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This paper introduces a new embedding model designed to capture preferential similarity rather than just semantic similarity, improving preference prediction for collective decision-making systems.
Researchers use four-state Markov chains to model vowel/consonant patterns in Pushkin’s Evgenij Onegin and its Italian translation, revealing structural asymmetries and narrative-linked phonological cues.
Researchers from EPFL and Idiap apply NLP methods (topic modeling, sentiment analysis, readability scoring) to over 2000 hyper-local news articles to assess how well local French-language media serves migrant communities. The study combines focus groups with computational text analysis to identify gaps between local news content and migrant readers' needs.