A. McCallum

A. McCallum, J. Weinman, A. Hanson, 2004, Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004..

Classification with Hybrid Generative/Discriminative Models

Rajat Raina, Andrew McCallum, Andrew Y. Ng, 2003, NIPS.

Learning to Extract Symbolic Knowledge from the World Wide Web

Tom M. Mitchell, Andrew McCallum, Dayne Freitag, 1998, AAAI/IAAI.

Efficiently Inducing Features of Conditional Random Fields pdf

Andrew McCallum, A. McCallum, 2002, UAI.

Using Maximum Entropy for Text Classification

Andrew McCallum, John Lafferty, Kamal Nigam, 1999 .

Semi-Supervised Clustering with User Feedback

Andrew McCallum, Rich Caruana, David Cohn, 2003 .

Instance-Based Utile Distinctions for Reinforcement Learning

Andrew McCallum, A. McCallum, 1995 .

Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

Andrew McCallum, Charles A. Sutton, Khashayar Rohanimanesh, 2004, J. Mach. Learn. Res..

Learning with Scope, with Application to Information Extraction and Classification

Andrew McCallum, David M. Blei, J. Andrew Bagnell, 2002, UAI.

Piecewise Training for Undirected Models pdf

Andrew McCallum, Charles A. Sutton, C. Sutton, 2005, UAI.

Distributional clustering of words for text classification

Andrew McCallum, L. Douglas Baker, A. McCallum, 1998, SIGIR '98.

Piecewise pseudolikelihood for efficient training of conditional random fields

Andrew McCallum, Charles A. Sutton, C. Sutton, 2007, ICML '07.

Efficient methods for topic model inference on streaming document collections

Andrew McCallum, David M. Mimno, Limin Yao, 2009, KDD.

Rethinking LDA: Why Priors Matter

Andrew McCallum, Hanna M. Wallach, David M. Mimno, 2009, NIPS.

Joint Parsing and Semantic Role Labeling

Andrew McCallum, Charles A. Sutton, C. Sutton, 2005, CoNLL.

An Introduction to Conditional Random Fields for Relational Learning

Andrew McCallum, Charles Sutton, Charles Sutton, 2007 .

Conditional Probabilistic Context-Free Grammars

Charles Sutton, A. McCallum, 2004 .

Reducing Weight Undertraining in Structured Discriminative Learning

Andrew McCallum, Charles A. Sutton, Michael Sindelar, 2006, NAACL.

Confidence Estimation for Information Extraction

Andrew McCallum, Aron Culotta, A. McCallum, 2004, NAACL.

Generalized Expectation Criteria

Gideon S. Mann, A. McCallum, Gregory Druck, 2007 .

SampleRank : Learning Preferences from Atomic Gradients

Michael L. Wick, A. McCallum, Khashayar Rohanimanesh, 2009 .

Topic and Role Discovery in Social Networks

Andrew McCallum, Xuerui Wang, Andrés Corrada-Emmanuel, 2005, IJCAI.

Using Reinforcement Learning to Spider the Web Efficiently

Andrew McCallum, Jason D. M. Rennie, Jason Rennie, 1999, ICML.

A Machine Learning Approach to Building Domain-Speci c Search Engines

Jason D. M. Rennie, A. McCallum, K. Nigam, 1999 .

Employing EM and Pool-Based Active Learning for Text Classification

Andrew McCallum, Kamal Nigam, A. McCallum, 1998, ICML.

Pachinko allocation: DAG-structured mixture models of topic correlations

Wei Li, Andrew McCallum, A. McCallum, 2006, ICML.

Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons

Wei Li, Andrew McCallum, A. McCallum, 2003, CoNLL.

Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space pdf

Andrew McCallum, Alexandre Passos, Arvind Neelakantan, 2014, EMNLP.

Feature Bagging: Preventing Weight Undertraining in Structured Discriminative Learning

Andrew McCallum, Charles Sutton, Michael Sindelar, 2005 .

Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks

Andrew McCallum, A. McCallum, 1996 .

Instance-Based State Identification for Reinforcement Learning

Andrew McCallum, A. McCallum, 1994, NIPS.

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning pdf

Alexander J. Smola, Akshay Krishnamurthy, Rajarshi Das, 2017, ICLR.

Automatic Categorization of Email into Folders: Benchmark Experiments on Enron and SRI Corpora

Andrew McCallum, Ron Bekkerman, Gary B. Huang, 2005 .

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

Andrew McCallum, Xuerui Wang, A. McCallum, 2006, KDD '06.

Semi-supervised classification with hybrid generative/discriminative methods

Christopher Joseph Pal, Andrew McCallum, Xiaojin Zhu, 2007, KDD '07.

Dynamic Conditional Random Fields for Jointly Labeling Multiple Sequences

Andrew McCallum, Khashayar Rohanimanesh, A. McCallum, 2003 .

Marginal Inference in MRFs using Frank-Wolfe

A. McCallum, D. Sheldon, David Belanger, 2013 .

Collective Segmentation and Labeling of Distant Entities in Information Extraction

Andrew McCallum, Charles Sutton, Charles Sutton, 2004 .

Building Domain-Speci c Search Engines with Machine Learning Techniques

Jason D. M. Rennie, A. McCallum, K. Nigam, 1999 .

Word Representations via Gaussian Embedding pdf

Andrew McCallum, Luke Vilnis, L. Vilnis, 2014, ICLR.

Organizing the OCA: learning faceted subjects from a library of digital books

Andrew McCallum, David M. Mimno, A. McCallum, 2007, JCDL '07.

Inference and Learning in Large Factor Graphs with Adaptive Proposal Distributions and a Rank-based Objective

Michael L. Wick, A. McCallum, Khashayar Rohanimanesh, 2009 .

Accurate Information Extraction from Research Papers using Conditional Random Fields

Andrew McCallum, Fuchun Peng, A. McCallum, 2004, NAACL.

Nonparametric Bayes Pachinko Allocation

Wei Li, Andrew McCallum, David M. Blei, 2007, UAI.

Mixtures of hierarchical topics with Pachinko allocation

Wei Li, Andrew McCallum, David M. Mimno, 2007, ICML '07.

Improved Dynamic Schedules for Belief Propagation

Andrew McCallum, Charles A. Sutton, C. Sutton, 2007, UAI.

