Inducing Event Types and Roles in Reverse: Using Function to Discover Theme

Natalie Ahn

doi:10.18653/v1/W17-2710

Inducing Event Types and Roles in Reverse: Using Function to Discover Theme

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Abstract

With growing interest in automated event extraction, there is an increasing need to overcome the labor costs of hand-written event templates, entity lists, and annotated corpora. In the last few years, more inductive approaches have emerged, seeking to discover unknown event types and roles in raw text. The main recent efforts use probabilistic generative models, as in topic modeling, which are formally concise but do not always yield stable or easily interpretable results. We argue that event schema induction can benefit from greater structure in the process and in linguistic features that distinguish words’ functions and themes. To maximize our use of limited data, we reverse the typical schema induction steps and introduce new similarity measures, building an intuitive process for inducing the structure of unknown events.

Anthology ID:: W17-2710
Volume:: Proceedings of the Events and Stories in the News Workshop
Month:: August
Year:: 2017
Address:: Vancouver, Canada
Editors:: Tommaso Caselli, Ben Miller, Marieke van Erp, Piek Vossen, Martha Palmer, Eduard Hovy, Teruko Mitamura, David Caswell
Venue:: EventStory
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 66–76
Language:
URL:: https://aclanthology.org/W17-2710/
DOI:: 10.18653/v1/W17-2710
Bibkey:
Cite (ACL):: Natalie Ahn. 2017. Inducing Event Types and Roles in Reverse: Using Function to Discover Theme. In Proceedings of the Events and Stories in the News Workshop, pages 66–76, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):: Inducing Event Types and Roles in Reverse: Using Function to Discover Theme (Ahn, EventStory 2017)
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PDF:: https://aclanthology.org/W17-2710.pdf

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@inproceedings{ahn-2017-inducing,
    title = "Inducing Event Types and Roles in Reverse: Using Function to Discover Theme",
    author = "Ahn, Natalie",
    editor = "Caselli, Tommaso  and
      Miller, Ben  and
      van Erp, Marieke  and
      Vossen, Piek  and
      Palmer, Martha  and
      Hovy, Eduard  and
      Mitamura, Teruko  and
      Caswell, David",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-2710/",
    doi = "10.18653/v1/W17-2710",
    pages = "66--76",
    abstract = "With growing interest in automated event extraction, there is an increasing need to overcome the labor costs of hand-written event templates, entity lists, and annotated corpora. In the last few years, more inductive approaches have emerged, seeking to discover unknown event types and roles in raw text. The main recent efforts use probabilistic generative models, as in topic modeling, which are formally concise but do not always yield stable or easily interpretable results. We argue that event schema induction can benefit from greater structure in the process and in linguistic features that distinguish words' functions and themes. To maximize our use of limited data, we reverse the typical schema induction steps and introduce new similarity measures, building an intuitive process for inducing the structure of unknown events."
}

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%0 Conference Proceedings
%T Inducing Event Types and Roles in Reverse: Using Function to Discover Theme
%A Ahn, Natalie
%Y Caselli, Tommaso
%Y Miller, Ben
%Y van Erp, Marieke
%Y Vossen, Piek
%Y Palmer, Martha
%Y Hovy, Eduard
%Y Mitamura, Teruko
%Y Caswell, David
%S Proceedings of the Events and Stories in the News Workshop
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ahn-2017-inducing
%X With growing interest in automated event extraction, there is an increasing need to overcome the labor costs of hand-written event templates, entity lists, and annotated corpora. In the last few years, more inductive approaches have emerged, seeking to discover unknown event types and roles in raw text. The main recent efforts use probabilistic generative models, as in topic modeling, which are formally concise but do not always yield stable or easily interpretable results. We argue that event schema induction can benefit from greater structure in the process and in linguistic features that distinguish words’ functions and themes. To maximize our use of limited data, we reverse the typical schema induction steps and introduce new similarity measures, building an intuitive process for inducing the structure of unknown events.
%R 10.18653/v1/W17-2710
%U https://aclanthology.org/W17-2710/
%U https://doi.org/10.18653/v1/W17-2710
%P 66-76

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Markdown (Informal)

[Inducing Event Types and Roles in Reverse: Using Function to Discover Theme](https://aclanthology.org/W17-2710/) (Ahn, EventStory 2017)

Inducing Event Types and Roles in Reverse: Using Function to Discover Theme (Ahn, EventStory 2017)

ACL

Natalie Ahn. 2017. Inducing Event Types and Roles in Reverse: Using Function to Discover Theme. In Proceedings of the Events and Stories in the News Workshop, pages 66–76, Vancouver, Canada. Association for Computational Linguistics.