@inproceedings{chen-etal-2019-global,
title = "Global Textual Relation Embedding for Relational Understanding",
author = "Chen, Zhiyu and
Zha, Hanwen and
Liu, Honglei and
Chen, Wenhu and
Yan, Xifeng and
Su, Yu",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1127",
doi = "10.18653/v1/P19-1127",
pages = "1324--1330",
abstract = "Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose embedding of textual relations, defined as the shortest dependency path between entities. Textual relation embedding provides a level of knowledge between word/phrase level and sentence level, and we show that it can facilitate downstream tasks requiring relational understanding of the text. To learn such an embedding, we create the largest distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. We use global co-occurrence statistics between textual and knowledge base relations as the supervision signal to train the embedding. Evaluation on two relational understanding tasks demonstrates the usefulness of the learned textual relation embedding. The data and code can be found at \url{https://github.com/czyssrs/GloREPlus}",
}
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<abstract>Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose embedding of textual relations, defined as the shortest dependency path between entities. Textual relation embedding provides a level of knowledge between word/phrase level and sentence level, and we show that it can facilitate downstream tasks requiring relational understanding of the text. To learn such an embedding, we create the largest distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. We use global co-occurrence statistics between textual and knowledge base relations as the supervision signal to train the embedding. Evaluation on two relational understanding tasks demonstrates the usefulness of the learned textual relation embedding. The data and code can be found at https://github.com/czyssrs/GloREPlus</abstract>
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%0 Conference Proceedings
%T Global Textual Relation Embedding for Relational Understanding
%A Chen, Zhiyu
%A Zha, Hanwen
%A Liu, Honglei
%A Chen, Wenhu
%A Yan, Xifeng
%A Su, Yu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-etal-2019-global
%X Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose embedding of textual relations, defined as the shortest dependency path between entities. Textual relation embedding provides a level of knowledge between word/phrase level and sentence level, and we show that it can facilitate downstream tasks requiring relational understanding of the text. To learn such an embedding, we create the largest distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. We use global co-occurrence statistics between textual and knowledge base relations as the supervision signal to train the embedding. Evaluation on two relational understanding tasks demonstrates the usefulness of the learned textual relation embedding. The data and code can be found at https://github.com/czyssrs/GloREPlus
%R 10.18653/v1/P19-1127
%U https://aclanthology.org/P19-1127
%U https://doi.org/10.18653/v1/P19-1127
%P 1324-1330
Markdown (Informal)
[Global Textual Relation Embedding for Relational Understanding](https://aclanthology.org/P19-1127) (Chen et al., ACL 2019)
ACL
- Zhiyu Chen, Hanwen Zha, Honglei Liu, Wenhu Chen, Xifeng Yan, and Yu Su. 2019. Global Textual Relation Embedding for Relational Understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1324–1330, Florence, Italy. Association for Computational Linguistics.