Pattabhi RK Rao

Also published as: Pattabhi RK Rao, T. Pattabhi R. K Rao


2024

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Finding the Causality of an Event in News Articles
Sobha Lalitha Devi | Pattabhi RK Rao
Proceedings of the 7th Workshop on Indian Language Data: Resources and Evaluation

This paper discusses about the finding of causality of an event in newspaper articles. The analysis of causality , otherwise known as cause and effect is crucial for building efficient Natural Language Understanding (NLU) supported AI systems such as Event tracking and it is considered as a complex semantic relation under discourse theory. A cause-effect relation consists of a linguistic marker and its two arguments. The arguments are semantic arguments where the cause is the first argument (Arg1) and the effect is the second argument(Arg2). In this work we have considered the causal relations in Tamil Newspaper articles. The analysis of causal constructions, the causal markers and their syntactic relation lead to the identification of different features for developing the language model using RBMs (Restricted Boltzmann Machine). The experiments we performed have given encouraging results. The Cause-Effect system developed is used in a mobile App for Event profiling called “Nigalazhvi” where the cause and effect of an event is identified and given to the user.

2023

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ChemXtract’ A System for Extraction of Chemical Events from Patent Documents
Pattabhi RK Rao | Sobha Lalitha Devi
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

ChemXtraxt main goal is to extract the chemical events from patent documents. Event extraction requires that we first identify the names of chemical compounds involved in the events. Thus, in this work two extractions are done and they are (a) names of chemical compounds and (b) event that identify the specific involvement of the chemical compounds in a chemical reaction. Extraction of essential elements of a chemical reaction, generally known as Named Entity Recognition (NER), extracts the compounds, condition and yields, their specific role in reaction and assigns a label according to the role it plays within a chemical reaction. Whereas event extraction identifies the chemical event relations between the chemical compounds identified. Here in this work we have used Neural Conditional Random Fields (NCRF), which combines the power of artificial neural network (ANN) and CRFs. Different levels of features that include linguistic, orthographical and lexical clues are used. The results obtained are encouraging.

2017

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Scalable Bio-Molecular Event Extraction System towards Knowledge Acquisition
Pattabhi RK Rao | Sindhuja Gopalan | Sobha Lalitha Devi
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

2015

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A Hybrid Discourse Relation Parser in CoNLL 2015
Sobha Lalitha Devi | Sindhuja Gopalan | Lakshmi S. | Pattabhi RK Rao | Vijay Sundar Ram | Malarkodi C.S.
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

2014

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A Generic Anaphora Resolution Engine for Indian Languages
Sobha Lalitha Devi | Vijay Sundar Ram | Pattabhi RK Rao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2012

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Tamil NER - Coping with Real Time Challenges
Malarkodi C.S | Pattabhi RK Rao | Sobha Lalitha Devi
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages

2011

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Hybrid Approach for Coreference Resolution
Lalitha Devi Sobha | Pattabhi RK Rao | R. Vijay Sundar Ram | CS. Malarkodi | A. Akilandeswari
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

2010

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How to Get the Same News from Different Language News Papers
T. Pattabhi R. K Rao | Sobha Lalitha Devi
Proceedings of the 4th Workshop on Cross Lingual Information Access