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Two Works
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Science IE: Conditional Statements
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Sequence Labeling for IE
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Multi-Output Sequence Labeling
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Sequence Labels
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Multi-Input Multi-Output Sequence Label...
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Evaluation
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Case Study (cont'd)
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Motivation (cont'd)
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System Pipeline
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Table Components
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Table Templates (cont'd)
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Problem Definition
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Ensemble Learning
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Assumption 1
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Learning-based Classifier
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Review: Tablepedia System
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Results (RecSys)
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Results: Asking ERD
Description:
Explore advanced techniques in scientific text mining and knowledge graph construction in this conference talk from KDD 2020. Delve into topics such as Science IE, conditional statements, sequence labeling for information extraction, and multi-output sequence labeling. Learn about evaluation methods and examine a detailed case study. Understand the motivation behind table extraction systems, explore system pipelines, and discover various table components and templates. Gain insights into problem definition, ensemble learning, and learning-based classifiers. Review the Tablepedia system and analyze results from recommender systems and entity resolution tasks.

Scientific Text Mining and Knowledge Graphs

Association for Computing Machinery (ACM)
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