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Solving Hard Coreference Problems
TLDR
This paper presents a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the state of the art level on standard coreferenceresolution datasets. Expand
Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning
TLDR
CrossMap is presented, a novel cross-modal representation learning method that uncovers urban dynamics with massive GTSM data and significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency. Expand
Story Comprehension for Predicting What Happens Next
TLDR
This paper presents a story comprehension model that explores three distinct semantic aspects: the sequence of events described in the story, its emotional trajectory, and its plot consistency, and uses a hidden variable to weigh the semantic aspects in the context of the story. Expand
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
TLDR
It is shown that existing temporal extraction systems can be improved via this resource and that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. Expand
A Joint Framework for Coreference Resolution and Mention Head Detection
TLDR
An ILP-based joint coreference resolution and mention head formulation that is shown to yield significant improvements on coreference from raw text, outperforming existing state-ofart systems on both the ACE-2004 and the CoNLL-2012 datasets. Expand
Event Detection and Co-reference with Minimal Supervision
TLDR
This paper develops an event detection and co-reference system with minimal supervision that outperform state-of-the-art supervised methods for event coreference on benchmark data sets, and support significantly better transfer across domains. Expand
CogCompTime: A Tool for Understanding Time in Natural Language
TLDR
This paper introduces CogCompTime, a system that has these two important functionalities and incorporates the most recent progress, achieves state-of-the-art performance, and is publicly available at http://cogcomp.org/page/publication_view/844. Expand
Two Discourse Driven Language Models for Semantics
TLDR
Two distinct models that capture semantic frame chains and discourse information while abstracting over the specific mentions of predicates and entities are developed. Expand
A Scalable Approach to Column-Based Low-Rank Matrix Approximation
TLDR
This work addresses the column-based low-rank matrix approximation problem using a novel parallel approach based on the divide-and-combine idea that enjoys a theoretical relative-error upper bound and is scalable on large-scale matrices. Expand
Cross-Lingual Dataless Classification for Many Languages
TLDR
This paper uses CLESA (cross-lingual explicit semantic analysis) to embed both foreign language documents and an English label space into a shared semantic space, and select the best label(s) for a document using the similarity between the corresponding semantic representations. Expand
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