Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915309014745088 |
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| author | Pranjić, Marko Dobrovoljc, Kaja Pollak, Senja Martinc, Matej |
| author_facet | Pranjić, Marko Dobrovoljc, Kaja Pollak, Senja Martinc, Matej |
| contents | In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers. Detecting and tracking semantic changes provides insight into the evolution of language caused by changes in society and culture. We present the first Slovene dataset for evaluating semantic change detection systems, which contains aggregated semantic change scores for 104 target words obtained from more than 3,000 manually annotated sentence pairs. We analyze an important class of measures of semantic change metrics based on the Average pairwise distance and identify several limitations. To address these limitations, we propose a novel metric based on regularized optimal transport, which offers a more robust framework for quantifying semantic change. We provide a comprehensive evaluation of various existing semantic change detection methods and associated semantic change measures on our dataset. Through empirical testing, we demonstrate that our proposed approach, leveraging regularized optimal transport, achieves either matching or improved performance compared to baseline approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance Pranjić, Marko Dobrovoljc, Kaja Pollak, Senja Martinc, Matej Computation and Language I.2.7 In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers. Detecting and tracking semantic changes provides insight into the evolution of language caused by changes in society and culture. We present the first Slovene dataset for evaluating semantic change detection systems, which contains aggregated semantic change scores for 104 target words obtained from more than 3,000 manually annotated sentence pairs. We analyze an important class of measures of semantic change metrics based on the Average pairwise distance and identify several limitations. To address these limitations, we propose a novel metric based on regularized optimal transport, which offers a more robust framework for quantifying semantic change. We provide a comprehensive evaluation of various existing semantic change detection methods and associated semantic change measures on our dataset. Through empirical testing, we demonstrate that our proposed approach, leveraging regularized optimal transport, achieves either matching or improved performance compared to baseline approaches. |
| title | Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance |
| topic | Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2402.16596 |