Ramesh Nallapati

11.0k total citations · 4 hit papers
63 papers, 4.3k citations indexed

About

Ramesh Nallapati is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ramesh Nallapati has authored 63 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 7 papers in Information Systems. Recurrent topics in Ramesh Nallapati's work include Topic Modeling (43 papers), Natural Language Processing Techniques (28 papers) and Advanced Text Analysis Techniques (17 papers). Ramesh Nallapati is often cited by papers focused on Topic Modeling (43 papers), Natural Language Processing Techniques (28 papers) and Advanced Text Analysis Techniques (17 papers). Ramesh Nallapati collaborates with scholars based in United States, Israel and Germany. Ramesh Nallapati's co-authors include Christopher D. Manning, Bowen Zhou, Bing Xiang, David Hall, Daniel Ramage, Feifei Zhai, William W. Cohen, Pramuditha Perera, Mihai Surdeanu and Julie Tibshirani and has published in prestigious journals such as Journal of Computational Physics, Machine Vision and Applications and CaltechAUTHORS (California Institute of Technology).

In The Last Decade

Ramesh Nallapati

58 papers receiving 4.0k citations

Hit Papers

Labeled LDA 2009 2026 2014 2020 2009 2017 2012 2019 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Labeled LDA
    2009 848 citations Ramesh Nallapati, Christopher D. Manning et al. profile →
  2. SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents
    2017 625 citations Ramesh Nallapati et al. profile →
  3. Multi-instance Multi-label Learning for Relation Extraction
    2012 387 citations Mihai Surdeanu, Julie Tibshirani et al. Empirical Methods in Natural Language Processing profile →
  4. OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations
    2019 331 citations Ramesh Nallapati, Bing Xiang et al. profile →

Peers

Ramesh Nallapati
Amr Ahmed United States
Chenliang Li China
Xuanjing Huang China
Xiang Ren United States
Andrew Kachites McCallum United States
Min Yang China
Yangqiu Song China
Ting Liu China
Yiming Yang United States
Yūji Matsumoto Japan
Amr Ahmed United States
Ramesh Nallapati
Citations per year, relative to Ramesh Nallapati Ramesh Nallapati (= 1×) peers Amr Ahmed

Countries citing papers authored by Ramesh Nallapati

Since Specialization
Citations

This map shows the geographic impact of Ramesh Nallapati's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ramesh Nallapati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ramesh Nallapati more than expected).

Fields of papers citing papers by Ramesh Nallapati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ramesh Nallapati. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ramesh Nallapati. The network helps show where Ramesh Nallapati may publish in the future.

Co-authorship network of co-authors of Ramesh Nallapati

This figure shows the co-authorship network connecting the top 25 collaborators of Ramesh Nallapati. A scholar is included among the top collaborators of Ramesh Nallapati based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ramesh Nallapati. Ramesh Nallapati is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Athiwaratkun, Ben, Shiqi Wang, Mingyue Shang, et al.. (2024). Token Alignment via Character Matching for Subword Completion. 15725–15738.
2.
Qian, Haifeng, Sujan K. Gonugondla, Mingyue Shang, et al.. (2024). BASS: Batched Attention-optimized Speculative Sampling. 8214–8224.
3.
Yadav, Prateek, Qing Sun, Xiaopeng Li, et al.. (2023). Exploring Continual Learning for Code Generation Models. 782–792. 7 indexed citations
4.
Wang, Zijian, Ming Tan, Ramesh Nallapati, et al.. (2022). DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization. 203–211. 16 indexed citations
5.
Wang, Jun, Patrick Ng, Zhiguo Wang, et al.. (2022). Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding. 306–312. 1 indexed citations
6.
Zhang, Dejiao, Nan Feng, Xiaokai Wei, et al.. (2021). Supporting Clustering with Contrastive Learning. 5419–5430. 118 indexed citations
7.
Feng, Nan, Ramesh Nallapati, Zhiguo Wang, et al.. (2021). Entity-level Factual Consistency of Abstractive Text Summarization. 2727–2733. 68 indexed citations
8.
Zhang, Dejiao, Ramesh Nallapati, Henghui Zhu, et al.. (2020). Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling. 3527–3536. 5 indexed citations
9.
Santos, Cícero Nogueira dos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, & Bing Xiang. (2020). Beyond [CLS] through Ranking by Generation. 1722–1727. 23 indexed citations
10.
Shakeri, Siamak, Cícero Nogueira dos Santos, Henghui Zhu, et al.. (2020). End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems. 5445–5460. 36 indexed citations
11.
Zhang, Dejiao, Ramesh Nallapati, Henghui Zhu, et al.. (2020). Unsupervised Domain Adaptation for Cross-lingual Text Labeling.. Empirical Methods in Natural Language Processing. 3527–3536. 4 indexed citations
12.
Wang, Shen, Xiaokai Wei, Cícero Nogueira dos Santos, et al.. (2020). H2KGAT: Hierarchical hyperbolic knowledge graph attention network. Empirical Methods in Natural Language Processing. 4952–4962. 2 indexed citations
13.
Ma, Xiaofei, Peng Xu, Zhiguo Wang, Ramesh Nallapati, & Bing Xiang. (2019). Domain Adaptation with BERT-based Domain Classification and Data Selection. 76–83. 54 indexed citations
14.
Nallapati, Ramesh, et al.. (2012). Skierarchy: Extending the Power of Crowdsourcing Using a Hierarchy of Domain Experts, Crowd and Machine Learning.. Text REtrieval Conference. 2 indexed citations
15.
Surdeanu, Mihai, Julie Tibshirani, Ramesh Nallapati, & Christopher D. Manning. (2012). Multi-instance Multi-label Learning for Relation Extraction. Empirical Methods in Natural Language Processing. 455–465. 387 indexed citations breakdown →
16.
Akbacak, Murat, Robert C. Bolles, J. Brian Burns, et al.. (2011). The 2011 SESAME Multimedia Event Detection (MED) System. TRECVID. 1 indexed citations
17.
Nallapati, Ramesh, Daniel A. McFarland, & Christopher D. Manning. (2011). TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents. International Conference on Artificial Intelligence and Statistics. 543–551. 19 indexed citations
18.
Arnold, Andrew O., Ramesh Nallapati, & William W. Cohen. (2008). Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition. Meeting of the Association for Computational Linguistics. 245–253. 30 indexed citations
19.
Nallapati, Ramesh, Bruce Croft, & James Allan. (2003). Relevant query feedback in statistical language modeling. 1 indexed citations
20.
Perot, Blair & Ramesh Nallapati. (1999). Conservation Properties of Unstructured Staggered Mesh Methods. APS Division of Fluid Dynamics Meeting Abstracts.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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