Polylingual Topic Models

Andrew McCallum, David A. Smith, Hanna M. Wallach, 2009, EMNLP.

Scalable probabilistic databases with factor graphs and MCMC

Andrew McCallum, Michael L. Wick, Gerome Miklau, 2010, Proc. VLDB Endow..

Topic models for taxonomies

Andrew McCallum, Hanna M. Wallach, David M. Mimno, 2012, JCDL '12.

Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields

Gideon S. Mann, Andrew McCallum, A. McCallum, 2008, ACL.

Direct Maximization of Rank-Based Metrics for Information Retrieval

W. Bruce Croft, A. McCallum, Donald Metzler, 2005 .

Simple, robust, scalable semi-supervised learning via expectation regularization

Gideon S. Mann, Andrew McCallum, A. McCallum, 2007, ICML '07.

A Note on Semi-Supervised Learning using Markov Random Fields

A. McCallum, Wei Li, 2004 .

The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email

Andrew McCallum, Xiaoyang Sean Wang, A. Corrada-emmanual, 2005 .

A Machine Learning Approach to Building Domain-Specific Search Engines

Andrew McCallum, Jason D. M. Rennie, Kamal Nigam, 1999, IJCAI.

Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data

Gideon S. Mann, Andrew McCallum, A. McCallum, 2010, J. Mach. Learn. Res..

Learning from labeled features using generalized expectation criteria

Gideon S. Mann, Andrew McCallum, Gregory Druck, 2008, SIGIR '08.

Building Domain-Specific Search Engines with Machine Learning Techniques

Andrew McCallum, Jason D. M. Rennie, Kamal Nigam, 1999 .

Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders pdf

Mohit Yadav, Andrew McCallum, Mohit Iyyer, 2019, NAACL.

Information Extraction with HMM Structures Learned by Stochastic Optimization

Andrew McCallum, Dayne Freitag, Dayne Freitag, 2000, AAAI/IAAI.

Piecewise training for structured prediction

Andrew McCallum, Charles A. Sutton, Charles Sutton, 2009, Machine Learning.

Fast and Robust Joint Models for Biomedical Event Extraction

Andrew McCallum, Sebastian Riedel, A. McCallum, 2011, EMNLP.

Piecewise Training with Parameter Independence Diagrams: Comparing Globally- and Locally-trained Linear-chain CRFs

Charles Sutton, A. McCallum, 2004 .

Learning to construct knowledge bases from the World Wide Web

Tom M. Mitchell, Andrew McCallum, Dayne Freitag, 2000, Artif. Intell..

Model Combination for Event Extraction in BioNLP 2011

Andrew McCallum, Mihai Surdeanu, Christopher D. Manning, 2011, BioNLP@ACL.

Information extraction from research papers using conditional random fields

Andrew McCallum, Fuchun Peng, A. McCallum, 2006, Inf. Process. Manag..

Scalable Bottom-Up Hierarchical Clustering pdf

Yuan Wang, Marc Najork, Amr Ahmed, 2020, ArXiv.

Incorporating Selectional Preferences in Multi-hop Relation Extraction

Rajarshi Das, Andrew McCallum, David Belanger, 2016, AKBC@NAACL-HLT.

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

Andrew McCallum, Charles Sutton, Charles Sutton, 2005 .

Shared Task Organizing Committee -transliteration Mining: Whitepaper of News 2010 Shared Task on Transliteration Generation Transliteration Generation and Mining with Limited Training Resources Transliteration Using a Phrase-based Statistical Machine Translation System to Re-score the Output of a Jo

Rafael E. Banchs, Mitesh M. Khapra, Robert E. Frederking, 2010 .

Predictive Random Fields : Latent Variable Models Fit by Multiway Conditional Probability with Applications to Document Analysis

C. Pal, A. McCallum, Xuerui Wang, 2005 .

Constrained Kronecker Deltas for Fast Approximate Inference and Estimation

C. Pal, Charles Sutton, A. McCallum, 2005 .

Machine Learning for Sequences and Structured Data : Tools for Non-Experts

A. McCallum, Fernando Pereira, John Pereira, 2004 .

Learning Task-Relevant State Spaces with a Utile Distinction Test

A. McCallum, 2007 .

Practical Markov Logic Containing First-Order Quantifiers with Application to Identity Uncertainty

A. McCallum, A. Culotta, 2006 .

Learning Clusterwise Similarity with First-Order Features

A. McCallum, A. Culotta, 2003 .

1 Semi-Supervised Text Classification Using EM

Tom M. Mitchell, Andrew McCallum, Kamal Nigam, 2006 .

Optimization and Learning in F ACTORIE

Andrew McCallum, Luke Vilnis, Alexandre Passos, 2014 .

Knowledge Base Completion using Compositional Vector Space Models

Arvind Neelakantan, A. McCallum, Benjamin Roth, 2014 .

Box-To-Box Transformations for Modeling Joint Hierarchies

Shib Sankar Dasgupta, A. McCallum, Xiang Li, 2021, REPL4NLP.

Integrating Web-Scale OpenIE Extractions and Knowledge Bases with Entity Neighborhood Encoder

A. McCallum, Dongxu Zhang, Subhabrata Mukherjee, 2019 .

Network Analysis Social Extraction Keyword names CRF Contact and Information Extraction Email Extraction Person

R. Bekkerman, A. McCallum, A. Culotta, 2018 .

Constraint-Driven Training of Complex Models Using MCMC

A. McCallum, Gregory Druck, Sameer Singh, 2012 .

JOINT PROBABILISTIC REASONING ABOUT COREFERENCE AND RELATIONS OF UNIVERSAL SCHEMA UNIVERSITY OF MASSACHUSETTS AMHERST OCTOBER 2017 FINAL TECHNICAL REPORT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED

A. McCallum, Nicholas Monath, 2017 .

Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

Rajarshi Das, Andrew McCallum, David Belanger, 2016, EACL.

Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

A. McCallum, E. Olivetti, G. Ceder, 2017 .

Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification

Christopher Joseph Pal, Andrew McCallum, Gregory Druck, 2006, AAAI.

Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks

A. McCallum, Trapit Bansal, Rishikesh Jha, 2019, COLING.

Synthesis Insights from Scienti fi c Literature via Text Extraction and Machine Learning

A. McCallum, E. Olivetti, G. Ceder, 2017 .

BIBTEX-based dataset generation for training citation parsers

A. McCallum, H. Ramesh, B. Veytsman, 2019 .

Using EM to Classify Text from Labeledand Unlabeled DocumentsKamal

Nigam, A. McCallum, Sebastian, 1998 .

Pachinko Allocation: Scalable Mixture Models of Topic Correlations

Wei Li, Andrew McCallum, A. McCallum, 2008 .

Introduction to Statistical Relational Learning

Andrew McCallum, Charles Sutton, Charles Sutton, 2007 .

Unsupervised Coreference of Publication Venues

Andrew McCallum, Charles Sutton, Charles Sutton, 2007 .

Sublinear Time Approximation of Text Similarity Matrices

A. McCallum, Cameron Musco, Archan Ray, 2021, AAAI.

Exact Inference on Hierarchical Clustering in Particle Physics and Cancer Genomics

Craig S. Greenberg, Patrick Flaherty, A. McCallum, 2020 .

Cryptogram Decoding for OCR Using Numerization Strings

Gary B. Huang, E. Learned-Miller, A. McCallum, 2007, IEEE International Conference on Document Analysis and Recognition.

Anchor Text Extraction for Academic Search Accurate Argumentative Zoning with Maximum Entropy Models Classification of Research Papers into a Patent Classification System Using Two Translation Models Detecting Key Sentences for Automatic Assistance in Peer Reviewing Research Articles in Educational

Colin R. Batchelor, Jesse Prabawa Gozali, Stephen Merity, 2009 .

Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning

Akshay Krishnamurthy, Rajarshi Das, Andrew McCallum, 2017, AKBC@NIPS.

Proceedings of the Ninth Conference on Computational Natural Language Learning, CoNLL 2005, Ann Arbor, Michigan, USA, June 29-30, 2005

Andrew McCallum, Charles Sutton, Charles Sutton, 2005, CoNLL.

Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008

Andrew McCallum, William W. Cohen, Sam T. Roweis, 2008, ICML.

Smoothing the Geometry of Probabilistic Box Embeddings

Xiang Li, Andrew McCallum, Michael Boratko, 2018, ICLR.

Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings

A. McCallum, Michael Boratko, S. Mishra, 2022, ACL.

Capacity and Bias of Learned Geometric Embeddings for Directed Graphs

K. Clarkson, L. Vilnis, A. McCallum, 2021, NeurIPS.

Event-Event Relation Extraction using Probabilistic Box Embedding

Jay Yoon Lee, A. McCallum, Dongxu Zhang, 2022, ACL.

Proceedings, Twenty-fifth International Conference on Machine Learning

Andrew McCallum, Sam T. Roweis, S. Roweis, 2008 .

Learning Multi-facet Embeddings of Phrases and Sentences using Sparse Coding for Unsupervised Semantic Applications

Andrew McCallum, Haw-Shiuan Chang, Amol Agrawal, 2019 .

Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions

A. McCallum, Nishant Yadav, Nicholas Monath, 2021, CRAC.

Paper Matching with Local Fairness Constraints

A. McCallum, B. Saha, Ari Kobren, 2019, KDD.

Discriminative fields for modeling spatial dependen-cies in natural images

Fernando Pereira, Andrew McCallum, John Lafferty, 2001, ICML 2001.

NIPS Workshop on Learning with Structured Outputs

Andrew McCallum, Charles Sutton, Charles Sutton, 2004, NIPS 2004.

Human-Machine Cooperation: Supporting User Corrections to Automatically Constructed KBs

Andrew McCallum, Michael L. Wick, Karl Schultz, 2012, AKBC-WEKEX@NAACL-HLT.

Ecient Web Spidering with Reinforcement Learning

Andrew McCallum, Jason D. M. Rennie, A. McCallum, 1999 .

Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)

Andrew McCallum, Charles Sutton, Charles Sutton, 2005 .

The Joint Ninth WEBKDD and 1st SNA-KDD Workshop on Web Mining and Social Network Analysis

John Yen, Jaideep Srivastava, Myra Spiliopoulou, 2007, KDD '07.

Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings

A. McCallum, Michael Boratko, S. Mishra, 2021, ACL.

Cora: Computer science research paper search engine

John A. Reed, Andrew McCallum, Jason D. M. Rennie, 2000 .

NIPS Workshop on Syntax, Semantics, and Statistics

Andrew McCallum, Khashayar Rohanimanesh, Charles Sutton, 2003 .

Augmenting Scientific Creativity with Retrieval across Knowledge Domains

A. McCallum, A. Kittur, E. Olivetti, 2022, 2206.01328.

An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity

A. McCallum, Michael Boratko, Nicholas Monath, 2022, AAAI.

Inferring gene annotations in Gene Ontology from gene expression data

A. McCallum, D. Kulp, Xuerui Wang, 2006 .

Edinburgh Research Explorer Piecewise training for structured prediction

Charles Sutton, A. McCallum, 2009 .

Modeling Label Space Interactions in Multi-label Classification using Box Embeddings

Jay Yoon Lee, A. McCallum, Michael Boratko, 2022, ICLR.

Enhanced Distant Supervision with State-Change Information for Relation Extraction

A. McCallum, Dongxu Zhang, Jui Shah, 2022 .

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000

Toby Walsh, Alan Bundy, Stefan Schaal, 2000, ICML.

Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling

A. McCallum, Kathryn Ricci, Haw-Shiuan Chang, 2022, ArXiv.

Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions

A. McCallum, Haw-Shiuan Chang, 2022, ACL.

Unsupervised Partial Sentence Matching for Cited Text Identification

A. McCallum, Kathryn Ricci, Haw-Shiuan Chang, 2022, SDP.

SampleRank : Learning Preferences from Atomic Gradients

Michael L. Wick, A. McCallum, Khashayar Rohanimanesh, 2009 .

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization

M. Zaheer, A. McCallum, Nishant Yadav, 2022, EMNLP.

Reports of the 2013 AAAI Spring Symposium Series

Erez Karpas, Francesca Rossi, Takashi Kido, 2013, AI Mag..

