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Cross-Domain NER using Cross-Domain Language Modeling

Chen Jia, Xiaobo Liang, Yue Zhang
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
Due to limitation of labeled resources, crossdomain named entity recognition (NER) has been a challenging task.  ...  To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing crossdomain and cross-task knowledge transfer by designing a novel parameter generation  ...  Intuitively, both noun entities and context patterns can be captured during LM training, which benefits the recognition of named entities.  ... 
doi:10.18653/v1/p19-1236 dblp:conf/acl/JiaXZ19 fatcat:zanegctwk5aflemccs7apfut44

Zero-Resource Cross-Lingual Named Entity Recognition

M Saiful Bari, Shafiq Joty, Prathyusha Jwalapuram
Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features.  ...  In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary  ...  Also thanks to Tasnim Mohiuddin for a useful discussion on the hyperparameters of the Word Translation model.  ... 
doi:10.1609/aaai.v34i05.6237 fatcat:fcmafyioyrhfdl7nqszdodeqli

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

M Saiful Bari and Shafiq Joty and Prathyusha Jwalapuram
2019 arXiv   pre-print
Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features.  ...  In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary  ...  In future work, we want to explore pre-trained language models for cross-lingual NER transfer.  ... 
arXiv:1911.09812v1 fatcat:fhj3krxbsre7jkzwocu327o2i4

Dual Adversarial Transfer for Sequence Labeling

Joey Tianyi Zhou, Hao Zhang, Di Jin, Xi Peng
2019 IEEE Transactions on Pattern Analysis and Machine Intelligence  
., Named entity recognition (NER), Part-of-Speech (POS) Tagging and Chunking.  ...  To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD) and adopt adversarial training to boost model generalization.  ...  The pre-trained English model and pre-trained multilingual model are used for the English sequence labeling tasks and Dutch named entity recognition task, respectively. labeling tasks.  ... 
doi:10.1109/tpami.2019.2931569 pmid:31369370 fatcat:o6qxk7gmkndobhhoct5ftkmslq

Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition [article]

Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Pengjun Xie
2021 arXiv   pre-print
Here we take named entity recognition (NER) as a study case, suggesting an annotator-aware representation learning model that inspired by the domain adaptation methods which attempt to capture effective  ...  domain-aware features.  ...  Named Entity Recognition NER is a fundamental and challenging task of NLP (Yadav and Bethard, 2018) .  ... 
arXiv:2105.14980v1 fatcat:h62jkrs6wnalnlrf6mcatsd6d4

Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation [article]

Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu
2022 arXiv   pre-print
Next, we introduce relation distillation as a means to align the unpaired cross-modal samples i.e. the unpaired target videos and unpaired 3D pose sequences.  ...  Barring synthetic or in-studio domains, acquiring such supervision for each new target environment is highly inconvenient.  ...  MPI-INF-3DHP (M) [49] is used as an unlabeled target domain to evaluate cross-studio adaptation.  ... 
arXiv:2204.01971v2 fatcat:fgdpgc3t4jfc3gh47idlxkhnqy

Test-Time Adaptation for Visual Document Understanding [article]

Sayna Ebrahimi, Sercan O. Arik, Tomas Pfister
2022 arXiv   pre-print
We also introduce new benchmarks using existing public datasets for various VDU tasks including entity recognition, key-value extraction, and document visual question answering tasks where DocTTA improves  ...  learned on a source domain to an unlabeled target domain at test time.  ...  Bottom right: documents from source and target domains for named entity recognition task from FUNSD [22] dataset.  ... 
arXiv:2206.07240v1 fatcat:sw6k5iwcknfs7nk6jzqossemvi

Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective [article]

Shumin Deng, Ningyu Zhang, Hui Chen, Feiyu Xiong, Jeff Z. Pan, Huajun Chen
2022 arXiv   pre-print
": Named Entity Recognition should identify the types of entities, e.g., 'Jack', 'Dr. Germ' ⇒ PERSON; Relation Extraction should identify the relationship of the given entity pair Jack, Dr.  ...  It learns intermediate representations of words which cluster well into named entity classes, making it classifies words with extremely limited number of training samples, and can potentially be used as  ... 
arXiv:2202.08063v1 fatcat:2q64tx2mzne53gt24adi6ymj7a

