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Computer Science

D-Index
39
Citations
12407
World Ranking
9512
National Ranking
4029

Overview

Ramesh Nallapati is affiliated with Amazon in the United States and has an extensive publication record primarily in computer science. Their research spans multiple subfields, including artificial intelligence, computer vision and pattern recognition, information systems, statistical and nonlinear physics, as well as sociology and political science.

The scientist's research focuses on a range of topics such as topic modeling, natural language processing techniques, multimodal machine learning applications, advanced text analysis techniques, software engineering research, machine learning and data classification, and complex network analysis techniques.

Notable recent papers by Ramesh Nallapati include:

  • Link-PLSA-LDA: A New Unsupervised Model for Topics and Influence of Blogs, 2021, Proceedings of the International AAAI Conference on Web and Social Media

Frequent co-authors in their work include:

  • Bing Xiang
  • Parminder Bhatia
  • Dejiao Zhang
  • Zhiguo Wang
  • Henghui Zhu

Ramesh Nallapati has published extensively in venues such as arXiv (Cornell University), with 24 publications, as well as in the Proceedings of the International AAAI Conference on Web and Social Media, the Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, the Proceedings of the AAAI Conference on Artificial Intelligence, and the Findings of the Association for Computational Linguistics: NAACL 2022.

Best Publications

  • Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

    Ramesh Nallapati;Bowen Zhou;Cicero Nogueira dos santos;Caglar Gulcehre

  • Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora

    Daniel Ramage;David Hall;Ramesh Nallapati;Christopher D. Manning

  • SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents

    Ramesh Nallapati;Feifei Zhai;Bowen Zhou

  • Multi-instance Multi-label Learning for Relation Extraction

    Mihai Surdeanu;Julie Tibshirani;Ramesh Nallapati;Christopher D. Manning

  • Pointing the unknown words

    Caglar Gulcehre;Sungjin Ahn;Ramesh Nallapati;Bowen Zhou

  • OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations

    Pramuditha Perera;Ramesh Nallapati;Bing Xiang

  • Joint latent topic models for text and citations

    Ramesh M. Nallapati;Amr Ahmed;Eric P. Xing;William W. Cohen

  • Discriminative models for information retrieval

    Ramesh Nallapati

  • Event threading within news topics

    Ramesh Nallapati;Ao Feng;Fuchun Peng;James Allan

  • A Comparative Study of Methods for Transductive Transfer Learning

    A. Arnold;R. Nallapati;W.W. Cohen

  • Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering

    Zhiguo Wang;Patrick Ng;Xiaofei Ma;Ramesh Nallapati

  • Sequence-to-Sequence RNNs for Text Summarization

    Ramesh Nallapati;Bing Xiang;Bowen Zhou

  • Link-PLSA-LDA: A New Unsupervised Model for Topics and Influence of Blogs.

    Ramesh Nallapati;William W. Cohen

  • Supporting Clustering with Contrastive Learning

    Dejiao Zhang;Feng Nan;Xiaokai Wei;Shang-Wen Li

  • A moving unstructured staggered mesh method for the simulation of incompressible free-surface flows

    Blair Perot;Ramesh Nallapati

  • Topic Modeling with Wasserstein Autoencoders

    Feng Nan;Ran Ding;Ramesh Nallapati;Bing Xiang

  • Classify or Select: Neural Architectures for Extractive Document Summarization

    Ramesh Nallapati;Bowen Zhou;Mingbo Ma

  • Capturing term dependencies using a language model based on sentence trees

    Ramesh Nallapati;James Allan

  • Elastic Machine Learning Algorithms in Amazon SageMaker

    Edo Liberty;Zohar Karnin;Bing Xiang;Laurence Rouesnel

  • Entity-level Factual Consistency of Abstractive Text Summarization.

    Feng Nan;Ramesh Nallapati;Zhiguo Wang;Cícero Nogueira dos Santos

  • Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability

    Ramesh Nallapati;William Cohen;John Lafferty

Frequent Co-Authors

Bing Xiang
Bing Xiang Amazon (United States)
Bowen Zhou
Bowen Zhou IBM (United States)
Kathleen R. McKeown
Kathleen R. McKeown Columbia University
James Allan
James Allan University of Massachusetts Amherst
William W. Cohen
William W. Cohen Carnegie Mellon University
Christopher D. Manning
Christopher D. Manning Stanford University
Peng Xu
Peng Xu Chinese Academy of Sciences
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)
Mihai Surdeanu
Mihai Surdeanu University of Arizona
Chen Sun
Chen Sun Google (United States)

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