@inproceedings{taub-tabib-etal-2020-interactive,
title = "Interactive Extractive Search over Biomedical Corpora",
author = "Taub Tabib, Hillel and
Shlain, Micah and
Sadde, Shoval and
Lahav, Dan and
Eyal, Matan and
Cohen, Yaara and
Goldberg, Yoav",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.bionlp-1.3",
doi = "10.18653/v1/2020.bionlp-1.3",
pages = "28--37",
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 \url{https://allenai.github.io/spike}",
}
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<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</abstract>
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%0 Conference Proceedings
%T Interactive Extractive Search over Biomedical Corpora
%A Taub Tabib, Hillel
%A Shlain, Micah
%A Sadde, Shoval
%A Lahav, Dan
%A Eyal, Matan
%A Cohen, Yaara
%A Goldberg, Yoav
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F taub-tabib-etal-2020-interactive
%X 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
%R 10.18653/v1/2020.bionlp-1.3
%U https://aclanthology.org/2020.bionlp-1.3
%U https://doi.org/10.18653/v1/2020.bionlp-1.3
%P 28-37
Markdown (Informal)
[Interactive Extractive Search over Biomedical Corpora](https://aclanthology.org/2020.bionlp-1.3) (Taub Tabib et al., BioNLP 2020)
ACL
- Hillel Taub Tabib, Micah Shlain, Shoval Sadde, Dan Lahav, Matan Eyal, Yaara Cohen, and Yoav Goldberg. 2020. Interactive Extractive Search over Biomedical Corpora. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 28–37, Online. Association for Computational Linguistics.