Neoveille, a Web Platform for Neologism Tracking

Cartier, Emmanuel

doi:10.18653/v1/e17-3024

Cited by 11 publications

(11 citation statements)

References 9 publications

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“…Other large bodies of research involve identifying the strength and emergence of neologisms for the purposes of advancing or understanding a single language [6,34]. More recent work using wordembeddings to explore neologisms have demonstrated promise in both detecting and contextualising neologisms [37,41].…”

Section: Computational Approachesmentioning

confidence: 99%

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On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere

Farrell

Araque

Fernández

et al. 2020

12th ACM Conference on Web Science

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Understanding the identities, needs, realities and development of subcultures has been a long term target of sociology and cultural studies. Socio-cultural linguistics, in particular, examines the use of language and, in particular, the existence and use of neologisms, slang and jargon. These terms capture concepts and expressions that are not in common use and represent the new realities, norms and values of subcommunities. Identifying and understanding such terms, however, is a very complex task, particularly considering the vast amount of content that is currently available online for many such groups. In this paper, we propose a combination of computational and socio-linguistic methods to automatically extract new terminology from large amounts of data, using word-embeddings to semantically contextualise their meaning. As a use case, we explore subculture on the platform Reddit. More specifically, we investigate groups considered part of the manosphere, a loose online community where men's perspectives, gripes, frustrations and desires are explicitly expressed and where women are typically targets of hostility. Characterisations of this group as a subculture are then provided, based on an in-depth analysis of the identified jargon. CCS CONCEPTS• Computing methodologies → Information extraction; Topic modeling; • Theory of computation → Random projections and metric embeddings.

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Section: Computational Approachesmentioning

confidence: 99%

“…More recent work using wordembeddings to explore neologisms have demonstrated promise in both detecting and contextualising neologisms [37,41]. It has also opened new avenues for conducting analyses on non-English corpora [32] 6 .…”

Section: Computational Approachesmentioning

confidence: 99%

On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere

Farrell

Araque

Fernández

et al. 2020

12th ACM Conference on Web Science

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“…Modern corpora like the NOW corpus ( Davies 2013 ) allow to study more comprehensive samples of neologisms and enable researchers to monitor their use over time, which is essential for investigating diffusion processes. In addition to general-purpose web corpora, several research groups built dedicated tools and specialized corpora for the monitoring and analysis of neologisms ( Renouf et al, 2007 ; Kerremans et al, 2012 ; Lemnitzer, 2010 ; Gérard et al, 2017 ; Cartier 2017 ).…”

Section: Modelling and Measuring The Diffusion Of Lexical Innovationsmentioning

confidence: 99%

Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter

Würschinger¹

2021

Front. Artif. Intell.

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Societies continually evolve and speakers use new words to talk about innovative products and practices. While most lexical innovations soon fall into disuse, others spread successfully and become part of the lexicon. In this paper, I conduct a longitudinal study of the spread of 99 English neologisms on Twitter to study their degrees and pathways of diffusion. Previous work on lexical innovation has almost exclusively relied on usage frequency for investigating the spread of new words. To get a more differentiated picture of diffusion, I use frequency-based measures to study temporal aspects of diffusion and I use network analyses for a more detailed and accurate investigation of the sociolinguistic dynamics of diffusion. The results show that frequency measures manage to capture diffusion with varying success. Frequency counts can serve as an approximate indicator for overall degrees of diffusion, yet they miss important information about the temporal usage profiles of lexical innovations. The results indicate that neologisms with similar total frequency can exhibit significantly different degrees of diffusion. Analysing differences in their temporal dynamics of use with regard to their age, trends in usage intensity, and volatility contributes to a more accurate account of their diffusion. The results obtained from the social network analysis reveal substantial differences in the social pathways of diffusion. Social diffusion significantly correlates with the frequency and temporal usage profiles of neologisms. However, the network visualisations and metrics identify neologisms whose degrees of social diffusion are more limited than suggested by their overall frequency of use. These include, among others, highly volatile neologisms (e.g., poppygate) and political terms (e.g., alt-left), whose use almost exclusively goes back to single communities of closely-connected, like-minded individuals. I argue that the inclusion of temporal and social information is of particular importance for the study of lexical innovation since neologisms exhibit high degrees of temporal volatility and social indexicality. More generally, the present approach demonstrates the potential of social network analysis for sociolinguistic research on linguistic innovation, variation, and change.

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“…Computational neologism. Much previous computational work on neologisms focused on automatic recognition of neologisms and their meanings (Cook and Stevenson, 2010;Cartier, 2017;Costin-Gabriel and Rebedea, 2014;Veale and Butnariu, 2010;Kerremans and Prokić, 2018). Work on computational generation of neologisms mostly focused on creating compounds and word blends from source words (Smith et al, 2014;Deri and Knight, 2015;Gangal et al, 2017;Kulkarni and Wang, 2018;Özbal and Strapparava, 2012;Simon, 2018).…”

Section: Related Workmentioning

confidence: 99%

Coming to Terms: Automatic Formation of Neologisms in Hebrew

Mizrahi

Seelig²,

Shahaf

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Spoken languages are ever-changing, with new words entering them all the time. However, coming up with new words (neologisms) today relies exclusively on human creativity. In this paper we propose a system to automatically suggest neologisms. We focus on the Hebrew language as a test case due to the unusual regularity of its noun formation. User studies comparing our algorithm to experts and nonexperts demonstrate that our algorithm is capable of generating high-quality outputs, as well as enhance human creativity. More broadly, we seek to inspire more computational work around the topic of linguistic creativity, which we believe offers numerous unexplored opportunities.

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Neoveille, a Web Platform for Neologism Tracking

Cited by 11 publications

References 9 publications

On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere

On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere

Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter

Coming to Terms: Automatic Formation of Neologisms in Hebrew

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