Survey of Computational Approaches to Lexical Semantic Change
arXiv:1811.06278
Abstract
Our languages are in constant flux driven by external factors such as cultural, societal and technological changes, as well as by only partially understood internal motivations. Words acquire new meanings and lose old senses, new words are coined or borrowed from other languages and obsolete words slide into obscurity. Understanding the characteristics of shifts in the meaning and in the use of words is useful for those who work with the content of historical texts, the interested general public, but also in and of itself. The findings from automatic lexical semantic change detection, and the models of diachronic conceptual change are currently being incorporated in approaches for measuring document across-time similarity, information retrieval from long-term document archives, the design of OCR algorithms, and so on. In recent years we have seen a surge in interest in the academic community in computational methods and tools supporting inquiry into diachronic conceptual change and lexical replacement. This article is an extract of a survey of recent computational techniques to tackle lexical semantic change currently under review. In this article we focus on diachronic conceptual change as an extension of semantic change.
arXiv commentThis survey is an extended version of Survey of Computational Approaches to Diachronic Conceptual Change
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Cited by in corpus (15)
- Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
- Cultural Cartography with Word Embeddings
- GASC: Genre-Aware Semantic Change for Ancient Greek
- Challenges for Computational Lexical Semantic Change
- Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018
- Lexical semantic change for Ancient Greek and Latin
- SCoT: Sense Clustering over Time: a tool for the analysis of lexical change
- Time Masking for Temporal Language Models
- SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces
- Unsupervised Embedding-based Detection of Lexical Semantic Changes
- Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inference
- UoB at SemEval-2020 Task 1: Automatic Identification of Novel Word Senses
- Text analysis and deep learning: A network approach
- We went to look for meaning and all we got were these lousy representations: aspects of meaning representation for computational semantics
- Semantic coordinates analysis reveals language changes in the AI field