Papers by Dayne Freitag

3 papers
Schema-Driven Information Extraction from Heterogeneous Tables (2024.findings-emnlp)

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Challenge: Existing work on information extraction from tables has focused on developing custom pipelines for each table collection.
Approach: They propose a task that transforms tabular data into structured records following a human-authored schema.
Outcome: The proposed task achieves F1 scores ranging from 74.2 to 96.1 while maintaining cost efficiency.
SynKB: Semantic Search for Synthetic Procedures (2022.emnlp-demos)

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Challenge: SynKB is an open-source, automatically extracted knowledge base of chemical synthesis protocols.
Approach: They propose to make SynKB available as an open-source tool for chemists . synKB supports more flexible queries about reaction conditions .
Outcome: The proposed open-source tool has higher recall and high precision than proprietary chemistry databases.
Valet: Rule-Based Information Extraction for Rapid Deployment (2022.lrec-1)

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Challenge: a number of machine learning models can be trained to perform sentence-level information extraction at accuracies ranging from strong to adequate.
Approach: They propose a Python framework for rule-based information extraction that allows for complex matching.
Outcome: The proposed framework can be used to perform rule-based information extraction on examples.

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