Neural Relation Extraction with Multi-lingual Attention

Lin, Yankai; Liu, Zhiyuan; Sun, Maosong

doi:10.18653/v1/p17-1004

Cited by 89 publications

(52 citation statements)

References 14 publications

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“…man (2015) proposed Convolution Networks with multi-sized window kernel Zeng et al (2015). proposed Piecewise Convolution Neural Networks (PCNN) Lin et al (2016Lin et al ( , 2017. improved this approach by proposing PCNN with sentence-level attention.…”

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confidence: 99%

Neural Relation Extraction for Knowledge Base Enrichment

Trisedya¹,

Weikum

Qi³

et al. 2019

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

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We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based attention model that captures multi-word entity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-theart baselines by 15.51% and 8.38% in terms of F1 score on two real-world datasets.

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confidence: 99%

Neural Relation Extraction for Knowledge Base Enrichment

Trisedya¹,

Weikum

Qi³

et al. 2019

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

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“…Unlike the existing approaches, our approach does not require aligned parallel corpora or machine translation systems. There are also several multilingual RE approaches, e.g., Verga et al (2016);Min et al (2017); Lin et al (2017), where the focus is to improve monolingual RE by jointly modeling texts in multiple languages.…”

Section: Related Workmentioning

confidence: 99%

Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping

Florian

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-ofthe-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challenging to transfer an RE model of a resource-rich language to a resource-poor language. In this paper, we propose a new approach for cross-lingual RE model transfer based on bilingual word embedding mapping. It projects word embeddings from a target language to a source language, so that a welltrained source-language neural network RE model can be directly applied to the target language. Experiment results show that the proposed approach achieves very good performance for a number of target languages on both in-house and open datasets, using a small bilingual dictionary with only 1K word pairs.

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“…This either limits the scalability of vocabulary size or relies on a strong distribution assumption. Inspired by Vulic and Moens (2016), we generate highly qualified comparable sentences via distant supervision, which is one of the most promising approaches to addressing the issue of sparse training data, and performs well in relation extraction (Lin et al, 2017a;Mintz et al, 2009;Zeng et al, 2015;Hoffmann et al, 2011;Surdeanu et al, 2012). Our comparable sentences may further benefit many other cross-lingual analysis, such as information retrieval .…”

Section: Related Workmentioning

confidence: 99%

Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

Cao¹,

Hou²,

Li³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and crosslingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitatively and quantitatively, demonstrate the significance of our method.

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Neural Relation Extraction with Multi-lingual Attention

Cited by 89 publications

References 14 publications

Neural Relation Extraction for Knowledge Base Enrichment

Neural Relation Extraction for Knowledge Base Enrichment

Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping

Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

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