Augmenting Scientific Creativity with Retrieval across Knowledge Domains

A. McCallum, A. Kittur, E. Olivetti, 2022, ArXiv.

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

M. Zaheer, R. Das, Hannaneh Hajishirzi, 2022, ICML.

Inducing and Using Alignments for Transition-based AMR Parsing

Ramón Fernández Astudillo, Andrew Drozdov, A. McCallum, 2022, NAACL.

You can't pick your neighbors, or can you? When and how to rely on retrieval in the $k$NN-LM

Andrew Drozdov, A. McCallum, Mohit Iyyer, 2022, 2210.15859.

MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network

Jan A. Botha, A. McCallum, Nicholas FitzGerald, 2021, ACL.

Probabilistic Case-based Reasoning in Knowledge Bases pdf

Manzil Zaheer, Nicholas Monath, Andrew McCallum, 2020, FINDINGS.

A Simple Approach to Case-Based Reasoning in Knowledge Bases pdf

Manzil Zaheer, Andrew McCallum, Rajarshi Das, 2020, AKBC.

Message Passing for Soft Constraint Dual Decomposition

Andrew McCallum, Sebastian Riedel, David Belanger, 2014, UAI.

Corrective feedback and persistent learning for information extraction

Paul A. Viola, Andrew McCallum, Trausti T. Kristjansson, 2006, Artif. Intell..

Chinese Segmentation and New Word Detection using Conditional Random Fields

Andrew McCallum, Fuchun Peng, Fangfang Feng, 2004, COLING.

Learning Visual Routines with Reinforcement Learning

Andrew Kachites McCallum, A. McCallum, 1996 .

Optimizing Semantic Coherence in Topic Models

Andrew McCallum, Hanna M. Wallach, David M. Mimno, 2011, EMNLP.

Linguistically-Informed Self-Attention for Semantic Role Labeling

Andrew McCallum, David Weiss, Emma Strubell, 2018, EMNLP.

High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models

Andrew McCallum, Gregory Druck, A. McCallum, 2010, ICML.

Entity Linking via Explicit Mention-Mention Coreference Modeling

A. McCallum, Nicholas Monath, Rico Angell, 2022, NAACL.

Entity Linking via Explicit Mention-Mention Coreference Modeling

A. McCallum, Nicholas Monath, Rico Angell, 2021, NAACL.

An Introduction to Conditional Random Fields

Andrew McCallum, Charles A. Sutton, Charles Sutton, 2010, Found. Trends Mach. Learn..

Semi-Supervised Sequence Modeling with Syntactic Topic Models

Wei Li, Andrew McCallum, A. McCallum, 2005, AAAI.

Resource-bounded information acquisition and learning

Andrew McCallum, Pallika Kanani, A. McCallum, 2012 .

Composition of Conditional Random Fields for Transfer Learning

Andrew McCallum, Charles A. Sutton, Charles Sutton, 2005, HLT.

ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning

Rajarshi Das, Andrew McCallum, Michael Boratko, 2020, EMNLP.

Improving Text Classification by Shrinkage in a Hierarchy of Classes

Tom M. Mitchell, Andrew McCallum, Ronald Rosenfeld, 1998, ICML.

Using error decay prediction to overcome practical issues of deep active learning for named entity recognition

A. McCallum, Shankar Vembu, Sunil Mohan, 2019, Machine Learning.

Active Learning by Labeling Features

Andrew McCallum, Burr Settles, Gregory Druck, 2009, EMNLP.

Efficient Computation of Entropy Gradient for Semi-Supervised Conditional Random Fields

Gideon S. Mann, Andrew McCallum, A. McCallum, 2007, NAACL.

Reducing Labeling Effort for Structured Prediction Tasks

Andrew McCallum, Aron Culotta, A. McCallum, 2005, AAAI.

Toward Optimal Active Learning through Sampling Estimation of Error Reduction

Andrew McCallum, Nicholas Roy, N. Roy, 2001, ICML.

Automating the Construction of Internet Portals with Machine Learning

Andrew McCallum, Jason D. M. Rennie, Kamal Nigam, 2000, Information Retrieval.

Extracting social networks and contact information from email and the Web

Andrew McCallum, Aron Culotta, Ron Bekkerman, 2004, CEAS.

Gene Prediction with Conditional Random Fields

Andrew McCallum, Aron Culotta, David Kulp, 2005 .

Generalized expectation criteria for lightly supervised learning

Andrew McCallum, Gregory Druck, A. McCallum, 2011 .

Towards Theoretical Bounds for Resource-bounded Information Gathering for Correlation Clustering

Andrew McCallum, Ramesh Sitaraman, Pallika Kanani, 2009 .

Reducing Annotation Effort Using Generalized Expectation Criteria

Gideon S. Mann, Andrew McCallum, Gregory Druck, 2007 .

Leveraging Existing Resources using Generalized Expectation Criteria

Gideon S. Mann, Andrew McCallum, Gregory Druck, 2007 .

A2N: Attending to Neighbors for Knowledge Graph Inference

Andrew McCallum, Sujith Ravi, Da-Cheng Juan, 2019, ACL.

Interactive Information Extraction with Constrained Conditional Random Fields

Paul A. Viola, Andrew McCallum, Trausti T. Kristjansson, 2004, AAAI.

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

Andrew McCallum, Lyle H. Ungar, Kamal Nigam, 2000, KDD '00.

Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models

Andrew McCallum, Sameer Singh, Amarnag Subramanya, 2011, ACL.

Structured Prediction Energy Networks

Andrew McCallum, David Belanger, A. McCallum, 2015, ICML.

Scaling up Structured Multi-Label Prediction using Discriminative Mean Field Networks

A. McCallum, David Belanger, 2015 .

Robust Biomedical Event Extraction with Dual Decomposition and Minimal Domain Adaptation

Andrew McCallum, Sebastian Riedel, A. McCallum, 2011, BioNLP@ACL.

An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text

Andrew McCallum, Sheshera Mysore, Ahsaas Bajaj, 2020, FINDINGS.

Lexicon Infused Phrase Embeddings for Named Entity Resolution pdf

Andrew McCallum, Vineet Kumar, Alexandre Passos, 2014, CoNLL.

An Exploration of Entity Models, Collective Classification and Relation Description

James Allan, Andrew McCallum, Hema Raghavan, 2004 .

