About: Interactive Extractive Search over Biomedical Corpora

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Interactive Extractive Search over Biomedical Corpora
Creator	Cohen, Yaara Eyal, Matan Goldberg, Yoav Lahav, Dan Sadde, Shoval »more»
source	ArXiv
abstract	We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to dependency-based search, we introduce a light-weight query language 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. Search is performed at an interactive speed due to efficient linguistic graph-indexing and retrieval engine. This allows for rapid exploration, development and refinement of user queries. We demonstrate the system using example workflows over two corpora: the PubMed corpus including 14,446,243 PubMed abstracts and the CORD-19 dataset, a collection of over 45,000 research papers focused on COVID-19 research. The system is publicly available at https://allenai.github.io/spike
has issue date	2020-06-07(xsd:dateTime)
has license	arxiv
sha1sum (hex)	e673f54c4005931fd3392bbf95c80a9f5024d5c4
resource representing a document's title	Interactive Extractive Search over Biomedical Corpora
resource representing a document's body	covid:e673f54c4005931fd3392bbf95c80a9f5024d5c4#body_text
is schema:about of	named entity 'INTERACTIVE' named entity 'LINGUISTIC' named entity 'COVID-19' named entity 'USING' named entity 'RETRIEVAL' »more»

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