Jeffrey Flanigan

2.6k total citations
16 papers, 624 citations indexed

About

Jeffrey Flanigan is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jeffrey Flanigan has authored 16 papers receiving a total of 624 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jeffrey Flanigan's work include Topic Modeling (13 papers), Natural Language Processing Techniques (11 papers) and Machine Learning in Bioinformatics (2 papers). Jeffrey Flanigan is often cited by papers focused on Topic Modeling (13 papers), Natural Language Processing Techniques (11 papers) and Machine Learning in Bioinformatics (2 papers). Jeffrey Flanigan collaborates with scholars based in United States. Jeffrey Flanigan's co-authors include Noah A. Smith, Sam Thomson, Chris Dyer, Jaime Carbonell, Fei Liu, Norman Sadeh, Nathan Schneider, Brendan O’Connor, Dani Yogatama and Dipanjan Das and has published in prestigious journals such as Edinburgh Research Explorer, Figshare and North American Chapter of the Association for Computational Linguistics.

In The Last Decade

Jeffrey Flanigan

13 papers receiving 559 citations

Peers

Countries citing papers authored by Jeffrey Flanigan

Since Specialization

Citations

This map shows the geographic impact of Jeffrey Flanigan'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 Jeffrey Flanigan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey Flanigan more than expected).

Fields of papers citing papers by Jeffrey Flanigan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeffrey Flanigan. 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 Jeffrey Flanigan. The network helps show where Jeffrey Flanigan may publish in the future.

Co-authorship network of co-authors of Jeffrey Flanigan

This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey Flanigan. A scholar is included among the top collaborators of Jeffrey Flanigan 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 Jeffrey Flanigan. Jeffrey Flanigan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

#	Work	Indexed citations
1	Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel 2024 ·(unknown), Jeffrey Flanigan	1
2	Task Contamination: Language Models May Not Be Few-Shot Anymore 2024 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Changmao Li, Jeffrey Flanigan	18
3	Large Language Model Unlearning via Embedding-Corrupted Prompts 2024 ·Jeffrey Flanigan, (unknown), Yang Liu, (unknown)	0
4	Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking 2023 ·(unknown), Jeffrey Flanigan	5
5	Automatic Identification of Code-Switching Functions in Speech Transcripts 2023 ·(unknown), Jeffrey Flanigan	0
6	Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers 2022 ·Changmao Li, Jeffrey Flanigan	4
7	DocAMR: Multi-Sentence AMR Representation and Evaluation 2022 ·Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ·Tahira Naseem, (unknown), (unknown), Tim O’Gorman, Young‐Suk Lee, Jeffrey Flanigan, Ramón Fernández Astudillo, Radu Florian, Salim Roukos, Nathan Schneider	5
8	Avoiding Overlap in Data Augmentation for AMR-to-Text Generation 2021 ·Wenchao Du, Jeffrey Flanigan	1
9	Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments 2018 ·Figshare ·Kevin Gimpel, Nathan Schneider, Brendan O’Connor, Dipanjan Das, (unknown), Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, Noah A. Smith	157
10	Generation from Abstract Meaning Representation using Tree Transducers 2016 ·Jeffrey Flanigan, Chris Dyer, Noah A. Smith, Jaime Carbonell	50
11	CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss 2016 ·Jeffrey Flanigan, Chris Dyer, Noah A. Smith, Jaime Carbonell	28
12	Toward Abstractive Summarization Using Semantic Representations 2015 ·Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, Noah A. Smith	148
13	The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP 2015 ·Edinburgh Research Explorer ·Nathan Schneider, Jeffrey Flanigan, Tim O’Gorman	2
14	A Discriminative Graph-Based Parser for the Abstract Meaning Representation 2014 ·Figshare ·Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, Chris Dyer, Noah A. Smith	186
15	CMU: Arc-Factored, Discriminative Semantic Dependency Parsing 2014 ·Figshare ·Sam Thomson, Brendan O’Connor, Jeffrey Flanigan, David Bamman, Jesse Dodge, Swabha Swayamdipta, Nathan Schneider, Chris Dyer, Noah A. Smith	8
16	Large-Scale Discriminative Training for Statistical Machine Translation Using Held-Out Line Search 2013 ·North American Chapter of the Association for Computational Linguistics ·Jeffrey Flanigan, Chris Dyer, Jaime Carbonell	11

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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