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1. Characterizing English Variation across Social Media Communities with BERT

   Lucy, Li; Bamman, David (January 2021, Transactions of the Association for Computational Linguistics)

   Daelemans, Walter (Ed.)

   Much previous work characterizing language variation across Internet social groups has focused on the types of words used by these groups. We extend this type of study by employing BERT to characterize variation in the senses of words as well, analyzing two months of English comments in 474 Reddit communities. The specificity of different sense clusters to a community, combined with the specificity of a community’s unique word types, is used to identify cases where a social group’s language deviates from the norm. We validate our metrics using user-created glossaries and draw on sociolinguistic theories to connect language variation with trends in community behavior. We find that communities with highly distinctive language are medium-sized, and their loyal and highly engaged users interact in dense networks. 

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2. ChainNet: Structured Metaphor and Metonymy in WordNet

   Maudslay, R; Teufel, S; Bond, F; Pustejovsky, J (May 2024, ELRA and ICCL)

   Calzolari, N; Kan, M; Hoste, V; Lenci, A; Sakti, S; Xue, N (Ed.)

   The senses of a word exhibit rich internal structure. In a typical lexicon, this structure is overlooked: A word`s senses are encoded as a list, without inter-sense relations. We present ChainNet, a lexical resource which for the first time explicitly identifies these structures, by expressing how senses in the Open English Wordnet are derived from one another. In ChainNet, every nominal sense of a word is either connected to another sense by metaphor or metonymy, or is disconnected (in the case of homonymy). Because WordNet senses are linked to resources which capture information about their meaning, ChainNet represents the first dataset of grounded metaphor and metonymy. 

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3. Which Evaluations Uncover Sense Representations that Actually Make Sense?

   Jordan Boyd-Graber, Fenfei Guo (May 2020, Proceedings of the 12th Language Resources and Evaluation Conference)

   null (Ed.)

   Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word’s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language’s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings. 

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4. Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction

   Jin, Lifeng; Schuler, William (January 2019, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics)

   In unsupervised grammar induction, data likelihood is known to be only weakly correlated with parsing accuracy, especially at convergence after multiple runs. In order to find a better indicator for quality of induced grammars, this paper correlates several linguistically- and psycholinguistically-motivated predictors to parsing accuracy on a large multilingual grammar induction evaluation data set. Results show that variance of average surprisal (VAS) better correlates with parsing accuracy than data likelihood and that using VAS instead of data likelihood for model selection provides a significant accuracy boost. Further evidence shows VAS to be a better candidate than data likelihood for predicting word order typology classification. Analyses show that VAS seems to separate content words from function words in natural language grammars, and to better arrange words with different frequencies into separate classes that are more consistent with linguistic theory. 

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5. Identifying the Desired Word Suggestion in Simultaneous Audio

   https://doi.org/10.1145/3733155.3733212  

   Gaines, Dylan; Vertanen, Keith (June 2025, ACM)

   We explore a method for presenting word suggestions for non-visual text input using simultaneous voices. We conduct two perceptual studies and investigate the impact of different presentations of voices on a user's ability to detect which voice, if any, spoke their desired word. Our sets of words simulated the word suggestions of a predictive keyboard during real-world text input. We find that when voices are simultaneous, user accuracy decreases significantly with each added word suggestion. However, adding a slight 0.15s delay between the start of each subsequent word allows two simultaneous words to be presented with no significant decrease in accuracy compared to presenting two words sequentially (84% simultaneous versus 86% sequential). This allows two word suggestions to be presented to the user 32% faster than sequential playback without decreasing accuracy. 

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