A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [article]

Alexander Kalinowski, Yuan An
2020 arXiv   pre-print
We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research.  ...  The adversarial method has been utilized to generate large benchmark datasets under the name Multilingual Unsupervised or Supervised Embeddings (MUSE), releasing parallel embedding spaces trained using  ...  While inherent noise present in human language makes learning such an alignment challenging, success in this area can assist with knowledge driven entity extraction and named entity recognition.  ... 
arXiv:2010.13688v1 fatcat:npkzwukih5gwnkvng2fxy7ls5y

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data [article]

Haoming Jiang, Danqing Zhang, Tianyu Cao, Bing Yin, Tuo Zhao
2021 arXiv   pre-print
Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER).  ...  ., without any human annotation, and shows that by merely using weakly labeled data, one can achieve good performance, though still underperforms fully supervised NER with manually/strongly labeled data  ...  Named Entity Recognition NER is the process of locating and classifying named entities in text into predefined entity categories, such as products, brands, diseases, chemicals.  ... 
arXiv:2106.08977v2 fatcat:5z7nwdkf6ra4rko7dplbp2iduu

Multi-Task Learning in Natural Language Processing: An Overview [article]

Shijie Chen, Yu Zhang, Qiang Yang
2021 arXiv   pre-print
We first review MTL architectures used in NLP tasks and categorize them into four classes, including the parallel architecture, hierarchical architecture, modular architecture, and generative adversarial  ...  Then we present optimization techniques on loss construction, data sampling, and task scheduling to properly train a multi-task model.  ...  Besides training on the POS tagging and NER tasks, a domain adversarial method is used to align monolingual word embedding matrices in an unsupervised way.  ... 
arXiv:2109.09138v1 fatcat:hlgzjykuvzczzmsgnl32w5qo5q

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios [article]

Michael A. Hedderich, Lukas Lange, Heike Adel, Jannik Strötgen, Dietrich Klakow
2021 arXiv   pre-print
As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settings.  ...  After a discussion about the different dimensions of data availability, we give a structured overview of methods that enable learning when training data is sparse.  ...  ANEA: distant supervision for correction systems with unsupervised pre-training low-resource named entity recognition. CoRR, on synthetic data.  ... 
arXiv:2010.12309v3 fatcat:26dwmlkmn5auha2ob2qdlrvla4

CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision

2019 Bioinformatics  
CoCoScore is trained using distant supervision based on a gold-standard set of associations between entities of interest.  ...  Instead of requiring a manually annotated training corpus, co-mentions are labeled as positives/negatives according to their presence/absence in the gold standard.  ...  The named entity recognition step is followed by a normalization step to a common naming scheme.  ... 
doi:10.1093/bioinformatics/btz490 pmid:31199464 pmcid:PMC6956794 fatcat:exveu52lavgdpebnzzglzvzdwm

A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis [article]

Xiang Chen, Xiaojun Wan
2022 arXiv   pre-print
Apart from that, our method can be extended to other sequence labeling tasks, such as named entity recognition (NER).  ...  Experiment results show that our proposed method outperforms the state-of-the-art methods for cross-domain ABSA by 4.32% Micro-F1 on average over 10 different domain pairs.  ...  Thank Xinyu for useful discussions. Thank Yifan for paper revision.  ... 
arXiv:2201.12549v1 fatcat:77vpzssyufdo7ly3a4pvrus3we

Massively Multilingual Transfer for NER [article]

Afshin Rahimi, Yuan Li, Trevor Cohn
2019 arXiv   pre-print
Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of  ...  In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language.  ...  Phonologically aware neu- ral model for named entity recognition in low re- source transfer settings.  ... 
arXiv:1902.00193v4 fatcat:y2rjryqrjnbs3asksdi3t2ardy
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