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Abstract-ZedSevcikovaSehyr_IntlSign.mp4"It’s a Small World" of Signs: Semantic Network Structure in American Sign Language
"It’s a Small World" of Signs: Semantic Network Structure in American Sign Language
May 19, 2025, 5 pm CEST
May 19, 2025, 5 pm CEST
How is meaning structured in the mind? While decades of research on spoken languages have shown that words are organized into highly efficient “small-world” semantic networks, much less is known about how sign languages organize conceptual knowledge. We constructed large-scale semantic networks for American Sign Language (ASL) using a free sign association task, in which Deaf signers produced the first three signs that came to mind in response to a cue sign. Data were collected using a custom-built platform for sign language responses and annotation, resulting in over 100,000 cue–response pairs from 45 Deaf ASL signers. We analyzed the structure of the ASL semantic lexicon using network metrics such as clustering, average shortest path length, and modularity, and compared these properties to spoken English using data from the Small World of Words (SWOW) project. While both networks exhibited small-world characteristics, the ASL network was more modular and sparsely connected, indicating a more compartmentalized structure. Measures of semantic density and response diversity were positively related to lexical frequency, and phonological neighborhood density was also positively correlated with semantic density. These findings suggest that while signed and spoken languages share core organizing principles, language modality may shape how meaning is structured and accessed..
Machine Learning Tools for Exploring Compositionality in the American SL Lexicon
Machine Learning Tools for Exploring Compositionality in the American SL Lexicon
June 30, 2025, 5 pm CEST
June 30, 2025, 5 pm CEST
Highly accurate and precise models of sub-lexical compositionality with high coverage over the lexicon could be a valuable tool in studying processes like acquisition, lexicalization, and specific cases of comprehension (like neologisms and classifier predicates). However, characterizing the systematic relationships between form and meaning in sign language lexicons can be challenging, not simply because annotations from humans are expensive to obtain, but also because those relationships are often probabilistic and vary across individuals and contexts. This type of pattern recognition is well within the scope of machine learning (ML) methodology, and for American SL, we now have sufficient data to empirically test to where and to what extent ML methods can learn compositionality as a task unto itself. This seminar will introduce several open-source tools designed for linguists and cognitive scientists interested in studying compositionality at scale, with emphasis on models that automatically identify the phonological and lexical-semantic features of isolated sign productions, especially signs that the ML model has never seen before. Experimental results support the hypothesis that machine learning models can internalize certain form-meaning relationships, and that this skill can be helpful in (a) predicting the average age of acquisition, (b) predicting free associations from 41 early-acquisition deaf signers, (c) approximating the meaning of unseen signs (in isolation), and (d) reproducing the process of new sign creation. Future work will attempt to improve the operationalization of lexical semantics and apply these tools to sentence-level data.
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