@inproceedings{shree-etal-2019-deep,
title = "Deep Natural Language Understanding of News Text",
author = "Shree, Jaya and
Liu, Emily and
Gordon, Andrew and
Hobbs, Jerry",
editor = "Bamman, David and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Fiterau, Madalina and
Iyyer, Mohit",
booktitle = "Proceedings of the First Workshop on Narrative Understanding",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2403",
doi = "10.18653/v1/W19-2403",
pages = "19--27",
abstract = "Early proposals for the deep understanding of natural language text advocated an approach of {``}interpretation as abduction,{''} where the meaning of a text was derived as an explanation that logically entailed the input words, given a knowledge base of lexical and commonsense axioms. While most subsequent NLP research has instead pursued statistical and data-driven methods, the approach of interpretation as abduction has seen steady advancements in both theory and software implementations. In this paper, we summarize advances in deriving the logical form of the text, encoding commonsense knowledge, and technologies for scalable abductive reasoning. We then explore the application of these advancements to the deep understanding of a paragraph of news text, where the subtle meaning of words and phrases are resolved by backward chaining on a knowledge base of 80 hand-authored axioms.",
}
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<abstract>Early proposals for the deep understanding of natural language text advocated an approach of “interpretation as abduction,” where the meaning of a text was derived as an explanation that logically entailed the input words, given a knowledge base of lexical and commonsense axioms. While most subsequent NLP research has instead pursued statistical and data-driven methods, the approach of interpretation as abduction has seen steady advancements in both theory and software implementations. In this paper, we summarize advances in deriving the logical form of the text, encoding commonsense knowledge, and technologies for scalable abductive reasoning. We then explore the application of these advancements to the deep understanding of a paragraph of news text, where the subtle meaning of words and phrases are resolved by backward chaining on a knowledge base of 80 hand-authored axioms.</abstract>
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%0 Conference Proceedings
%T Deep Natural Language Understanding of News Text
%A Shree, Jaya
%A Liu, Emily
%A Gordon, Andrew
%A Hobbs, Jerry
%Y Bamman, David
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Fiterau, Madalina
%Y Iyyer, Mohit
%S Proceedings of the First Workshop on Narrative Understanding
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F shree-etal-2019-deep
%X Early proposals for the deep understanding of natural language text advocated an approach of “interpretation as abduction,” where the meaning of a text was derived as an explanation that logically entailed the input words, given a knowledge base of lexical and commonsense axioms. While most subsequent NLP research has instead pursued statistical and data-driven methods, the approach of interpretation as abduction has seen steady advancements in both theory and software implementations. In this paper, we summarize advances in deriving the logical form of the text, encoding commonsense knowledge, and technologies for scalable abductive reasoning. We then explore the application of these advancements to the deep understanding of a paragraph of news text, where the subtle meaning of words and phrases are resolved by backward chaining on a knowledge base of 80 hand-authored axioms.
%R 10.18653/v1/W19-2403
%U https://aclanthology.org/W19-2403
%U https://doi.org/10.18653/v1/W19-2403
%P 19-27
Markdown (Informal)
[Deep Natural Language Understanding of News Text](https://aclanthology.org/W19-2403) (Shree et al., WNU 2019)
ACL
- Jaya Shree, Emily Liu, Andrew Gordon, and Jerry Hobbs. 2019. Deep Natural Language Understanding of News Text. In Proceedings of the First Workshop on Narrative Understanding, pages 19–27, Minneapolis, Minnesota. Association for Computational Linguistics.