Edison Marrese-Taylor

956 total citations
31 papers, 539 citations indexed

About

Edison Marrese-Taylor is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Sociology and Political Science. According to data from OpenAlex, Edison Marrese-Taylor has authored 31 papers receiving a total of 539 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 6 papers in Sociology and Political Science. Recurrent topics in Edison Marrese-Taylor's work include Topic Modeling (17 papers), Natural Language Processing Techniques (13 papers) and Sentiment Analysis and Opinion Mining (9 papers). Edison Marrese-Taylor is often cited by papers focused on Topic Modeling (17 papers), Natural Language Processing Techniques (13 papers) and Sentiment Analysis and Opinion Mining (9 papers). Edison Marrese-Taylor collaborates with scholars based in Japan, Chile and Australia. Edison Marrese-Taylor's co-authors include Yutaka Matsuo, Felipe Bravo-Márquez, Juan D. Velásquez, Pablo Loyola, Jorge Balazs, Suzana Ilić, Yutaka Matsuo, Machel Reid, Stephen Jay Gould and Cristian Rodríguez-Opazo and has published in prestigious journals such as Expert Systems with Applications, Information Fusion and ANU Open Research (Australian National University).

In The Last Decade

Edison Marrese-Taylor

28 papers receiving 509 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Edison Marrese-Taylor Japan 11 423 163 103 55 33 31 539
José M. Del Álamo Spain 11 149 0.4× 167 1.0× 194 1.9× 14 0.3× 51 1.5× 46 383
Lizhen Qu Australia 12 386 0.9× 183 1.1× 36 0.3× 76 1.4× 30 0.9× 50 546
Wael Etaiwi Jordan 11 237 0.6× 137 0.8× 28 0.3× 28 0.5× 37 1.1× 32 383
Maliheh Izadi Netherlands 9 165 0.4× 274 1.7× 25 0.2× 42 0.8× 49 1.5× 28 356
Fatih Gedikli Germany 7 214 0.5× 285 1.7× 71 0.7× 99 1.8× 23 0.7× 13 405
Moritz Y. Becker United Kingdom 11 223 0.5× 114 0.7× 280 2.7× 37 0.7× 47 1.4× 26 458
Giuseppe Primiero United Kingdom 11 222 0.5× 70 0.4× 95 0.9× 14 0.3× 60 1.8× 75 398
Traian Marius Truţă United States 13 310 0.7× 117 0.7× 225 2.2× 11 0.2× 46 1.4× 36 437
Sarah Chasins United States 11 90 0.2× 122 0.7× 37 0.4× 38 0.7× 47 1.4× 28 316
Abdallah Yousif China 8 271 0.6× 269 1.7× 37 0.4× 35 0.6× 41 1.2× 13 464

Countries citing papers authored by Edison Marrese-Taylor

Since Specialization
Citations

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

Fields of papers citing papers by Edison Marrese-Taylor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Edison Marrese-Taylor. 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 Edison Marrese-Taylor. The network helps show where Edison Marrese-Taylor may publish in the future.

Co-authorship network of co-authors of Edison Marrese-Taylor

This figure shows the co-authorship network connecting the top 25 collaborators of Edison Marrese-Taylor. A scholar is included among the top collaborators of Edison Marrese-Taylor 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 Edison Marrese-Taylor. Edison Marrese-Taylor 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.
Yang, Rui, Edison Marrese-Taylor, Yuhe Ke, et al.. (2024). KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques. 155–166. 14 indexed citations
2.
3.
Marrese-Taylor, Edison, et al.. (2024). Media Bias Detection Across Families of Language Models. 4083–4098.
4.
Marrese-Taylor, Edison, Pin Wang, & Yutaka Matsuo. (2023). Towards Better Evaluation for Formality-Controlled English-Japanese Machine Translation. 551–560. 1 indexed citations
5.
Marrese-Taylor, Edison, et al.. (2023). An Effective Approach for Informational and Lexical Bias Detection. 66–77. 2 indexed citations
6.
Loyola, Pablo, Edison Marrese-Taylor, & Andrés Hoyos-Idrobo. (2023). Perceptual Structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLMs. 1536–1542. 2 indexed citations
7.
Rodríguez-Opazo, Cristian, Edison Marrese-Taylor, Basura Fernando, Hiroya Takamura, & Qi Wu. (2023). Memory-efficient Temporal Moment Localization in Long Videos. 1909–1924. 1 indexed citations
8.
Marrese-Taylor, Edison, et al.. (2023). Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding. 13101–13123. 4 indexed citations
9.
Marrese-Taylor, Edison, et al.. (2023). Target-Aware Contextual Political Bias Detection in News. 782–792. 1 indexed citations
10.
Reid, Machel, et al.. (2021). Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining. 234–240. 10 indexed citations
11.
Marrese-Taylor, Edison, Machel Reid, & Yutaka Matsuo. (2021). Variational Inference for Learning Representations of Natural Language Edits. Proceedings of the AAAI Conference on Artificial Intelligence. 35(15). 13552–13560. 1 indexed citations
12.
Rodríguez-Opazo, Cristian, Edison Marrese-Taylor, Basura Fernando, Hongdong Li, & Stephen Jay Gould. (2021). DORi: Discovering Object Relationships for Moment Localization of a Natural Language Query in a Video. ANU Open Research (Australian National University). 1078–1087. 26 indexed citations
13.
Marrese-Taylor, Edison, Pablo Loyola, Jorge Balazs, & Yutaka Matsuo. (2020). Learning to Describe Editing Activities in Collaborative Environments: A Case Study on GitHub and Wikipedia.. Pacific Asia Conference on Language, Information, and Computation. 188–198. 1 indexed citations
14.
Marrese-Taylor, Edison, Pablo Loyola, & Yutaka Matsuo. (2019). An Edit-centric Approach for Wikipedia Article Quality Assessment. 6 indexed citations
15.
Balazs, Jorge, Edison Marrese-Taylor, & Yutaka Matsuo. (2018). IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations. 50–56. 9 indexed citations
16.
Loyola, Pablo, Edison Marrese-Taylor, Jorge Balazs, Yutaka Matsuo, & Fumiko Satoh. (2018). Content Aware Source Code Change Description Generation. 119–128. 18 indexed citations
17.
Marrese-Taylor, Edison, et al.. (2018). Learning to Automatically Generate Fill-In-The-Blank Quizzes. 152–156. 13 indexed citations
18.
Ilić, Suzana, Edison Marrese-Taylor, Jorge Balazs, & Yutaka Matsuo. (2018). Deep contextualized word representations for detecting sarcasm and irony. 78 indexed citations
19.
Loyola, Pablo, Edison Marrese-Taylor, & Yutaka Matsuo. (2017). A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes. 287–292. 79 indexed citations
20.
Marrese-Taylor, Edison & Yutaka Matsuo. (2017). EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity. 233–237. 1 indexed citations

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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