The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914665716514816 |
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| author | Schlechtweg, Dominik Virk, Shafqat Mumtaz Sander, Pauline Sköldberg, Emma Linke, Lukas Theuer Zhang, Tuo Tahmasebi, Nina Kuhn, Jonas Walde, Sabine Schulte im |
| author_facet | Schlechtweg, Dominik Virk, Shafqat Mumtaz Sander, Pauline Sköldberg, Emma Linke, Lukas Theuer Zhang, Tuo Tahmasebi, Nina Kuhn, Jonas Walde, Sabine Schulte im |
| contents | We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface. The tool supports standardized human annotation as well as computational annotation, building on recent advances with Word-in-Context models. Annotator judgments are clustered with automatic graph clustering techniques and visualized for analysis. This allows to measure word senses with simple and intuitive micro-task judgments between use pairs, requiring minimal preparation efforts. The tool offers additional functionalities to compare the agreement between annotators to guarantee the inter-subjectivity of the obtained judgments and to calculate summary statistics giving insights into sense frequency distributions, semantic variation or changes of senses over time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_12664 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change Schlechtweg, Dominik Virk, Shafqat Mumtaz Sander, Pauline Sköldberg, Emma Linke, Lukas Theuer Zhang, Tuo Tahmasebi, Nina Kuhn, Jonas Walde, Sabine Schulte im Computation and Language Artificial Intelligence We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface. The tool supports standardized human annotation as well as computational annotation, building on recent advances with Word-in-Context models. Annotator judgments are clustered with automatic graph clustering techniques and visualized for analysis. This allows to measure word senses with simple and intuitive micro-task judgments between use pairs, requiring minimal preparation efforts. The tool offers additional functionalities to compare the agreement between annotators to guarantee the inter-subjectivity of the obtained judgments and to calculate summary statistics giving insights into sense frequency distributions, semantic variation or changes of senses over time. |
| title | The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2311.12664 |