Understanding semantic change of words over centuries

By Derry Tanti Wijaya, Reyyan Yeniterzi. 2011

172 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cultural Studies, Statistical and Nonlinear Physics, Natural Language Processing, Computational Linguistics & Semantics

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Citations in the corpus

Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings (2019, background): “Recently short-term semantic changes have been studied, for example in Amazon Reviews (Kulkarni et al., 2015), scientiﬁc papers (Rudolph and Blei, 2018), news articles (Tang et al., 2016; Yao et al., 2018), and the UK Web Archive (Basile and McGillivray, 2018).”
That’s sick dude!: Automatic identification of word sense change across different timescales (2014, background): “…phenomena of change in word senses has existed ever since the beginning of human communication (Bamman and Crane, 2011; Michel et 1020 al., 2011; Wijaya and Yeniterzi, 2011; Mihalcea and Nastase, 2012); however, with the advent of modern technology and the availability of huge volumes of…”
Diachronic word embeddings and semantic shifts: a survey (2018, background): “In Wijaya and Yeniterzi (2011), it helped solve a typical digital humanities task of ﬁnding traces of real-world events in the texts.”
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (2019, background): “few words: case studies of individual words (Wijaya and Yeniterzi, 2011; Jatowt and Duh, 2014; Hamilton et al., 2016a), or a comparison between a few changing and semantically stable words (Lau et al.”
A Bayesian Model of Diachronic Meaning Change (2016, unknown): “Participating SemEval Systems We compared our model against three other systems which participated in the SemEval task.8 AMBRA (Zampieri et al., 2015) adopts a learning-to-rank modeling approach and uses several stylistic, grammatical, and lexical features.”
An automatic approach to identify word sense changes in text media across timescales (2015, background): “Ever since the emergence of human communication, words have gone through sense changes (Bamman and Crane 2011; Michel et al. 2011; Wijaya and Yeniterzi 2011; Mihalcea and Nastase 2012); however, with the advent of modern technology and the availability of huge volumes of diachronic data, this resear”
Diachronic degradation of language models: Insights from social media (2018, background): “For instance, a sentiment model trained on Victorian-era language would label ‘aweful’ as positive sentiment; however, in contemporary usage, ‘awful’ is considered a negative word (Wijaya and Yeniterzi, 2011).”
Semantic Variation in Online Communities of Practice (2018, unknown): “A variety of techniques have been explored: Latent Semantic Analysis (Sagi et al., 2011; Jatowt and Duh, 2014), topic clustering (Wijaya and Yeniterzi, 2011) and dynamic topic modelling (Frermann and Lapata, 2016).”
Entity Cloze By Date: What LMs Know About Unseen Entities (2022, background): “Another line of work has looked at diachronic embeddings: (Wijaya and Yeniterzi, 2011; Kim et al., 2014; Hamilton et al., 2016; Bamler and Mandt, 2017), which can model changing meanings of words over time.”
Statistically Significant Detection of Linguistic Change (2014, methodology): “Wijaya and Yeniterzi [39] study evolution of words using a topic modeling approach but do not suggest an explicit change point detection algorithm.”