Srini Narayanan

Also published as: Srinivas Narayanan


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Points, Paths, and Playscapes: Large-scale Spatial Language Understanding Tasks Set in the Real World
Jason Baldridge | Tania Bedrax-Weiss | Daphne Luong | Srini Narayanan | Bo Pang | Fernando Pereira | Radu Soricut | Michael Tseng | Yuan Zhang
Proceedings of the First International Workshop on Spatial Language Understanding

Spatial language understanding is important for practical applications and as a building block for better abstract language understanding. Much progress has been made through work on understanding spatial relations and values in images and texts as well as on giving and following navigation instructions in restricted domains. We argue that the next big advances in spatial language understanding can be best supported by creating large-scale datasets that focus on points and paths based in the real world, and then extending these to create online, persistent playscapes that mix human and bot players, where the bot players must learn, evolve, and survive according to their depth of understanding of scenes, navigation, and interactions.


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Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning
Ekaterina Shutova | Lin Sun | Elkin Darío Gutiérrez | Patricia Lichtenstein | Srini Narayanan
Computational Linguistics, Volume 43, Issue 1 - April 2017

Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing (NLP) applications. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the systems hard to scale. Recent years have witnessed a rise in statistical approaches to metaphor processing. However, these approaches often require extensive human annotation effort and are predominantly evaluated within a limited domain. In contrast, we experiment with weakly supervised and unsupervised techniques—with little or no annotation—to generalize higher-level mechanisms of metaphor from distributional properties of concepts. We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings. Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups—English, Spanish, and Russian—achieving state-of-the-art results with little supervision. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor.


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Bridging Text and Knowledge with Frames
Srini Narayanan
Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014)


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Catching Metaphors
Matt Gedigian | John Bryant | Srini Narayanan | Branimir Ciric
Proceedings of the Third Workshop on Scalable Natural Language Understanding


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Question Answering Based on Semantic Structures
Srini Narayanan | Sanda Harabagiu
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Answering Questions Using Advanced Semantics and Probabilistic Inference
Srini Narayanan | Sanda Harabagiu
Proceedings of the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004


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Putting FrameNet Data into the ISO Linguistic Annotation Framework
Srinivas Narayanan | Miriam R. L. Petruck | Collin F. Baker | Charles J. Fillmore
Proceedings of the ACL 2003 Workshop on Linguistic Annotation: Getting the Model Right

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Semantic Extraction with Wide-Coverage Lexical Resources
Behrang Mohit | Srini Narayanan
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers


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Putting Frames in Perspective
Nancy Chang | Srini Narayanan | Miriam R.L. Petruck
COLING 2002: The 19th International Conference on Computational Linguistics