@inproceedings{rajendran-etal-2019-ne,
title = "{NE}-Table: A Neural key-value table for Named Entities",
author = "Rajendran, Janarthanan and
Ganhotra, Jatin and
Guo, Xiaoxiao and
Yu, Mo and
Singh, Satinder and
Polymenakos, Lazaros",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1114",
doi = "10.26615/978-954-452-056-4_114",
pages = "980--993",
abstract = "Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task, which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set which are available at - \url{https://github.com/IBM/ne-table-datasets/}",
}
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<abstract>Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task, which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set which are available at - https://github.com/IBM/ne-table-datasets/</abstract>
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%0 Conference Proceedings
%T NE-Table: A Neural key-value table for Named Entities
%A Rajendran, Janarthanan
%A Ganhotra, Jatin
%A Guo, Xiaoxiao
%A Yu, Mo
%A Singh, Satinder
%A Polymenakos, Lazaros
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F rajendran-etal-2019-ne
%X Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task, which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set which are available at - https://github.com/IBM/ne-table-datasets/
%R 10.26615/978-954-452-056-4_114
%U https://aclanthology.org/R19-1114
%U https://doi.org/10.26615/978-954-452-056-4_114
%P 980-993
Markdown (Informal)
[NE-Table: A Neural key-value table for Named Entities](https://aclanthology.org/R19-1114) (Rajendran et al., RANLP 2019)
ACL
- Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh, and Lazaros Polymenakos. 2019. NE-Table: A Neural key-value table for Named Entities. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 980–993, Varna, Bulgaria. INCOMA Ltd..