Papers by Rajakrishnan Rajkumar
Dual Mechanism Priming Effects in Hindi Word Order (2022.aacl-main)
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| Challenge: | Existing studies have shown that word order choices can be primed by preceding sentences. |
| Approach: | They propose to model lexical priming and lexically-independent syntactic priming using a logistic regression model. |
| Outcome: | The proposed hypothesis supports multiple cognitive mechanisms . the experimental record shows that lexical priming and lexically-independent priming affect complementary sets of verb classes. |
Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech (N19-3)
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| Challenge: | a study aims to understand the role of disfluencies in speech production . speakers tend to lessen cognitive load for upcoming difficulties . |
| Approach: | They examine the role of three influential theories of language processing in predicting disfluencies in speech production. |
| Outcome: | The proposed classifiers predict disfluencies in English conversational speech . the classifier features lexical surprisal, word duration and DLT integration costs . |
Discourse Context Predictability Effects in Hindi Word Order (2022.emnlp-main)
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| Challenge: | Prior work has shown that information status, dependency length, and syntactic surprisal influence word order preferences, but the role of discourse predictability is underexplored in the literature. |
| Approach: | They propose to use Hindi-Urdu Treebank corpus to build a classifier to predict which sentences actually occurred in the corpus against artificially generated distractors. |
| Outcome: | The proposed classifier predicts which sentences occur in the Hindi-Urdu Treebank corpus against artificial distractors. |
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre (2022.coling-1)
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| Challenge: | linguistically motivated features are used to classify paragraph-level text into fiction and non-fiction genres. |
| Approach: | They deploy linguistically motivated features to classify paragraph-level text into fiction and non-fiction genres using a logistic regression model. |
| Outcome: | The proposed model gives 15.56% accuracy jump over baseline model . the proposed model also transfers over to another dataset, Baby BNC corpus . |