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Syntactic Search by Example
- Micah Shlain, Hillel Taub-Tabib, Shoval Sadde, Yoav Goldberg
- Computer ScienceAnnual Meeting of the Association for…
- 4 June 2020
A light-weight query language is introduced that does not require the user to know the details of the underlying syntactic representations, and instead to query the corpus by providing an example sentence coupled with simple markup.
Interactive Extractive Search over Biomedical Corpora
- Hillel Taub-Tabib, Micah Shlain, Yoav Goldberg
- Computer ScienceBIONLP
- 7 June 2020
A light-weight query language is introduced that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup, allowing for rapid exploration, development and refinement of user queries.
Template Kernels for Dependency Parsing
- Hillel Taub-Tabib, Yoav Goldberg, A. Globerson
- Computer ScienceNAACL
- 2015
This work presents a novel kernel which facilitates efficient parsing with feature representations corresponding to a much larger set of combinations, and integrates into a parse reranking system and demonstrates its effectiveness on four languages from the CoNLL-X shared task.
A Dataset for N-ary Relation Extraction of Drug Combinations
- A. Tiktinsky, Vijay Viswanathan, Yoav Goldberg
- Biology, Computer ScienceNAACL
- 4 May 2022
An expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature, and presents a unique NLP challenge, as the first relation extraction dataset consisting of variable-length relations.
Bootstrapping Relation Extractors using Syntactic Search by Examples
- Matan Eyal, Asaf Amrami, Hillel Taub-Tabib, Yoav Goldberg
- Computer ScienceEACL
- 9 February 2021
This work proposes a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts and takes advantage of search engines over syntactic-graphs to obtain positive examples by searching for sentences that are syntactically similar to user input examples.
Neural Extractive Search
- Shauli Ravfogel, Hillel Taub-Tabib, Yoav Goldberg
- Computer ScienceACL
- 8 June 2021
The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential.
Extending the Boundaries of Cancer Therapeutic Complexity with Literature Data Mining
- Danna Niezni, Hillel Taub-Tabib, Y. Shamay
- MedicinebioRxiv
- 30 October 2022
A TDM based assistive technology, cancer plan builder (CPB), which is made publicly available and allows experts to create literature-anchored high complexity combination treatment (HCCT) plans of significantly larger size and it is shown that experts using CPB are able to create HCCT plans at much greater speed and quality, compared to experts without CPB.
Large Scale Substitution-based Word Sense Induction
- Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav Goldberg
- Computer ScienceACL
- 14 October 2021
A word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora, and which allows to induce corpora-specific senses, which may not appear in standard sense inventories, is presented.
Rapid Knowledgebase Construction and Hypotheses Generation Using Extractive Literature Search
- Shaked Launer-Wachs, Hillel Taub-Tabib, Yoav Goldberg, Y. Shamay
- Computer SciencebioRxiv
- 15 February 2022
This work presents a methodology and a supporting tool to allow individual researchers or small teams, without background in bio-curation or computer science, to mine the scientific literature and construct ad-hoc, personalized, and literature-anchored knowledgebases, that are tailored around their specific research interests and support their scientific goals.