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blamepipeline's Introduction

BlamePipeline

Implementation of the AAAI 2019 paper: Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks

Task

Given a news article, extract blame ties (who blames whom) between entities in the article.

Example

CCG with LM

*An example sentence from our dataset containing a blame tie. The red/bold words are entities involved in a blame tie, and the blue/italic words are supporting evidence that the blame tie exists.*

Dataset

• Source: New York Times/Wall Street Journal/USA Today

• Time period: 2007/10– 2010/06

USA NYT WSJ
days 310 736 648
articles 132 429 438
blame ties 353 787 754
Number value Ratio value
# of articles 998 Average -/+ ratio 2.19
# of samples 8562 Total -/+ ratio 3.61

Models

Context Model Context Model

Results

Model Dev F1 Test F1
Entity 61.07 60.06
Context 73.16 66.35
Combined 76.13 69.92

blamepipeline's People

Contributors

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Stargazers

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Watchers

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Forkers

lgw863 jmmaki

blamepipeline's Issues

Where is data?

Would it be possible to provide the data used in the experiments?

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