A Conditional Model of Deduplication for Multi-Type Relational Data

Andrew McCallum, Aron Culotta, A. McCallum, 2005 .

Energy and Policy Considerations for Deep Learning in NLP pdf

Andrew McCallum, Emma Strubell, Ananya Ganesh, 2019, ACL.

Exact and Approximate Hierarchical Clustering Using A

Andrew McCallum, Amr Ahmed, Manzil Zaheer, 2021, UAI.

Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering

Craig S. Greenberg, Patrick Flaherty, A. McCallum, 2020, AISTATS.

Compact Representation of Uncertainty in Clustering

Andrew McCallum, Ari Kobren, Andrew McGregor, 2018, NeurIPS.

Detecting anomalies in network traffic using maximum entropy estimation

Donald F. Towsley, Andrew McCallum, Yu Gu, 2005, IMC '05.

Long Document Summarization in a Low Resource Setting using Pretrained Language Models

Rajarshi Das, Andrew McCallum, Ahsaas Bajaj, 2021, ACL.

Pool-Based Active Learning for Text Classification

Andrew McCallum, Kamal Nigam, A. McCallum, 1999 .

Correlations and Anticorrelations in LDA Inference

Andrew McCallum, Alexandre Passos, Hanna Wallach, 2011 .

Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression

Andrew McCallum, David M. Mimno, A. McCallum, 2008, UAI.

Wikilinks: A Large-scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia

Fernando Pereira, Andrew McCallum, Sameer Singh, 2012 .

Attending to All Mention Pairs for Full Abstract Biological Relation Extraction pdf

Andrew McCallum, Emma Strubell, Patrick Verga, 2017, AKBC@NIPS.

Low resource recognition and linking of biomedical concepts from a large ontology

Andrew McCallum, Rico Angell, Sunil Mohan, 2021, BCB.

A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance

Andrew McCallum, Fernando Pereira, Kedar Bellare, 2005, UAI.

Lightly-Supervised Attribute Extraction

Fernando Pereira, Mark Dredze, Andrew McCallum, 2007 .

Modeling Relations and Their Mentions without Labeled Text

Andrew McCallum, Sebastian Riedel, Limin Yao, 2010, ECML/PKDD.

Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision pdf

Andrew McCallum, Trapit Bansal, Pat Verga, 2019, AAAI.

Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL? pdf

Andrew McCallum, Emma Strubell, A. McCallum, 2018, ArXiv.

Universal schema for entity type prediction

Andrew McCallum, Sebastian Riedel, Limin Yao, 2013, AKBC '13.

Relation Extraction with Matrix Factorization and Universal Schemas

Andrew McCallum, Benjamin M. Marlin, Sebastian Riedel, 2013, NAACL.

Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks

Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, 2020, EMNLP.

Energy and Policy Considerations for Modern Deep Learning Research

Andrew McCallum, Emma Strubell, Ananya Ganesh, 2020, AAAI.

Case-based Reasoning for Natural Language Queries over Knowledge Bases pdf

Rajarshi Das, Andrew McCallum, Manzil Zaheer, 2021, EMNLP.

Changing the Mind of Transformers for Topically-Controllable Language Generation

Andrew McCallum, Mohit Iyyer, Jiaming Yuan, 2021, EACL.

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Andrew McCallum, Shikhar Murty, Patrick Verga, 2018, ACL.

Unsupervised Pre-training for Biomedical Question Answering pdf

Kun Li, Trapit Bansal, Andrew McCallum, 2020, CLEF.

Machine-learned and codified synthesis parameters of oxide materials

Emma Strubell, Elsa Olivetti, Edward Kim, 2017, Scientific Data.

Compositional Vector Space Models for Knowledge Base Completion

Andrew McCallum, Benjamin Roth, Arvind Neelakantan, 2015, ACL.

Data-Dependent Spatial Context for Computer Vision with Conditional Markov Fields

Andrew McCallum, Jerod J. Weinman, A. McCallum, 2005 .

Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering pdf

Rajarshi Das, Andrew McCallum, Manzil Zaheer, 2019, ICLR.

Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email

Andrew McCallum, Xuerui Wang, Andrés Corrada-Emmanuel, 2007, J. Artif. Intell. Res..

Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text

Andrew McCallum, Aron Culotta, Jonathan Betz, 2006, NAACL.

Toward Optimal Active Learning through Monte Carlo Estimation of Error Reduction

Andrew McCallum, Nicholas Roy, N. Roy, 2001, ICML 2001.

Paper Matching with Local Fairness Constraints

Andrew McCallum, Ari Kobren, Barna Saha, 2019, KDD.

Expertise modeling for matching papers with reviewers

Andrew McCallum, David M. Mimno, A. McCallum, 2007, KDD '07.

Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples

Andrew McCallum, Erik G. Learned-Miller, Haw-Shiuan Chang, 2017, NIPS.

Using Genetic Algorithms to Learn Disjunctive Rules from Examples

Andrew McCallum, Kent A. Spackman, A. McCallum, 1990, ML.

Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

Andrew McCallum, Amr Ahmed, Manzil Zaheer, 2019, KDD.

A Probabilistic Model for Topic and Role Discovery in Social Networks and Message Text

Andrew McCallum, Xuerui Wang, Andrés Corrada-Emmanuel, 2022 .

Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval

A. McCallum, Xuerui Wang, Xing Wei, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Open Scholarship and Peer Review: a Time for Experimentation

Andrew McCallum, David A W Soergel, Adam Saunders, 2013 .

Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction pdf

Andrew McCallum, Emma Strubell, Patrick Verga, 2018, NAACL.

Structured Relation Discovery using Generative Models

Andrew McCallum, Aria Haghighi, Sebastian Riedel, 2011, EMNLP.

End-to-End Learning for Structured Prediction Energy Networks

Andrew McCallum, Bishan Yang, David Belanger, 2017, ICML.

SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications pdf

Isabelle Augenstein, Andrew McCallum, Sebastian Riedel, 2017, *SEMEVAL.

Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks pdf

Rajarshi Das, Andrew McCallum, Manzil Zaheer, 2017, ACL.

Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling

A. McCallum, Kathryn Ricci, Haw-Shiuan Chang, 2022, ACL.

Compositional Vector Space Models for Knowledge Base Inference

Andrew McCallum, Benjamin Roth, Arvind Neelakantan, 2015, AAAI Spring Symposia.

Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining

Kelsey R. Allen, M. Zaheer, A. McCallum, 2023, AISTATS.

Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures

Xiang Li, Andrew McCallum, Shikhar Murty, 2018, ACL.

Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions pdf

Andrew McCallum, David Belanger, Emma Strubell, 2017, ArXiv.

Joint Inference for Natural Language Processing

Andrew McCallum, A. McCallum, 2009, CoNLL.

Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

Andrew McCallum, Tsendsuren Munkhdalai, Trapit Bansal, 2021, EMNLP.

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

Andrew McCallum, David Belanger, Emma Strubell, 2017, EMNLP.

Learning a Natural Language Interface with Neural Programmer pdf

Martín Abadi, Quoc V. Le, Andrew McCallum, 2016, ICLR.

A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset

Rajarshi Das, Andrew McCallum, Kartik Talamadupula, 2018, QA@ACL.

Learning and inference in weighted logic with application to natural language processing

Andrew McCallum, Aron Culotta, A. McCallum, 2008 .

First-Order Probabilistic Models for Coreference Resolution

Andrew McCallum, Aron Culotta, Michael L. Wick, 2007, NAACL.

Database of NIH grants using machine-learned categories and graphical clustering

Andrew McCallum, David Newman, David Mimno, 2011, Nature Methods.

Probabilistic Representations for Integrating Unreliable Data Sources

Andrew McCallum, Gerome Miklau, David Mimno, 2007 .

DAG-Structured Clustering by Nearest Neighbors

Andrew McCallum, Amr Ahmed, Manzil Zaheer, 2021, AISTATS.

Multi-way distributional clustering via pairwise interactions

Ran El-Yaniv, Andrew McCallum, Ron Bekkerman, 2005, ICML.

FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs

Andrew McCallum, Sameer Singh, Karl Schultz, 2009, NIPS.

FACTORIE: Efficient Probabilistic Programming for Relational Factor Graphs via Imperative Declarations of Structure, Inference and Learning

Andrew McCallum, Sameer Singh, Michael Wick, 2008 .

Unsupervised deduplication using cross-field dependencies

Andrew McCallum, Charles A. Sutton, Robert J. Hall, 2008, KDD.

A Hierarchical Algorithm for Extreme Clustering

Akshay Krishnamurthy, Andrew McCallum, Ari Kobren, 2017, KDD.

Information extraction, data mining and joint inference

Andrew McCallum, A. McCallum, 2006, KDD '06.

Structured topic models: jointly modeling words and their accompanying modalities

Andrew McCallum, Xuerui Wang, A. McCallum, 2009 .

From the journals …

Martin C. Cooper, Paul A. Viola, Mark Junjie Li, 2007, The Knowledge Engineering Review.

Multilingual Relation Extraction using Compositional Universal Schema pdf

Andrew McCallum, Benjamin Roth, David Belanger, 2015, NAACL.

A Note on Topical N-grams

Andrew McCallum, Xuerui Wang, A. McCallum, 2005 .

Bibliometric impact measures leveraging topic analysis

Gideon S. Mann, Andrew McCallum, David Mimno, 2006, Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '06).

Joint Group and Topic Discovery from Relations and Text

Andrew McCallum, Xuerui Wang, Natasha Mohanty, 2006, SNA@ICML.

A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes

A. McCallum, Dongxu Zhang, M. Torkar, 2022, LREC.

Machine Reading Comprehension using Case-based Reasoning

M. Zaheer, R. Das, Hannaneh Hajishirzi, 2023, ArXiv.

Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models pdf

Mohit Iyyer, Simeng Sun, Andrew McCallum, 2020, ACL.

A Note on the Unification of Information Extraction and Data Mining using Conditional-Probability, Relational Models

Andrew McCallum, David Jensen, David D. Jensen, 2003 .

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Andrew McCallum, Kevin Huang, Stefanie Jegelka, 2018, J. Chem. Inf. Model..

Collective multi-label classification

A. McCallum, Nadia Ghamrawi, 2005, CIKM '05.

Clustering-based Inference for Biomedical Entity Linking

Andrew McCallum, Rico Angell, Nicholas Monath, 2020, NAACL.

MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text

Andrew McCallum, Elsa Olivetti, Sheshera Mysore, 2021, EMNLP.

Sparse Forward-Backward Using Minimum Divergence Beams for Fast Training Of Conditional Random Fields

Christopher Joseph Pal, Andrew McCallum, Charles A. Sutton, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Improved Latent Tree Induction with Distant Supervision via Span Constraints pdf

Andrew McCallum, Mohit Iyyer, Tim O'Gorman, 2021, EMNLP.

Adaptive Selection of Anchor Items for CUR-based k-NN search with Cross-Encoders

M. Zaheer, A. McCallum, Nishant Yadav, 2023, arXiv.org.

Using EM to Classify Text from Labeled and Unlabeled Documents

Sebastian Thrun, Tom M. Mitchell, Andrew McCallum, 1998 .

Advances in Web Mining and Web Usage Analysis, 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Philadelphia, PA, USA, August 20, 2006, Revised Papers

John Yen, Jaideep Srivastava, Myra Spiliopoulou, 2007, WebKDD.

Ask the GRU: Multi-task Learning for Deep Text Recommendations

Andrew McCallum, Trapit Bansal, David Belanger, 2016, RecSys.

Scalable Hierarchical Clustering with Tree Grafting

Akshay Krishnamurthy, Andrew McCallum, Ari Kobren, 2019, KDD.

Information Extraction with HMMs and Shrinkage

Andrew McCallum, Dayne Freitag, Dayne Freitag, 1999 .

Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders

Mohit Iyyer, Andrew McCallum, Subendhu Rongali, 2020, EMNLP.

Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks

A. McCallum, Dongxu Zhang, Gopal Sharma, 2018, NeurIPS.

Selecting actions for resource-bounded information extraction using reinforcement learning

Andrew McCallum, Pallika H. Kanani, A. McCallum, 2012, WSDM '12.

Answering Compositional Queries with Set-Theoretic Embeddings

A. McCallum, Steffen Rendle, S. Dasgupta, 2023, ArXiv.

Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering pdf

Rajarshi Das, Andrew McCallum, Hamed Zamani, 2019, EMNLP.

