Robust Domain Adaptation for Relation Extraction via Clustering Consistency

Nguyen, Minh Luan; Tsang, Ivor W.; Chieu, Hai Leong

doi:10.3115/v1/p14-1076

Cited by 12 publications

(2 citation statements)

References 18 publications

(13 reference statements)

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“…The unsupervised approach mainly regards the relation extraction task as the clustering problem (Das et al , 2019; Min et al , 2012; Nguyen et al , 2014). For clustering, a huge number of entity pairs would firstly be chosen and the similarities based on the intermediate context of the entity would be measured.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards an entity relation extraction framework in the cross-lingual context

Xue

Wang

et al. 2021

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Purpose Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages. Design/methodology/approach This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction. Findings The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages. Originality/value The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.

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Section: Literature Reviewmentioning

confidence: 99%

Towards an entity relation extraction framework in the cross-lingual context

Xue

Wang

et al. 2021

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“…Multiple labels from single corpora: For ChemProt corpora we consider various labels as different sources following Nguyen et al, (2014) The five positive labels of ChemProt are: CPR: 3, CPR: 4, CPR: 5, CPR: 6, CPR: 9 which stand for upregulator, downregulator, agonist, antagonist and substrate, respectively. We predict the classification performance for unlabeled targets CPR:6 and CPR:9 taking multi-source labeled input denoted as 3C from three sources-CPR: 3, CPR: 4, CPR: 5 as positive instances and remaining as negative.…”

Section: 2 Multi-source Single Target (Msst)mentioning

confidence: 99%

Biomedical Relation Classification by single and multiple source domain adaptation

Chakraborty

Sarkar

Goyal

et al. 2019

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it takes a lot of effort to label data for a new domain. In this paper, we explore domain adaptation techniques for this task. While past works have focused on single source domain adaptation for bio-medical relation classification, we classify relations in an unlabeled target domain by transferring useful knowledge from one or more related source domains. Our experiments with the model have shown to improve state-of-the-art F1 score on 3 benchmark biomedical corpora for single domain and on 2 out of 3 for multi-domain scenarios. When used with contextualized embeddings, there is further boost in performance outperforming neural-network based domain adaptation baselines for both the cases.

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Feature assembly method for extracting relations in Chinese

Chen

2015

Artificial Intelligence

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Please cite this article in press as: Y. Chen et al., Feature assembly method for extracting relations in Chinese, Artificial Intelligence (2015), http://dx. AbstractThe goal of relation extraction is to detect relations between two entities in free text. In a sentence, a relation instance usually comprises a small number of words, which yields a sparse feature representation. To make better use of limited information in a relation instance, parsing trees and combined features are employed widely to capture the local dependencies of relation instances. However, the performance of parsing tree-based systems is often degraded by chunking or parsing errors. Combined features are used widely, but few studies have addressed how features can be combined to achieve optimal performance. Thus, in this study, we propose a feature assembly method for relation extraction. Six types of candidate features (head noun, POS tag, n-gram, omni-word, etc.) are employed as atomic features and six constraint conditions (singleton, position, syntax, etc.) are used to combine these features in different settings. Depending on the utilization of candidate features, different constraint conditions can be explored to achieve the optimal extraction performance. Our method is effective for capturing local dependencies and it reduces the errors caused by inaccurate parsing. We tested the proposed method using the ACE 2005 Chinese and English corpora, and it achieved state-of-the-art performance, where it was significantly superior to existing methods.

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Robust Domain Adaptation for Relation Extraction via Clustering Consistency

Cited by 12 publications

References 18 publications

Towards an entity relation extraction framework in the cross-lingual context

Towards an entity relation extraction framework in the cross-lingual context

Biomedical Relation Classification by single and multiple source domain adaptation

Feature assembly method for extracting relations in Chinese

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