2023 - Research.com Computer Science in United States Leader Award
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.
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.
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.
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.
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.
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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)
A comparison of event models for naive bayes text classification
Andrew McCallum;Kamal Nigam.
national conference on artificial intelligence (1998)
Text Classification from Labeled and Unlabeled Documents using EM
Kamal Nigam;Andrew Kachites McCallum;Sebastian Thrun;Tom Mitchell.
Machine Learning (2000)
An Introduction to Conditional Random Fields for Relational Learning
Charles Sutton;Andrew McCallum.
Maximum Entropy Markov Models for Information Extraction and Segmentation
Andrew McCallum;Dayne Freitag;Fernando C. N. Pereira.
international conference on machine learning (2000)
Introduction to Statistical Relational Learning
Charles Sutton;Andrew McCallum.
MIT Press (2007)
Topics over time: a non-Markov continuous-time model of topical trends
Xuerui Wang;Andrew McCallum.
knowledge discovery and data mining (2006)
Optimizing Semantic Coherence in Topic Models
David Mimno;Hanna Wallach;Edmund Talley;Miriam Leenders.
empirical methods in natural language processing (2011)
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)
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)
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