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1
Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation ...
Abstract: Constructing knowledge graphs from unstructured text is an important task that is relevant to many domains. Most previous work focuses on extracting information from sentences or paragraphs, due to the difficulty of analyzing longer contexts. In this paper we propose a new jointly trained model that can be used for various information extraction tasks at the document level. The tasks performed in this paper are entity and event identification, typing, and coreference resolution. In order to improve entity and event extraction, we utilize context-aware representations aggregated from the detected mentions of the corresponding entities and event triggers across the entire document. By extending our system to document-level, we can improve our results by incorporating cross-sentence dependencies and additional contextual information that might not be available at the sentence level, which allows for more globally optimized predictions. We evaluate our system on documents from the ACE05-E+ dataset and find ...
URL: https://underline.io/lecture/30052-joint-detection-and-coreference-resolution-of-entities-and-events-with-document-level-context-aggregation
https://dx.doi.org/10.48448/sees-jz22
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2
Stage-wise Fine-tuning for Graph-to-Text Generation ...
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3
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction ...
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4
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection ...
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5
Learning Shared Semantic Space for Speech-to-Text Translation ...
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6
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference ...
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7
Event-Centric Natural Language Processing ...
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8
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension ...
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9
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation ...
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