Improving Local Identifiability in Probabilistic Box Embeddings

L. Vilnis, A. McCallum, Michael Boratko, 2020, NeurIPS.

Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis

John Yen, Jaideep Srivastava, Myra Spiliopoulou, 2007, KDD 2007.

Table extraction for answer retrieval

W. Bruce Croft, Andrew McCallum, Xing Wei, 2006, Information Retrieval.

Compact Representation of Uncertainty in Hierarchical Clustering pdf

Andrew McCallum, Kyle Cranmer, Craig S. Greenberg, 2020, ArXiv.

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

Andrew McCallum, Haw-Shiuan Chang, Luke Vilnis, 2017, NAACL.

ACL-05 Feature Engineering for Machine Learning in Natural Language Processing

Dilek Z. Hakkani-Tür, Eric K. Ringger, Francine R. Chen, 2022 .

Cryptogram decoding for optical character recognition

Andrew McCallum, Erik Learned-Miller, Gary B. Huang, 2006 .

The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures pdf

Andrew McCallum, Jeffrey Flanigan, Sheshera Mysore, 2019, LAW@ACL.

Automatically Extracting Action Graphs from Materials Science Synthesis Procedures pdf

Andrew McCallum, Kevin Huang, Edward Kim, 2017, ArXiv.

Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension pdf

Rajarshi Das, Andrew McCallum, Tsendsuren Munkhdalai, 2018, ICLR.

Monte Carlo MCMC: Efficient Inference by Sampling Factors

Andrew McCallum, Michael L. Wick, Sameer Singh, 2012, AKBC-WEKEX@NAACL-HLT.

DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions

Pranay Kumar Yelugam, R. Das, A. McCallum, 2021, NAACL.

Large Language Model Augmented Narrative Driven Recommendations

A. McCallum, Hamed Zamani, Sheshera Mysore, 2023, ArXiv.

You can't pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LM

Andrew Drozdov, A. McCallum, Mohit Iyyer, 2022, EMNLP.

CSFCube - A Test Collection of Computer Science Research Articles for Faceted Query by Example

A. McCallum, Hamed Zamani, Timothy J. O'Gorman, 2021, NeurIPS Datasets and Benchmarks.

Text Classification by Bootstrapping with Keywords, EM and Shrinkage

Andrew McCallum, Kamal Nigam, A. McCallum, 1999 .

Clustering-based Inference for Zero-Shot Biomedical Entity Linking pdf

Rico Angell, Nicholas Monath, Andrew McCallum, 2020, ArXiv.

Inducing Value Sparsity for Parallel Inference in Tree-shaped Models

Andrew McCallum, Sameer Singh, A. McCallum, 2011 .

Efficient training methods for conditional random fields

Andrew McCallum, Charles Sutton, Charles Sutton, 2008 .

People-LDA: Anchoring Topics to People using Face Recognition

Andrew McCallum, Erik G. Learned-Miller, Vidit Jain, 2007, 2007 IEEE 11th International Conference on Computer Vision.

CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases

R. Das, A. McCallum, I. Abdelaziz, 2022, ArXiv.

AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

Jun Ma, Andrew McCallum, Xin Luna Dong, 2020, KDD.

Improved Representation Learning for Predicting Commonsense Ontologies pdf

Xiang Li, Andrew McCallum, Luke Vilnis, 2017, ArXiv.

Penn/Umass/CHOP Biocreative II systems

Koby Crammer, Yang Jin, Gideon S. Mann, 2007 .

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

A. McCallum, Muhao Chen, Michael Boratko, 2021, NAACL.

Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond

A. McCallum, Haw-Shiuan Chang, Hong Yu, 2023, ACL.

Transition-based Dependency Parsing with Selectional Branching

Andrew McCallum, Jinho D. Choi, A. McCallum, 2013, ACL.

Information Extraction

Andrew McCallum, A. McCallum, 2005, ACM Queue.

You can't pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LM

Andrew Drozdov, A. McCallum, Mohit Iyyer, 2022, EMNLP.

Scalable Hierarchical Agglomerative Clustering

M. Zaheer, A. McCallum, Amr Ahmed, 2020, KDD.

Scalable Bottom-Up Hierarchical Clustering pdf

Andrew McCallum, Yuan Wang, Marc Najork, 2020, ArXiv.

An Online Hierarchical Algorithm for Extreme Clustering pdf

Akshay Krishnamurthy, Andrew McCallum, Ari Kobren, 2017, ArXiv.

M ODELING L ABEL S PACE I NTERACTIONS IN M ULTI LABEL C LASSIFICATION USING B OX E MBEDDINGS

Jay Yoon Lee, A. McCallum, Michael Boratko, 2022 .

Learning to Predict the Quality of Contributions to Wikipedia

Andrew McCallum, Gerome Miklau, Gregory Druck, 2008 .

Box Embeddings: An open-source library for representation learning using geometric structures pdf

Tejas Chheda, Andrew McCallum, Michael Boratko, 2021, EMNLP.

Representing Joint Hierarchies with Box Embeddings

Andrew McCallum, Xiang Li, Luke Vilnis, 2020, AKBC.

Word2Box: Learning Word Representation Using Box Embeddings pdf

Andrew McCallum, Michael Boratko, Shib Sankar Dasgupta, 2021, ArXiv.

Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders

Andrew McCallum, Mohit Iyyer, Patrick Verga, 2019, EMNLP.

KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals

Seyed Mehran Kazemi, A. McCallum, Deepak Ramachandran, 2023, SUSTAINLP.

SampleRank: Training Factor Graphs with Atomic Gradients

Andrew McCallum, Aron Culotta, Michael L. Wick, 2011, ICML.

Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema pdf

Andrew McCallum, Patrick Verga, Arvind Neelakantan, 2016, EACL.

Undirected and Interpretable Continuous Topic Models of Documents

Koby Crammer, Andrew McCallum, Xuerui Wang, 2007 .

Modeling Career Path Trajectories

Andrew McCallum, David Mimno, A. McCallum, 2008 .

Supervised Hierarchical Clustering with Exponential Linkage

Andrew McCallum, Ari Kobren, Nicholas Monath, 2019, ICML.

