Mr. Depei Wang Profile

Depei Wang

at Guangdong Univ of Technology

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 November 2020 Paper

Event detection without trigger words on movie scripts

Weichuan Xu, Weiwen Zhang, Depei Wang

Proceedings Volume 11584, 115841G (2020) https://doi.org/10.1117/12.2577626

KEYWORDS: Computer programming, Performance modeling, Transformers, Neural networks, Network architectures, Mining, Feature extraction

Read Abstract +

Event extraction is a challenging task in text mining. Current research on event extraction is often based on the sentence level, while the research based on the document level is less investigated. In this paper, we propose a deep learning model, termed BLMA, which combines Bi-LSTM and Multi-head weighted Attention to detect multiple event types. Unlike traditional methods, BLMA can directly detect events in texts without trigger words. We regard the event detection task of the chapter as a multi-label classification problem and apply the BLMA onto the application of detecting events in movie scripts. Experiment results show that BLMA performs well on an automatically tagged screenplay dataset containing 11 event types. F1 score of event detection using BLMA reaches 91.6% for a chapter with a single event, and 93.9% with multiple events, which outperforms other models. Therefore, our deep learning approach can effectively extract events from a chapter, which can be used for event detection on movie scripts. In addition, we explore the contribution of each component of the BLMA to the results, and find that multi-head attention has a significant impact on the performance. Keywords: Event Detection, Bi-LSTM, Attention.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years