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Found 8 papers, 1 papers with code
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre
We also compared the classification accuracy of the logistic regression model with two deep-learning models.
A bounded rationality account of dependency length minimization in Hindi
Additionally, for the task of distinguishing corpus sentences from counterfactual variants, we find that the dependency length and constituent length of the constituent closest to the main verb are much better predictors of whether a sentence appeared in the corpus than total dependency length.
Discourse Context Predictability Effects in Hindi Word Order
While prior work has shown that a number of factors (e. g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature.
Dual Mechanism Priming Effects in Hindi Word Order
By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.
A Simple Approach to Classify Fictional and Non-Fictional Genres
In this work, we deploy a logistic regression classifier to ascertain whether a given document belongs to the fiction or non-fiction genre.
Surprisal and Interference Effects of Case Markers in Hindi Word Order
Based on the Production-Distribution-Comprehension (PDC) account of language processing, we formulate two distinct hypotheses about case marking, word order choices and processing in Hindi.
Uniform Information Density Effects on Syntactic Choice in Hindi
According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 2010), speakers tend to distribute information density across the signal uniformly while producing language.
