Workshop on Computational Approaches to Historical Language Change最新文献_第3页

Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 2020 使用神经主题模型跟踪单词的上下文变化:以2020年4月封锁前后的covid - 19相关术语为例研究

Workshop on Computational Approaches to Historical Language Change Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.lchange-1.14

Olga Kellert, M. Zaman

引用次数: 5

Modeling the Evolution of Word Senses with Force-Directed Layouts of Co-occurrence Networks 用共现网络的力向布局来模拟词义的演化

Workshop on Computational Approaches to Historical Language Change Pub Date : 1900-01-01 DOI: 10.18653/v1/2021.lchange-1.8

T. Reke, Robert Schwanhold, Ralf Krestel

引用次数: 0

black[LSCDiscovery shared task] GlossReader at LSCDiscovery: Train to Select a Proper Gloss in English – Discover Lexical Semantic Change in Spanish black[LSCDiscovery共享任务]:在英语中选择适当的光泽训练-发现西班牙语词汇语义变化

Workshop on Computational Approaches to Historical Language Change Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.lchange-1.22

M. Rachinskiy, N. Arefyev

{"title":"black[LSCDiscovery shared task] \u0000 GlossReader at LSCDiscovery: Train to Select a Proper Gloss in English – Discover Lexical Semantic Change in Spanish","authors":"M. Rachinskiy, N. Arefyev","doi":"10.18653/v1/2022.lchange-1.22","DOIUrl":"https://doi.org/10.18653/v1/2022.lchange-1.22","url":null,"abstract":"The contextualized embeddings obtained from neural networks pre-trained as Language Models (LM) or Masked Language Models (MLM) are not well suitable for solving the Lexical Semantic Change Detection (LSCD) task because they are more sensitive to changes in word forms rather than word meaning, a property previously known as the word form bias or orthographic bias. Unlike many other NLP tasks, it is also not obvious how to fine-tune such models for LSCD. In order to conclude if there are any differences between senses of a particular word in two corpora, a human annotator or a system shall analyze many examples containing this word from both corpora. This makes annotation of LSCD datasets very labour-consuming. The existing LSCD datasets contain up to 100 words that are labeled according to their semantic change, which is hardly enough for fine-tuning. To solve these problems we fine-tune the XLM-R MLM as part of a gloss-based WSD system on a large WSD dataset in English. Then we employ zero-shot cross-lingual transferability of XLM-R to build the contextualized embeddings for examples in Spanish. In order to obtain the graded change score for each word, we calculate the average distance between our improved contextualized embeddings of its old and new occurrences. For the binary change detection subtask, we apply thresholding to the same scores. Our solution has shown the best results among all other participants in all subtasks except for the optional sense gain detection subtask.","PeriodicalId":120650,"journal":{"name":"Workshop on Computational Approaches to Historical Language Change","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124183846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model 用跨语言词性标注部分重构经典语言谱系树模型

Workshop on Computational Approaches to Historical Language Change Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.lchange-1.8

Anat Samohi, Daniel Weisberg Mitelman, Kfir Bar

引用次数: 0

Deconstructing destruction: A Cognitive Linguistics perspective on a computational analysis of diachronic change 解构破坏:历时变化计算分析的认知语言学视角

Workshop on Computational Approaches to Historical Language Change Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.lchange-1.3

Karlien Franco, Mariana Montes, K. Heylen

引用次数: 0

“Vaderland”, “Volk” and “Natie”: Semantic Change Related to Nationalism in Dutch Literature Between 1700 and 1880 Captured with Dynamic Bernoulli Word Embeddings “Vaderland”、“Volk”和“native”:用动态伯努利词嵌入捕捉1700 - 1880年荷兰文学中与民族主义相关的语义变化

Workshop on Computational Approaches to Historical Language Change Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.lchange-1.13

Marije Timmermans, Eva Vanmassenhove, D. Shterionov

{"title":"“Vaderland”, “Volk” and “Natie”: Semantic Change Related to Nationalism in Dutch Literature Between 1700 and 1880 Captured with Dynamic Bernoulli Word Embeddings","authors":"Marije Timmermans, Eva Vanmassenhove, D. Shterionov","doi":"10.18653/v1/2022.lchange-1.13","DOIUrl":"https://doi.org/10.18653/v1/2022.lchange-1.13","url":null,"abstract":"Languages can respond to external events in various ways - the creation of new words or named entities, additional senses might develop for already existing words or the valence of words can change. In this work, we explore the semantic shift of the Dutch words “natie” (“nation”), “volk” (“people”) and “vaderland” (“fatherland”) over a period that is known for the rise of nationalism in Europe: 1700-1880. The semantic change is measured by means of Dynamic Bernoulli Word Embeddings which allow for comparison between word embeddings over different time slices. The word embeddings were generated based on Dutch fiction literature divided over different decades. From the analysis of the absolute drifts, it appears that the word “natie” underwent a relatively small drift. However, the drifts of “vaderland’” and “volk”’ show multiple peaks, culminating around the turn of the nineteenth century. To verify whether this semantic change can indeed be attributed to nationalistic movements, a detailed analysis of the nearest neighbours of the target words is provided. From the analysis, it appears that “natie”, “volk” and “vaderlan”’ became more nationalistically-loaded over time.","PeriodicalId":120650,"journal":{"name":"Workshop on Computational Approaches to Historical Language Change","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model 用BERT模型预测18世纪文本的可解释出版年份

Workshop on Computational Approaches to Historical Language Change Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.lchange-1.7

Iiro Rastas, Yann Ciarán Ryan, Iiro Tiihonen, Mohammadreza Qaraei, Liina Repo, Rohit Babbar, E. Mäkelä, M. Tolonen, Filip Ginter

引用次数: 9