Statistical modality tagging from rule-based annotations and crowdsourcing

Vinodkumar Prabhakaran; Michael Bloodgood; Mona Diab; Bonnie Dorr; Lori Levin; Christine D. Piatko; Owen Rambow; Benjamin Van Durme

Statistical modality tagging from rule-based annotations and crowdsourcing

Vinodkumar Prabhakaran
, Michael Bloodgood
, Mona Diab
, Bonnie Dorr
, Lori Levin
, Christine D. Piatko
, Owen Rambow
, Benjamin Van Durme

Linguistics

Stony Brook University

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

21 Scopus citations

Abstract

We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.

Original language	English
Title of host publication	Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012
Editors	Roser Morante, Caroline Sporleder
Publisher	Association for Computational Linguistics (ACL)
Pages	57-64
Number of pages	8
ISBN (Electronic)	9781937284343
State	Published - 2012
Event	2012 Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Jeju Island, Korea, Republic of Duration: Jul 13 2012 → …

Publication series

Name	Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

Conference

Conference	2012 Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012
Country/Territory	Korea, Republic of
City	Jeju Island
Period	07/13/12 → …

Cite this

Prabhakaran, V., Bloodgood, M., Diab, M., Dorr, B., Levin, L., Piatko, C. D., Rambow, O., & Van Durme, B. (2012). Statistical modality tagging from rule-based annotations and crowdsourcing. In R. Morante, & C. Sporleder (Eds.), Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 (pp. 57-64). (Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012). Association for Computational Linguistics (ACL).

Prabhakaran, Vinodkumar ; Bloodgood, Michael ; Diab, Mona et al. / Statistical modality tagging from rule-based annotations and crowdsourcing. Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012. editor / Roser Morante ; Caroline Sporleder. Association for Computational Linguistics (ACL), 2012. pp. 57-64 (Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012).

@inproceedings{24397b5372f845f1b4b4d0b69807eb86,

title = "Statistical modality tagging from rule-based annotations and crowdsourcing",

abstract = "We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.",

author = "Vinodkumar Prabhakaran and Michael Bloodgood and Mona Diab and Bonnie Dorr and Lori Levin and Piatko, \{Christine D.\} and Owen Rambow and \{Van Durme\}, Benjamin",

note = "Publisher Copyright: {\textcopyright} 2012 Association for Computational Linguistics.; 2012 Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 ; Conference date: 13-07-2012",

year = "2012",

language = "English",

series = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012",

publisher = "Association for Computational Linguistics (ACL)",

pages = "57--64",

editor = "Roser Morante and Caroline Sporleder",

booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012",

}

Prabhakaran, V, Bloodgood, M, Diab, M, Dorr, B, Levin, L, Piatko, CD, Rambow, O & Van Durme, B 2012, Statistical modality tagging from rule-based annotations and crowdsourcing. in R Morante & C Sporleder (eds), Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012. Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012, Association for Computational Linguistics (ACL), pp. 57-64, 2012 Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012, Jeju Island, Korea, Republic of, 07/13/12.

Statistical modality tagging from rule-based annotations and crowdsourcing. / Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona et al.
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012. ed. / Roser Morante; Caroline Sporleder. Association for Computational Linguistics (ACL), 2012. p. 57-64 (Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012).

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

TY - GEN

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AU - Prabhakaran, Vinodkumar

AU - Bloodgood, Michael

AU - Diab, Mona

AU - Dorr, Bonnie

AU - Levin, Lori

AU - Piatko, Christine D.

AU - Rambow, Owen

AU - Van Durme, Benjamin

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AB - We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.

UR - https://www.scopus.com/pages/publications/84908594665

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A2 - Morante, Roser

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PB - Association for Computational Linguistics (ACL)

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Y2 - 13 July 2012

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Prabhakaran V, Bloodgood M, Diab M, Dorr B, Levin L, Piatko CD et al. Statistical modality tagging from rule-based annotations and crowdsourcing. In Morante R, Sporleder C, editors, Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012. Association for Computational Linguistics (ACL). 2012. p. 57-64. (Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExPro 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012).

Statistical modality tagging from rule-based annotations and crowdsourcing

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