Papers by Haoruo Peng

3 papers
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource (N18-1)

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Challenge: Existing temporal extraction systems that extract temporal relations can be improved by using a resource that provides prior knowledge of the temporal order that events usually follow.
Approach: They propose to use a probabilistic knowledge base acquired in the news domain to extract temporal relations between events from the New York Times articles over a 20-year span.
Outcome: The proposed system and resource are both publicly available.
CogCompTime: A Tool for Understanding Time in Natural Language (D18-2)

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Challenge: Existing systems that extract temporal information from text can be useful for natural language understanding.
Approach: They propose a system that extracts temporal information from text and normalizes it to a standard format.
Outcome: The proposed system achieves state-of-the-art performance and incorporates the most recent progress.
A Meta-framework for Spatiotemporal Quantity Extraction from Text (2022.acl-long)

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Challenge: a meta-framework for news events that extracts quantities from text is proposed . a previous work on news events focused on extracting event mentions, attributes, and relationships .
Approach: They propose a meta-framework for solving the NLP problem of spatiotemporal quantity extraction . they demonstrate the framework is general and extensible, and shareable crowdsourcing pipeline and baseline models are used .
Outcome: The proposed framework is general and extensible, the authors say . it can extract quantity from news streams, quickly respond to emergencies, investigate incidents .

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