrf4

DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population [article]

Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li, Xiaozhuan Liang, Yunzhi Yao (+10 others)
2022 arXiv   pre-print
DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction.  ...  Besides, we present an online system in http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various tasks, and a demo video.  ...  Named Entity Recognition As an essential task of IE, named entity recognition (NER) picks out the entity mentions and classifies them into pre-defined semantic categories given plain texts.  ... 
arXiv:2201.03335v4 fatcat:wp7iohheprhathl4crmp44wkpa

Exploring Modular Task Decomposition in Cross-domain Named Entity Recognition

Xinghua Zhang, Bowen Yu, Yubin Wang, Tingwen Liu, Taoyu Su, Hongbo Xu
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
Cross-domain Named Entity Recognition (NER) aims to transfer knowledge from the source domain to the target, alleviating expensive labeling costs in the target domain.  ...  Concretely, we suggest a modular learning approach in which two sub-tasks (entity span detection and type classification) are learned by separate functional modules to perform respective cross-domain transfer  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their insightful comments and constructive suggestions.  ... 
doi:10.1145/3477495.3531976 fatcat:vtz5l5qjz5dvln65vuacyw6gnm

Architectural elements of language engineering robustness

DIANA MAYNARD, VALENTIN TABLAN, HAMISH CUNNINGHAM, CRISTIAN URSU, HORACIO SAGGION, KALINA BONTCHEVA, YORICK WILKS
2002 Natural Language Engineering  
To verify our ideas we present results from the development of a multi-purpose cross-genre Named Entity recognition system.  ...  domains and genres.  ...  The ACE entity detection and tracking (EDT) task goes beyond existing named entity recognition tasks, in that all mentions of an entity (in the form of a name, description or pronoun) must be recognised  ... 
doi:10.1017/s1351324902002930 fatcat:7me2xr4c7ncpbkhh7nf5tmhhwq

Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model

Min Xiao, Yuhong Guo
2013 International Conference on Machine Learning  
We empirically evaluate the proposed learning technique on WSJ and MED-LINE domains with POS tagging systems, and on WSJ and Brown corpora with syntactic chunking and named entity recognition systems.  ...  Our primary results show that the proposed domain adaptation method outperforms a number of comparison methods for cross domain sequence labeling tasks.  ...  Under this setting, the test results in term of error rate are reported in Domain Adaptation for Named Entity Recognition For named entity recognition task, we used the same source data and target data  ... 
dblp:conf/icml/XiaoG13 fatcat:e5ayddancbe4vb2jemwmgbqqja

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

BalaKrishna Kolluru, Lezan Hawizy, Peter Murray-Rust, Junichi Tsujii, Sophia Ananiadou, Tim J. Hubbard
2011 PLoS ONE  
These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern  ...  Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER)  ...  Paul Dobson for valuable comments, and Dr. John McNaught for the numerous edits of our paper. Last, but not least, we would like to thank Dr. Yoshinobu Kano for his help with U-Compare.  ... 
doi:10.1371/journal.pone.0020181 pmid:21633495 pmcid:PMC3102085 fatcat:va6yipqxtvfdhjbqlyptqt4yzu

Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework [article]

Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao
2021 arXiv   pre-print
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings.  ...  Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity  ...  Acknowledgment The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions.  ... 
arXiv:2109.05357v1 fatcat:hgxymfox5vcvndhxknqs24nkzu
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