Rajasekar Krishnamurthy


2021

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Development of an Enterprise-Grade Contract Understanding System
Arvind Agarwal | Laura Chiticariu | Poornima Chozhiyath Raman | Marina Danilevsky | Diman Ghazi | Ankush Gupta | Shanmukha Guttula | Yannis Katsis | Rajasekar Krishnamurthy | Yunyao Li | Shubham Mudgal | Vitobha Munigala | Nicholas Phan | Dhaval Sonawane | Sneha Srinivasan | Sudarshan R. Thitte | Mitesh Vasa | Ramiya Venkatachalam | Vinitha Yaski | Huaiyu Zhu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Contracts are arguably the most important type of business documents. Despite their significance in business, legal contract review largely remains an arduous, expensive and manual process. In this paper, we describe TECUS: a commercial system designed and deployed for contract understanding and used by a wide range of enterprise users for the past few years. We reflect on the challenges and design decisions when building TECUS. We also summarize the data science life cycle of TECUS and share lessons learned.

2020

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Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification
Prithviraj Sen | Marina Danilevsky | Yunyao Li | Siddhartha Brahma | Matthias Boehm | Laura Chiticariu | Rajasekar Krishnamurthy
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world. We present RuleNN, a neural network architecture for learning transparent models for sentence classification. The models are in the form of rules expressed in first-order logic, a dialect with well-defined, human-understandable semantics. More precisely, RuleNN learns linguistic expressions (LE) built on top of predicates extracted using shallow natural language understanding. Our experimental results show that RuleNN outperforms statistical relational learning and other neuro-symbolic methods, and performs comparably with black-box recurrent neural networks. Our user studies confirm that the learned LEs are explainable and capture domain semantics. Moreover, allowing domain experts to modify LEs and instill more domain knowledge leads to human-machine co-creation of models with better performance.

2012

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Towards Efficient Named-Entity Rule Induction for Customizability
Ajay Nagesh | Ganesh Ramakrishnan | Laura Chiticariu | Rajasekar Krishnamurthy | Ankush Dharkar | Pushpak Bhattacharyya
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2010

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Domain Adaptation of Rule-Based Annotators for Named-Entity Recognition Tasks
Laura Chiticariu | Rajasekar Krishnamurthy | Yunyao Li | Frederick Reiss | Shivakumar Vaithyanathan
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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SystemT: An Algebraic Approach to Declarative Information Extraction
Laura Chiticariu | Rajasekar Krishnamurthy | Yunyao Li | Sriram Raghavan | Frederick Reiss | Shivakumar Vaithyanathan
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2008

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Regular Expression Learning for Information Extraction
Yunyao Li | Rajasekar Krishnamurthy | Sriram Raghavan | Shivakumar Vaithyanathan | H. V. Jagadish
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing