| 1. |
- Hengchen, Simon, 1988, et al.
(author)
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Challenges for computational lexical semantic change
- 2021
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In: Computational approaches to semantic change / Tahmasebi, Nina, Borin, Lars, Jatowt, Adam, Yang, Xu, Hengchen, Simon (eds.). - Berlin : Language Science Press. - 2366-7818. - 9783985540082 ; , s. 341-372
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Book chapter (peer-reviewed)abstract
- The computational study of lexical semantic change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics. Most of the research so far has focused on methods for modelling and detecting semantic change using large diachronic textual data, with the majority of the approaches employing neural embeddings. While methods that offer easy modelling of diachronic text are one of the main reasons for the spiking interest in LSC, neural models leave many aspects of the problem unsolved. The field has several open and complex challenges. In this chapter, we aim to describe the most important of these challenges and outline future directions.
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| 2. |
- Perrone, Valerio, et al.
(author)
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Lexical semantic change for Ancient Greek and Latin
- 2021
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In: Computational approaches to semantic change. - Berlin : Language Science Press. - 9783985540082 ; , s. 287-310
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Book chapter (peer-reviewed)abstract
- Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associating a word with its correct meaning in its historical context is a central challenge in diachronic research. Historical corpora of classical languages, such as Ancient Greek and Latin, typically come with rich metadata, and existing models are limited by their inability to exploit contextual information beyond the document timestamp. While embedding-based methods feature among the current state of the art systems, they are lacking in their interpretative power. In contrast, Bayesian models provide explicit and interpretable representations of semantic change phenomena. In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model. In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on text genres. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-ofthe-art embedding-based models. On top of providing a full description of meaning change over time, we show that Bayesian mixture models are highly competitive approaches to detect binary semantic change in both Ancient Greek and Latin.
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| 3. |
- Petersson, Stellan, 1982, et al.
(author)
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Semantic change in Swedish – from a lexicographic perspective
- 2021
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In: Computational approaches to semantic change. Eds.: Nina Tahmasebi, Lars Borin, Adam Jatowt, Yang Xu & Simon Hengchen. - Berlin : Language Science Press. - 9783985540082 ; , s. 149-167
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Book chapter (peer-reviewed)abstract
- In this chapter, we examine semantic change in the general vocabulary of present-day Swedish and its lexicographic description. We discuss the question of whether automatic and semi-automatic methods of computational linguistics are relevant to lexicography and conclude that such methods can facilitate, formalize, and sharpen lexicographic investigations of semantic change.
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