H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 88 Citations 37,357 248 World Ranking 290 National Ranking 176

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Programming language

Raymond J. Mooney spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Natural language and Parsing. His research in Artificial intelligence intersects with topics in Multi-task learning and Pattern recognition. Raymond J. Mooney has included themes like Semantics, Inductive logic programming and Active learning in his Natural language processing study.

His Machine learning study integrates concerns from other disciplines, such as Active learning and Training set. His Natural language research incorporates themes from Annotation, Picture language, Object, Text mining and Kernel method. His Parsing study combines topics in areas such as Executable, Grammar, Rule-based machine translation and Machine translation.

His most cited work include:

  • Content-based book recommending using learning for text categorization (1123 citations)
  • Adaptive duplicate detection using learnable string similarity measures (851 citations)
  • Explanation-Based Learning: An Alternative View (851 citations)

What are the main themes of his work throughout his whole career to date?

Raymond J. Mooney focuses on Artificial intelligence, Natural language processing, Machine learning, Natural language and Parsing. His Artificial intelligence research includes elements of Statistical relational learning and Pattern recognition. His work focuses on many connections between Natural language processing and other disciplines, such as Context, that overlap with his field of interest in Perception.

His research investigates the connection between Machine learning and topics such as Data mining that intersect with problems in Correlation clustering, Fuzzy clustering and Cluster analysis. His study explores the link between Natural language and topics such as Human–computer interaction that cross with problems in Robot, Human–robot interaction and Dialog box. His Parsing research is multidisciplinary, relying on both Syntax and Formal grammar, Grammar.

He most often published in these fields:

  • Artificial intelligence (75.99%)
  • Natural language processing (38.16%)
  • Machine learning (26.97%)

What were the highlights of his more recent work (between 2013-2021)?

  • Artificial intelligence (75.99%)
  • Natural language processing (38.16%)
  • Natural language (23.36%)

In recent papers he was focusing on the following fields of study:

Raymond J. Mooney mostly deals with Artificial intelligence, Natural language processing, Natural language, Human–computer interaction and Machine learning. His work in Probabilistic logic, Inference, Parsing, Robot and Training set are all subfields of Artificial intelligence research. His Natural language processing research is multidisciplinary, incorporating perspectives in Rule of inference, Distributional semantics and Scripting language.

The study incorporates disciplines such as Executable, Code, Object, Source code and Reinforcement learning in addition to Natural language. His Human–computer interaction research is multidisciplinary, incorporating elements of Active learning, Dialog box and Human–robot interaction. His work carried out in the field of Machine learning brings together such families of science as Multi-task learning and Abductive reasoning.

Between 2013 and 2021, his most popular works were:

  • Sequence to Sequence -- Video to Text (747 citations)
  • Translating Videos to Natural Language Using Deep Recurrent Neural Networks (636 citations)
  • Sequence to Sequence -- Video to Text (205 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Programming language

His primary areas of investigation include Artificial intelligence, Natural language processing, Natural language, Text corpus and Human–computer interaction. Recurrent neural network, Closed captioning, Cognitive neuroscience of visual object recognition, Rule of inference and Robot are subfields of Artificial intelligence in which his conducts study. The various areas that Raymond J. Mooney examines in his Natural language processing study include Probabilistic logic, Training set and Scripting language.

His study on Natural language also encompasses disciplines like

  • Object together with Machine vision,
  • Language model together with Set. In his work, Distributional semantics and Information retrieval is strongly intertwined with Grammaticality, which is a subfield of Text corpus. The Human–computer interaction study combines topics in areas such as Style, Human–robot interaction and Benchmark.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Content-based book recommending using learning for text categorization

Raymond J. Mooney;Loriene Roy.
acm international conference on digital libraries (2000)

1817 Citations

Explanation-Based Learning: An Alternative View

Gerald Dejong;Raymond Mooney.
Machine Learning (1986)

1411 Citations

Adaptive duplicate detection using learnable string similarity measures

Mikhail Bilenko;Raymond J. Mooney.
knowledge discovery and data mining (2003)

1216 Citations

Semi-supervised Clustering by Seeding

Sugato Basu;Arindam Banerjee;Raymond J. Mooney.
international conference on machine learning (2002)

1158 Citations

Integrating constraints and metric learning in semi-supervised clustering

Mikhail Bilenko;Sugato Basu;Raymond J. Mooney.
international conference on machine learning (2004)

1041 Citations

A probabilistic framework for semi-supervised clustering

Sugato Basu;Mikhail Bilenko;Raymond J. Mooney.
knowledge discovery and data mining (2004)

1037 Citations

Impact of Similarity Measures on Web-page Clustering

Alexander Strehl;Joydeep Ghosh;Raymond Mooney.
(2000)

996 Citations

A Shortest Path Dependency Kernel for Relation Extraction

Razvan Bunescu;Raymond Mooney.
empirical methods in natural language processing (2005)

978 Citations

Adaptive name matching in information integration

M. Bilenko;R. Mooney;W. Cohen;P. Ravikumar.
IEEE Intelligent Systems (2003)

696 Citations

Relational learning of pattern-match rules for information extraction

Mary Elaine Califf;Raymond J. Mooney.
national conference on artificial intelligence (1999)

695 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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