{"title":"HSE在TempoWiC:用历时语言模型检测社交媒体的意义转移","authors":"Elizaveta Tukhtina, Kseniia Kashleva, Svetlana Vydrina","doi":"10.18653/v1/2022.evonlp-1.6","DOIUrl":null,"url":null,"abstract":"This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.","PeriodicalId":158578,"journal":{"name":"Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HSE at TempoWiC: Detecting Meaning Shift in Social Media with Diachronic Language Models\",\"authors\":\"Elizaveta Tukhtina, Kseniia Kashleva, Svetlana Vydrina\",\"doi\":\"10.18653/v1/2022.evonlp-1.6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.\",\"PeriodicalId\":158578,\"journal\":{\"name\":\"Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2022.evonlp-1.6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2022.evonlp-1.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
HSE at TempoWiC: Detecting Meaning Shift in Social Media with Diachronic Language Models
This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
