Advances in Argument Mining

Katarzyna Budzynska; Chris Reed

doi:10.18653/v1/P19-4008

Advances in Argument Mining

Katarzyna Budzynska, Chris Reed

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

This course aims to introduce students to an exciting and dynamic area that has witnessed remarkable growth over the past 36 months. Argument mining builds on opinion mining, sentiment analysis and related to tasks to automatically extract not just *what* people think, but *why* they hold the opinions they do. From being largely beyond the state of the art barely five years ago, there are now many hundreds of papers on the topic, millions of dollars of commercial and research investment, and the 6th ACL workshop on the topic will be in Florence in 2019. The tutors have delivered tutorials on argument mining at ACL 2016, at IJCAI 2016 and at ESSLLI 2017; for ACL 2019, we have developed a tutorial that provides a synthesis of the major advances in the area over the past three years.

Original language	English
Title of host publication	Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publication	Tutorial Abstracts
Publisher	Association for Computational Linguistics
Pages	39-42
Number of pages	4
DOIs	https://doi.org/10.18653/v1/P19-4008
Publication status	Published - Jul 2019
Event	57th Annual Meeting of the Association for Computational Linguistics - Florence, Italy Duration: 1 Jul 2019 → 1 Jul 2019

Conference

Conference	57th Annual Meeting of the Association for Computational Linguistics
Country/Territory	Italy
City	Florence
Period	1/07/19 → 1/07/19

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10.18653/v1/P19-4008

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title = "Advances in Argument Mining",

abstract = "This course aims to introduce students to an exciting and dynamic area that has witnessed remarkable growth over the past 36 months. Argument mining builds on opinion mining, sentiment analysis and related to tasks to automatically extract not just *what* people think, but *why* they hold the opinions they do. From being largely beyond the state of the art barely five years ago, there are now many hundreds of papers on the topic, millions of dollars of commercial and research investment, and the 6th ACL workshop on the topic will be in Florence in 2019. The tutors have delivered tutorials on argument mining at ACL 2016, at IJCAI 2016 and at ESSLLI 2017; for ACL 2019, we have developed a tutorial that provides a synthesis of the major advances in the area over the past three years.",

author = "Katarzyna Budzynska and Chris Reed",

year = "2019",

month = jul,

doi = "10.18653/v1/P19-4008",

language = "English",

pages = "39--42",

booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

note = "57th Annual Meeting of the Association for Computational Linguistics ; Conference date: 01-07-2019 Through 01-07-2019",

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AB - This course aims to introduce students to an exciting and dynamic area that has witnessed remarkable growth over the past 36 months. Argument mining builds on opinion mining, sentiment analysis and related to tasks to automatically extract not just *what* people think, but *why* they hold the opinions they do. From being largely beyond the state of the art barely five years ago, there are now many hundreds of papers on the topic, millions of dollars of commercial and research investment, and the 6th ACL workshop on the topic will be in Florence in 2019. The tutors have delivered tutorials on argument mining at ACL 2016, at IJCAI 2016 and at ESSLLI 2017; for ACL 2019, we have developed a tutorial that provides a synthesis of the major advances in the area over the past three years.

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M3 - Conference contribution

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

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T2 - 57th Annual Meeting of the Association for Computational Linguistics

Y2 - 1 July 2019 through 1 July 2019

ER -

Advances in Argument Mining

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Argument Mining

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Advances in Argument Mining

Abstract

Conference

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Argument Mining

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