Papers by Hillel Taub-Tabib

6 papers
Hierarchy Builder: Organizing Textual Spans into a Hierarchy to Facilitate Navigation (2023.acl-demo)

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Challenge: Information extraction systems produce hundreds to thousands of strings on a specific topic.
Approach: They propose a method that allows users to consume a large collection of related textual strings in an exploratory mode.
Outcome: The proposed method allows users to consume a large collection of related textual strings in an exploratory mode.
Large Scale Substitution-based Word Sense Induction (2022.acl-long)

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Challenge: Word forms are ambiguous, and derive meaning from the context in which they appear . word sense induction can be performed over a corpus-derived sense inventory .
Approach: They propose a word-sense induction method based on pre-trained masked language models . they train a static word embeddings algorithm on the sense-tagged corpus .
Outcome: The proposed method outperforms existing senseful embeddings methods on Wikipedia and on an outlier detection dataset.
Syntactic Search by Example (2020.acl-demos)

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Challenge: a new system allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs.
Approach: They propose a query language that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs.
Outcome: The proposed system searches the English wikipedia and English pubmed abstracts at a rapid speed.
A Dataset for N-ary Relation Extraction of Drug Combinations (2022.naacl-main)

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Challenge: Combination therapies are becoming standard of care for diseases such as cancer, tuberculosis, malaria and HIV.
Approach: They construct an expert-annotated dataset for extracting drug combinations from the scientific literature.
Outcome: The proposed dataset is the first relation extraction dataset consisting of variable-length relations.
Neural Extractive Search (2021.acl-demo)

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Challenge: a domain expert often needs to extract structured information from large corpora.
Approach: They propose a search paradigm called "extractive search" that extends search queries with capture-slots to allow for rapid extraction.
Outcome: The proposed search paradigm can be extended with neural similarity techniques.
Bootstrapping Relation Extractors using Syntactic Search by Examples (2021.eacl-main)

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Challenge: Existing methods for supervised relation extraction still require a large quantity of training data.
Approach: They propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts.
Outcome: The proposed method outperforms models trained on manual and distant data augmentation techniques and the search-based approach with the NLG method.

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