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        <title>A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms</title>
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    <name type="personal">
        <namePart type="given">Mustafa</namePart>
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    <name type="personal">
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    <abstract>This paper addresses the task of extending a given synset with additional synonyms taking into account synonymy strength as a fuzzy value. Given a mono/multilingual synset and a threshold (a fuzzy value [0−1]), our goal is to extract new synonyms above this threshold from existing lexicons. We present twofold contributions: an algorithm and a benchmark dataset. The dataset consists of 3K candidate synonyms for 500 synsets. Each candidate synonym is annotated with a fuzzy value by four linguists. The dataset is important for (i) understanding how much linguists (dis/)agree on synonymy, in addition to (ii) using the dataset as a baseline to evaluate our algorithm. Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate. Our evaluations show that the algorithm behaves like a linguist and its fuzzy values are close to those proposed by linguists (using RMSE and MAE). The dataset and a demo page are publicly available at https://portal.sina.birzeit.edu/synonyms.</abstract>
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%0 Conference Proceedings
%T A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms
%A Ghanem, Sana
%A Jarrar, Mustafa
%A Jarrar, Radi
%A Bounhas, Ibrahim
%Y Rigau, German
%Y Bond, Francis
%Y Rademaker, Alexandre
%S Proceedings of the 12th Global Wordnet Conference
%D 2023
%8 January
%I Global Wordnet Association
%C University of the Basque Country, Donostia - San Sebastian, Basque Country
%F ghanem-etal-2023-benchmark
%X This paper addresses the task of extending a given synset with additional synonyms taking into account synonymy strength as a fuzzy value. Given a mono/multilingual synset and a threshold (a fuzzy value [0−1]), our goal is to extract new synonyms above this threshold from existing lexicons. We present twofold contributions: an algorithm and a benchmark dataset. The dataset consists of 3K candidate synonyms for 500 synsets. Each candidate synonym is annotated with a fuzzy value by four linguists. The dataset is important for (i) understanding how much linguists (dis/)agree on synonymy, in addition to (ii) using the dataset as a baseline to evaluate our algorithm. Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate. Our evaluations show that the algorithm behaves like a linguist and its fuzzy values are close to those proposed by linguists (using RMSE and MAE). The dataset and a demo page are publicly available at https://portal.sina.birzeit.edu/synonyms.
%U https://aclanthology.org/2023.gwc-1.34
%P 274-283

Markdown (Informal)

[A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms](https://aclanthology.org/2023.gwc-1.34) (Ghanem et al., GWC 2023)

• A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms (Ghanem et al., GWC 2023)

ACL

• Sana Ghanem, Mustafa Jarrar, Radi Jarrar, and Ibrahim Bounhas. 2023. A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms. In Proceedings of the 12th Global Wordnet Conference, pages 274–283, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.