Rajakrishnan Rajkumar

Also published as: Rajkumar Rajakrishnan


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Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre
Arman Kazmi | Sidharth Ranjan | Arpit Sharma | Rajakrishnan Rajkumar
Proceedings of the 29th International Conference on Computational Linguistics

This work deploys linguistically motivated features to classify paragraph-level text into fiction and non-fiction genre using a logistic regression model and infers lexical and syntactic properties that distinguish the two genres. Previous works have focused on classifying document-level text into fiction and non-fiction genres, while in this work, we deal with shorter texts which are closer to real-world applications like sentiment analysis of tweets. Going beyond simple POS tag ratios proposed in Qureshi et al.(2019) for document-level classification, we extracted multiple linguistically motivated features belonging to four categories: Lexical features, POS ratio features, Syntactic features and Raw features. For the task of short-text classification, a model containing 28 best-features (selected via Recursive feature elimination with cross-validation; RFECV) confers an accuracy jump of 15.56 % over a baseline model consisting of 2 POS-ratio features found effective in previous work (cited above). The efficacy of the above model containing a linguistically motivated feature set also transfers over to another dataset viz, Baby BNC corpus. We also compared the classification accuracy of the logistic regression model with two deep-learning models. A 1D CNN model gives an increase of 2% accuracy over the logistic Regression classifier on both corpora. And the BERT-base-uncased model gives the best classification accuracy of 97% on Brown corpus and 98% on Baby BNC corpus. Although both the deep learning models give better results in terms of classification accuracy, the problem of interpreting these models remains unsolved. In contrast, regression model coefficients revealed that fiction texts tend to have more character-level diversity and have lower lexical density (quantified using content-function word ratios) compared to non-fiction texts. Moreover, subtle differences in word order exist between the two genres, i.e., in fiction texts Verbs precede Adverbs (inter-alia).

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Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech
Sidharth Ranjan | Rajakrishnan Rajkumar | Sumeet Agarwal
Proceedings of the Society for Computation in Linguistics 2022

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Dual Mechanism Priming Effects in Hindi Word Order
Sidharth Ranjan | Marten van Schijndel | Sumeet Agarwal | Rajakrishnan Rajkumar
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Word order choices during sentence production can be primed by preceding sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is driven by multiple different sources. Using a Hindi corpus of text productions, we model lexical priming with an n-gram cache model, and we capture more abstract syntactic priming with an adaptive neural language model. We permute the preverbal constituents of corpus sentences and then use a logistic regression model to predict which sentences actually occurred in the corpus against artificially generated meaning-equivalent variants. Our results indicate that lexical priming and lexically-independent syntactic priming affect complementary sets of verb classes. By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.


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Effects of Duration, Locality, and Surprisal in Speech Disfluency Prediction in English Spontaneous Speech
Samvit Dammalapati | Rajakrishnan Rajkumar | Sumeet Agarwal
Proceedings of the Society for Computation in Linguistics 2021


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Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech
Samvit Dammalapati | Rajakrishnan Rajkumar | Sumeet Agarwal
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

This study examines the role of three influential theories of language processing, viz., Surprisal Theory, Uniform Information Density (UID) hypothesis and Dependency Locality Theory (DLT), in predicting disfluencies in speech production. To this end, we incorporate features based on lexical surprisal, word duration and DLT integration and storage costs into logistic regression classifiers aimed to predict disfluencies in the Switchboard corpus of English conversational speech. We find that disfluencies occur in the face of upcoming difficulties and speakers tend to handle this by lessening cognitive load before disfluencies occur. Further, we see that reparandums behave differently from disfluent fillers possibly due to the lessening of the cognitive load also happening in the word choice of the reparandum, i.e., in the disfluency itself. While the UID hypothesis does not seem to play a significant role in disfluency prediction, lexical surprisal and DLT costs do give promising results in explaining language production. Further, we also find that as a means to lessen cognitive load for upcoming difficulties speakers take more time on words preceding disfluencies, making duration a key element in understanding disfluencies.

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Surprisal and Interference Effects of Case Markers in Hindi Word Order
Sidharth Ranjan | Sumeet Agarwal | Rajakrishnan Rajkumar
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

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.

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A Simple Approach to Classify Fictional and Non-Fictional Genres
Mohammed Rameez Qureshi | Sidharth Ranjan | Rajakrishnan Rajkumar | Kushal Shah
Proceedings of the Second Workshop on Storytelling

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.


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Uniform Information Density Effects on Syntactic Choice in Hindi
Ayush Jain | Vishal Singh | Sidharth Ranjan | Rajakrishnan Rajkumar | Sumeet Agarwal
Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing

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.


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Quantifying sentence complexity based on eye-tracking measures
Abhinav Deep Singh | Poojan Mehta | Samar Husain | Rajkumar Rajakrishnan
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

Eye-tracking reading times have been attested to reflect cognitive processes underlying sentence comprehension. However, the use of reading times in NLP applications is an underexplored area of research. In this initial work we build an automatic system to assess sentence complexity using automatically predicted eye-tracking reading time measures and demonstrate the efficacy of these reading times for a well known NLP task, namely, readability assessment. We use a machine learning model and a set of features known to be significant predictors of reading times in order to learn per-word reading times from a corpus of English text having reading times of human readers. Subsequently, we use the model to predict reading times for novel text in the context of the aforementioned task. A model based only on reading times gave competitive results compared to the systems that use extensive syntactic features to compute linguistic complexity. Our work, to the best of our knowledge, is the first study to show that automatically predicted reading times can successfully model the difficulty of a text and can be deployed in practical text processing applications.


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Minimal Dependency Length in Realization Ranking
Michael White | Rajakrishnan Rajkumar
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning


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A Word Reordering Model for Improved Machine Translation
Karthik Visweswariah | Rajakrishnan Rajkumar | Ankur Gandhe | Ananthakrishnan Ramanathan | Jiri Navratil
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Linguistically Motivated Complementizer Choice in Surface Realization
Rajakrishnan Rajkumar | Michael White
Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop

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The OSU System for Surface Realization at Generation Challenges 2011
Rajakrishnan Rajkumar | Dominic Espinosa | Michael White
Proceedings of the 13th European Workshop on Natural Language Generation


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Designing Agreement Features for Realization Ranking
Rajakrishnan Rajkumar | Michael White
Coling 2010: Posters

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Further Meta-Evaluation of Broad-Coverage Surface Realization
Dominic Espinosa | Rajakrishnan Rajkumar | Michael White | Shoshana Berleant
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing


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Exploiting Named Entity Classes in CCG Surface Realization
Rajakrishnan Rajkumar | Michael White | Dominic Espinosa
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Grammar Engineering for CCG using Ant and XSLT
Scott Martin | Rajakrishnan Rajkumar | Michael White
Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing (SETQA-NLP 2009)

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Perceptron Reranking for CCG Realization
Michael White | Rajakrishnan Rajkumar
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing


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A More Precise Analysis of Punctuation for Broad-Coverage Surface Realization with CCG
Michael White | Rajakrishnan Rajkumar
Coling 2008: Proceedings of the workshop on Grammar Engineering Across Frameworks


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Towards broad coverage surface realization with CCG
Michael White | Rajakrishnan Rajkumar | Scott Martin
Proceedings of the Workshop on Using corpora for natural language generation