Named Entity Recognition for Novel Types by Transfer Learning

Qu, Lizhen; Ferraro, Gabriela; Zhou, Liyuan; Hou, Weiwei; Baldwin, Timothy

doi:10.18653/v1/d16-1087

Cited by 26 publications

(23 citation statements)

References 10 publications

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“…When considering how to deal with the situation of lack of labelled data, Pen and Yang (2010) have categorized and reviewed the reserch progress on transfer learning for classification, regression, and clustering problems. Similar to the work of Qu et al (2016) in domain adaption, in this paper we study a instance transfer strategy, which is hot today but rarely used in NER (Arnold et al, 2008;Chen et al, 2014), to make use of out-of-domain data (source domain).…”

Section: Related Workmentioning

confidence: 99%

An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

et al. 2020

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Recently, neural networks have shown promising results for named entity recognition (NER), which needs a number of labeled data to for model training. When meeting a new domain (target domain) for NER, there is no or a few labeled data, which makes domain NER much more difficult. As NER has been researched for a long time, some similar domain already has well labelled data (source domain). Therefore, in this paper, we focus on domain NER by studying how to utilize the labelled data from such similar source domain for the new target domain. We design a kernel function based instance transfer strategy by getting similar labelled sentences from a source domain. Moreover, we propose an enhanced recurrent neural network (ERNN) by adding an additional layer that combines the source domain labelled data into traditional RNN structure. Comprehensive experiments are conducted on two datasets. The comparison results among HMM, CRF and RNN show that RNN performs bette than others. When there is no labelled data in domain target, compared to directly using the source domain labelled data without selecting transferred instances, our enhanced RNN approach gets improvement from 0.8052 to 0.9328 in terms of F1 measure.

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Section: Related Workmentioning

confidence: 99%

An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

et al. 2020

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“…Researches were carried out on NER related TL too. Qu et al, (2016) explored TL for NER with different NE categories (different output spaces). They pre-train a linear-chain CRF on large amount annotated data in the source domain.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning for Sequence Labeling Using Source Model and Target Data

Chen

Moschitti

2019

AAAI

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In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label categories appear. Our transfer learning (TL) techniques enable to adapt the source model using the target data and new categories, without accessing to the source data. Our solution consists in adding new neurons in the output layer of the target model and transferring parameters from the source model, which are then fine-tuned with the target data. Additionally, we propose a neural adapter to learn the difference between the source and the target label distribution, which provides additional important information to the target model. Our experiments on Named Entity Recognition show that (i) the learned knowledge in the source model can be effectively transferred when the target data contains new categories and (ii) our neural adapter further improves such transfer.

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“…Given the explosion in tag-set size, they introduce automatic pruning of cross-product tags. Kim et al (2015) and Qu et al (2016) automatically learn correlations between tag-sets, given training data for both tag-sets. They rely on similar contexts for related source and target tags, such as 'professor' and 'student'.…”

Section: Related Workmentioning

confidence: 99%

A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy

Beryozkin¹,

Drori²,

Gilon³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags. This setting occurs when various datasets are created using different annotation schemes. This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset. We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. We compare this model to combining independent models and to a model based on the multitasking approach. Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.

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Named Entity Recognition for Novel Types by Transfer Learning

Cited by 26 publications

References 10 publications

An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

Transfer Learning for Sequence Labeling Using Source Model and Target Data

A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy

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