Implementation of the AAAI 2019 paper: Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
Given a news article, extract blame ties (who blames whom) between entities in the article.
*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.*• 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 |
Model | Dev F1 | Test F1 |
---|---|---|
Entity | 61.07 | 60.06 |
Context | 73.16 | 66.35 |
Combined | 76.13 | 69.92 |