Papers with Information Extraction task

4 papers
Multi-lingual Entity Discovery and Linking (P18-5)

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Challenge: This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task.
Approach: This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task.
Outcome: The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task.
Semantic Context Path Labeling for Semantic Exploration of User Reviews (2021.emnlp-demo)

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Challenge: a prototype system for semantic exploration of user reviews is presented . the system enables effective navigation in a rich contextual semantic schema .
Approach: They propose a system that extracts rich and diverse information from informative texts . they use a task to assign types and semantic roles to entities in the reviews .
Outcome: The proposed system can extract rich and diverse information from informative texts . it can be used to explore large quantities of user reviews, which contain useful information .
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)

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Challenge: Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources.
Approach: They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages.
Outcome: The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions.
Breaking Writer’s Block: Low-cost Fine-tuning of Natural Language Generation Models (2021.eacl-demos)

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Challenge: Currently, it is standard procedure to fine-tune large pre-trained language models for information extraction tasks, but this is not the case for generation tasks, which relies on a variety of techniques for controlled language generation.
Approach: They propose a system that fine-tunes a natural language generation model for the problem of solving writer’s block.
Outcome: The proposed system obtains excellent results even with a small number of epochs and a total cost of USD 150.

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