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. Our first hypothesis is that Hindi tends to optimize for processing efficiency at both lexical and syntactic levels. We quantify the role of case markers in this process. For the task of predicting the reference sentence occurring in a corpus (amidst meaning-equivalent grammatical variants) using a machine learning model, surprisal estimates from an artificial version of the language (i.e., Hindi without any case markers) result in lower prediction accuracy compared to natural Hindi. Our second hypothesis is that Hindi tends to minimize interference due to case markers while ordering preverbal constituents. We show that Hindi tends to avoid placing next to each other constituents whose heads are marked by identical case inflections. Our findings adhere to PDC assumptions and we discuss their implications for language production, learning and universals.

In this work, we deploy a logistic regression classifier to ascertain whether a given document belongs to the fiction or non-fiction genre. For genre identification, previous work had proposed three classes of features, viz., low-level (character-level and token counts), high-level (lexical and syntactic information) and derived features (type-token ratio, average word length or average sentence length). Using the Recursive feature elimination with cross-validation (RFECV) algorithm, we perform feature selection experiments on an exhaustive set of nineteen features (belonging to all the classes mentioned above) extracted from Brown corpus text. As a result, two simple features viz., the ratio of the number of adverbs to adjectives and the number of adjectives to pronouns turn out to be the most significant. Subsequently, our classification experiments aimed towards genre identification of documents from the Brown and Baby BNC corpora demonstrate that the performance of a classifier containing just the two aforementioned features is at par with that of a classifier containing the exhaustive feature set.

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. The prior works cited above studied syntactic reduction in language production at particular choice points in a sentence. In contrast, we use a variant of the above UID hypothesis in order to investigate the extent to which word order choices in Hindi are influenced by the drive to minimize the variance of information across entire sentences. To this end, we propose multiple lexical and syntactic measures (at both word and constituent levels) to capture the uniform spread of information across a sentence. Subsequently, we incorporate these measures in machine learning models aimed to distinguish between a naturally occurring corpus sentence and its grammatical variants (expressing the same idea). Our results indicate that our UID measures are not a significant factor in predicting the corpus sentence in the presence of lexical surprisal, a competing control predictor. Finally, in the light of other recent works, we conclude with a discussion of reasons for UID not being suitable for a theory of word order.