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Found 14 papers, 0 papers with code
A Meta-framework for Spatiotemporal Quantity Extraction from Text
News events are often associated with quantities (e. g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events.
KnowSemLM: A Knowledge Infused Semantic Language Model
Story understanding requires developing expectations of what events come next in text.
Solving Hard Coreference Problems
Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions.
CogCompTime: A Tool for Understanding Time in Natural Language Text
Automatic extraction of temporal information in text is an important component of natural language understanding.
CogCompTime: A Tool for Understanding Time in Natural Language
Automatic extraction of temporal information is important for natural language understanding.
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow.
Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision
Neural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion.
Story Comprehension for Predicting What Happens Next
Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense.
Ranked #13 on
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on StoryCloze
A Joint Model for Semantic Sequences: Frames, Entities, Sentiments
Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task.
Two Discourse Driven Language Models for Semantics
Natural language understanding often requires deep semantic knowledge.
