A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
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| Challenge: | Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison. |
| Approach: | They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction. |
| Outcome: | The proposed model can be utilised for the detection of domain-specific sense divergences in the field of term extraction. |
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| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
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| Challenge: | Existing methods for lexical semantic-change detection quantify changes in the meaning of words over time. |
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| Challenge: | Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing. |
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| Challenge: | Lexical semantic change detection is a new and innovative research field. |
| Approach: | They propose to pre-train on large corpora and refine on diachronic target corpors to improve performance. |
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Lexical Semantic Change Discovery (2021.acl-long)
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| Challenge: | Existing approaches to Lexical Semantic Change Detection are limited. |
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| Challenge: | Existing algorithms to detect semantic shifts have been criticized for their difficulty in evaluating them. |
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| Challenge: | Existing methods for detecting semantic change only measure the level of individual usage instances. |
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Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change (N18-2)
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| Challenge: | Existing frameworks for evaluating lexical semantic change are limited . evaluation of lexicals is a major obstacle in the field of semantic change detection . |
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