Papers by Anne Przewozny-Desriaux
Understanding Computational Models of Semantic Change: New Insights from the Speech Community (2023.emnlp-main)
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| Challenge: | Using type-level and token-level word embeddings, we obtain semantic change estimates from type-based models and empirical linguistic properties. |
| Approach: | They analyze 40 target words with type-level and token-level word embeddings, empirical linguistic properties, and speaker-provided acceptability ratings and qualitative remarks. |
| Outcome: | The proposed models are able to describe the sociolinguistic issue of contact-induced semantic shifts in Quebec English and are validated by qualitative interviews with 15 speakers from Montreal. |
Detecting Contact-Induced Semantic Shifts: What Can Embedding-Based Methods Do in Practice? (2021.emnlp-main)
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| Challenge: | Existing work on semantic change detection methods has focused on generic research questions and datasets, using them as a training ground for proof-of-concept studies. |
| Approach: | They propose to use type-level embeddings to detect new semantic shifts and token-level embeddeds to isolate regionally specific occurrences. |
| Outcome: | The proposed method is comparable to state-of-the-art on diachrony tasks, but it does not translate to practical value in detecting new semantic shifts. |
Collecting Tweets to Investigate Regional Variation in Canadian English (2020.lrec-1)
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| Challenge: | 78.8 million tweets, 1.3 billion words, and a focus on the dialect regions of Toronto, Montreal, and Vancouver are included in this study. |
| Approach: | They propose to use a 78.8-million-tweet, 1.3-billion-word corpus to study regional variation in Canadian English with a focus on the dialect regions of Toronto, Montreal, and Vancouver. |
| Outcome: | The proposed corpus mirrors national and regional specificities of Canadian English and provides sufficient aggregate and user-level data and maintains a reasonably balanced distribution of content across regions and users. |