H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 109 Citations 83,350 341 World Ranking 96 National Ranking 63

Research.com Recognitions

Awards & Achievements

2017 - ACM Fellow For contributions to machine learning with structured data, and innovations in scientific communication

2009 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the theory and application of information extraction, natural language processing, data mining, machine learning, and their integration.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Programming language

Artificial intelligence, Machine learning, Pattern recognition, Natural language processing and Conditional random field are his primary areas of study. His Artificial intelligence and Topic model, Probabilistic logic, Word, Naive Bayes classifier and Hidden Markov model investigations all form part of his Artificial intelligence research activities. His research in Probabilistic logic intersects with topics in Bayesian network and Domain knowledge.

His Machine learning study incorporates themes from Vocabulary, Prior probability and Data mining. He has researched Pattern recognition in several fields, including Feature and Maximum-entropy Markov model. Andrew McCallum has included themes like Structured prediction, Graphical model, Information extraction and Discriminative model in his Conditional random field study.

His most cited work include:

  • Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data (11231 citations)
  • A comparison of event models for naive bayes text classification (2576 citations)
  • Text Classification from Labeled and Unlabeled Documents using EM (2460 citations)

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

Artificial intelligence, Machine learning, Natural language processing, Inference and Information retrieval are his primary areas of study. Within one scientific family, Andrew McCallum focuses on topics pertaining to Pattern recognition under Artificial intelligence, and may sometimes address concerns connected to Feature. His research investigates the link between Machine learning and topics such as Data mining that cross with problems in Cluster analysis.

His work in Natural language processing tackles topics such as Knowledge base which are related to areas like Schema. His biological study spans a wide range of topics, including Algorithm, Theoretical computer science and Markov chain Monte Carlo. His Theoretical computer science research integrates issues from Embedding and Graph.

He most often published in these fields:

  • Artificial intelligence (57.66%)
  • Machine learning (28.15%)
  • Natural language processing (21.40%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (57.66%)
  • Natural language processing (21.40%)
  • Information retrieval (15.09%)

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

Andrew McCallum mainly focuses on Artificial intelligence, Natural language processing, Information retrieval, Cluster analysis and Theoretical computer science. His studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition. His studies deal with areas such as Language model and Named-entity recognition as well as Machine learning.

His work investigates the relationship between Natural language processing and topics such as Embedding that intersect with problems in Inference. The study incorporates disciplines such as Knowledge base and Citation in addition to Information retrieval. His Theoretical computer science study combines topics in areas such as Graph, Knowledge graph, Probabilistic logic, Benchmark and Case-based reasoning.

Between 2018 and 2021, his most popular works were:

  • Energy and Policy Considerations for Deep Learning in NLP (388 citations)
  • Energy and Policy Considerations for Deep Learning in NLP (165 citations)
  • Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (52 citations)

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

  • Artificial intelligence
  • Machine learning
  • Programming language

His main research concerns Artificial intelligence, Natural language processing, Question answering, Information retrieval and Inference. Andrew McCallum combines topics linked to Pattern recognition with his work on Artificial intelligence. His work on Convolutional neural network as part of his general Pattern recognition study is frequently connected to Term, thereby bridging the divide between different branches of science.

When carried out as part of a general Information retrieval research project, his work on Open domain is frequently linked to work in Hop, therefore connecting diverse disciplines of study. His Inference study incorporates themes from Embedding and Spurious relationship. The Natural language study combines topics in areas such as Machine learning and Transformer.

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

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

John D. Lafferty;Andrew McCallum;Fernando C. N. Pereira.
international conference on machine learning (2001)

14581 Citations

A comparison of event models for naive bayes text classification

Andrew McCallum;Kamal Nigam.
national conference on artificial intelligence (1998)

4696 Citations

Text Classification from Labeled and Unlabeled Documents using EM

Kamal Nigam;Andrew Kachites McCallum;Sebastian Thrun;Tom Mitchell.
Machine Learning (2000)

3733 Citations

An Introduction to Conditional Random Fields for Relational Learning

Charles Sutton;Andrew McCallum.
(2007)

2110 Citations

An Introduction to Conditional Random Fields

Charles Sutton;Andrew McCallum.
(2012)

1829 Citations

Maximum Entropy Markov Models for Information Extraction and Segmentation

Andrew McCallum;Dayne Freitag;Fernando C. N. Pereira.
international conference on machine learning (2000)

1815 Citations

Introduction to Statistical Relational Learning

Charles Sutton;Andrew McCallum.
MIT Press (2007)

1707 Citations

Topics over time: a non-Markov continuous-time model of topical trends

Xuerui Wang;Andrew McCallum.
knowledge discovery and data mining (2006)

1542 Citations

Efficient clustering of high-dimensional data sets with application to reference matching

Andrew McCallum;Kamal Nigam;Lyle H. Ungar.
knowledge discovery and data mining (2000)

1358 Citations

Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons

Andrew McCallum;Wei Li.
north american chapter of the association for computational linguistics (2003)

1343 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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