@inproceedings{lossio-ventura-etal-2016-automatic,
title = "Automatic Biomedical Term Polysemy Detection",
author = "Lossio-Ventura, Juan Antonio and
Jonquet, Clement and
Roche, Mathieu and
Teisseire, Maguelonne",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1266",
pages = "1684--1688",
abstract = "Polysemy is the capacity for a word to have multiple meanings. Polysemy detection is a first step for Word Sense Induction (WSI), which allows to find different meanings for a term. The polysemy detection is also important for information extraction (IE) systems. In addition, the polysemy detection is important for building/enriching terminologies and ontologies. In this paper, we present a novel approach to detect if a biomedical term is polysemic, with the long term goal of enriching biomedical ontologies. This approach is based on the extraction of new features. In this context we propose to extract features following two manners: (i) extracted directly from the text dataset, and (ii) from an induced graph. Our method obtains an Accuracy and F-Measure of 0.978.",
}
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<abstract>Polysemy is the capacity for a word to have multiple meanings. Polysemy detection is a first step for Word Sense Induction (WSI), which allows to find different meanings for a term. The polysemy detection is also important for information extraction (IE) systems. In addition, the polysemy detection is important for building/enriching terminologies and ontologies. In this paper, we present a novel approach to detect if a biomedical term is polysemic, with the long term goal of enriching biomedical ontologies. This approach is based on the extraction of new features. In this context we propose to extract features following two manners: (i) extracted directly from the text dataset, and (ii) from an induced graph. Our method obtains an Accuracy and F-Measure of 0.978.</abstract>
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%0 Conference Proceedings
%T Automatic Biomedical Term Polysemy Detection
%A Lossio-Ventura, Juan Antonio
%A Jonquet, Clement
%A Roche, Mathieu
%A Teisseire, Maguelonne
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F lossio-ventura-etal-2016-automatic
%X Polysemy is the capacity for a word to have multiple meanings. Polysemy detection is a first step for Word Sense Induction (WSI), which allows to find different meanings for a term. The polysemy detection is also important for information extraction (IE) systems. In addition, the polysemy detection is important for building/enriching terminologies and ontologies. In this paper, we present a novel approach to detect if a biomedical term is polysemic, with the long term goal of enriching biomedical ontologies. This approach is based on the extraction of new features. In this context we propose to extract features following two manners: (i) extracted directly from the text dataset, and (ii) from an induced graph. Our method obtains an Accuracy and F-Measure of 0.978.
%U https://aclanthology.org/L16-1266
%P 1684-1688
Markdown (Informal)
[Automatic Biomedical Term Polysemy Detection](https://aclanthology.org/L16-1266) (Lossio-Ventura et al., LREC 2016)
ACL
- Juan Antonio Lossio-Ventura, Clement Jonquet, Mathieu Roche, and Maguelonne Teisseire. 2016. Automatic Biomedical Term Polysemy Detection. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1684–1688, Portorož, Slovenia. European Language Resources Association (ELRA).