@inproceedings{nigam-etal-2020-varying,
title = "Varying Vector Representations and Integrating Meaning Shifts into a {P}age{R}ank Model for Automatic Term Extraction",
author = {Nigam, Anurag and
H{\"a}tty, Anna and
Schulte im Walde, Sabine},
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.540",
pages = "4388--4394",
abstract = "We perform a comparative study for automatic term extraction from domain-specific language using a PageRank model with different edge-weighting methods. We vary vector space representations within the PageRank graph algorithm, and we go beyond standard co-occurrence and investigate the influence of measures of association strength and first- vs. second-order co-occurrence. In addition, we incorporate meaning shifts from general to domain-specific language as personalized vectors, in order to distinguish between termhood strengths of ambiguous words across word senses. Our study is performed for two domain-specific English corpora: ACL and do-it-yourself (DIY); and a domain-specific German corpus: cooking. The models are assessed by applying average precision and the roc score as evaluation metrices.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>We perform a comparative study for automatic term extraction from domain-specific language using a PageRank model with different edge-weighting methods. We vary vector space representations within the PageRank graph algorithm, and we go beyond standard co-occurrence and investigate the influence of measures of association strength and first- vs. second-order co-occurrence. In addition, we incorporate meaning shifts from general to domain-specific language as personalized vectors, in order to distinguish between termhood strengths of ambiguous words across word senses. Our study is performed for two domain-specific English corpora: ACL and do-it-yourself (DIY); and a domain-specific German corpus: cooking. The models are assessed by applying average precision and the roc score as evaluation metrices.</abstract>
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%0 Conference Proceedings
%T Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction
%A Nigam, Anurag
%A Hätty, Anna
%A Schulte im Walde, Sabine
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F nigam-etal-2020-varying
%X We perform a comparative study for automatic term extraction from domain-specific language using a PageRank model with different edge-weighting methods. We vary vector space representations within the PageRank graph algorithm, and we go beyond standard co-occurrence and investigate the influence of measures of association strength and first- vs. second-order co-occurrence. In addition, we incorporate meaning shifts from general to domain-specific language as personalized vectors, in order to distinguish between termhood strengths of ambiguous words across word senses. Our study is performed for two domain-specific English corpora: ACL and do-it-yourself (DIY); and a domain-specific German corpus: cooking. The models are assessed by applying average precision and the roc score as evaluation metrices.
%U https://aclanthology.org/2020.lrec-1.540
%P 4388-4394
Markdown (Informal)
[Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction](https://aclanthology.org/2020.lrec-1.540) (Nigam et al., LREC 2020)
ACL