Semi-Supervised Text Classification Using EM

Tom M. Mitchell, Kamal Nigam, Andrew McCallum, 2006, Semi-Supervised Learning.

Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets

Andrew McCallum, Patrick Verga, Trapit Bansal, 2018, EMNLP.

Learning Extractors from Unlabeled Text using Relevant Databases

Andrew McCallum, Kedar Bellare, A. McCallum, 2007 .

Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications pdf

Andrew McCallum, Amol Agrawal, Haw-Shiuan Chang, 2021, AAAI.

Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings pdf

Andrew McCallum, Vinayak Mathur, Haw-Shiuan Chang, 2018, TextGraphs@NAACL-HLT.

Generalized component analysis for text with heterogeneous attributes

Christopher Joseph Pal, Andrew McCallum, Xuerui Wang, 2007, KDD '07.

A Continuous-Time Model of Topic Co-occurrence Trends

Wei Li, Andrew McCallum, Xuerui Wang, 2006 .

Group and topic discovery from relations and text

Andrew McCallum, Xuerui Wang, Natasha Mohanty, 2005, LinkKDD '05.

Topic regression

Andrew McCallum, David M. Mimno, A. McCallum, 2011 .

Gibbs Sampling for Logistic Normal Topic Models with Graph-Based Priors

Andrew McCallum, Hanna M. Wallach, David Mimno, 2008 .

Database of NIH grants using machine-learned categories and graphical clustering

Hanna M. Wallach, A. McCallum, Gully A. Burns, 2011, Nature Methods.

Low-Resource Compositional Semantic Parsing with Concept Pretraining

A. McCallum, Konstantine Arkoudas, Subendhu Rongali, 2023, EACL.

Reducin gLabelin gEffor tfo rStructure dPrediction Tasks

Andrew McCallum, Aron Culotta, A. McCallum, 2005 .

Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling pdf

Andrew McCallum, David Belanger, Shikhar Murty, 2017, ArXiv.

Conditional Models of Identity Uncertainty with Application to Noun Coreference

Andrew McCallum, Ben Wellner, A. McCallum, 2004, NIPS.

Longtonotes: OntoNotes with Longer Coreference Chains

M. Zaheer, A. McCallum, Mrinmaya Sachan, 2022, FINDINGS.

Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition pdf

Andrew McCallum, Shankar Vembu, Haw-Shiuan Chang, 2019, ArXiv.

Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning

Christopher Joseph Pal, Andrew McCallum, B. Michael Kelm, 2006, 18th International Conference on Pattern Recognition (ICPR'06).

Learning a Natural Language Interface with Neural Programmer

Quoc V. Le, Quoc V. Le, Dario Amodei, 2016, ICLR.

Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference

Rajarshi Das, Andrew McCallum, Manzil Zaheer, 2019, EMNLP.

Efﬁcient Computation of Entropy Gradient for Semi-Supervised Conditional Random Fields

Gideon S. Mann, A. McCallum, 2019 .

Min/max stability and box distributions

Andrew McCallum, Michael Boratko, Shib Sankar Dasgupta, 2021, UAI.

Finer Grained Entity Typing with TypeNet pdf

Andrew McCallum, Shikhar Murty, Patrick Verga, 2017, AKBC@NIPS.

Joint inference of entities, relations, and coreference

Andrew McCallum, Sameer Singh, Sebastian Riedel, 2013, AKBC '13.

Disambiguating Web appearances of people in a social network

Andrew McCallum, Ron Bekkerman, R. Bekkerman, 2005, WWW '05.

Classification Models for New Event Detection

James Allan, Andrew McCallum, Girdhar Kumaran, 2004 .

Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference

Andrew McCallum, Ben Wellner, A. McCallum, 2003, IIWeb.

Transfer Learning for Enhancing Information Flow in Organizations and Social Networks

Andrew McCallum, Chris Pal, Xuerui Wang, 2007 .

Piecewise pseudolikelihood for efficient training of conditional random fields

Charles Sutton, A. McCallum, 2007, ICML '07.

Text Classification from Labeled and Unlabeled Documents using EM

Tom Michael Mitchell, S. Thrun, A. McCallum, 2000, Machine Learning.

Toward interactive training and evaluation

Andrew McCallum, Gregory Druck, A. McCallum, 2011, CIKM '11.

Monte Carlo MCMC: Efficient Inference by Approximate Sampling

Andrew McCallum, Michael L. Wick, Sameer Singh, 2012, EMNLP.

Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002

Djoerd Hiemstra, Alan F. Smeaton, Nicholas J. Belkin, 2003, SIGF.

Adaptive Selection of Anchor Items for CUR-based k-NN search with Cross-Encoders

M. Zaheer, A. McCallum, Nishant Yadav, 2023, ArXiv.

Machine Reading Comprehension using Case-based Reasoning

M. Zaheer, Hannaneh Hajishirzi, A. McCallum, 2023, EMNLP.

Drug Repositioning using Consilience of Knowledge Graph Completion Methods

A. McCallum, S. Dhuliawala, M. Sinha, 2023, bioRxiv.

A Discriminative Hierarchical Model for Fast Coreference at Large Scale

Andrew McCallum, Michael L. Wick, Sameer Singh, 2012, ACL.

Joint deduplication of multiple record types in relational data

Andrew McCallum, Aron Culotta, A. McCallum, 2005, CIKM '05.

Probabilistic Reasoning about Human Edits in Information Integration

Andrew McCallum, Ari Kobren, Michael Wick, 2013 .

Learning Soft Linear Constraints with Application to Citation Field Extraction pdf

Andrew McCallum, David Belanger, Alexandre Passos, 2014, ACL.

A joint model for discovering and linking entities

Andrew McCallum, Michael L. Wick, Sameer Singh, 2013, AKBC '13.

Distantly Labeling Data for Large Scale Cross-Document Coreference pdf

Andrew McCallum, Michael L. Wick, Sameer Singh, 2010, ArXiv.

Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference

Andrew McCallum, Michael L. Wick, Sameer Singh, 2009, NIPS.

Bi-directional Joint Inference for Entity Resolution and Segmentation Using Imperatively-Defined Factor Graphs

Andrew McCallum, Sameer Singh, Karl Schultz, 2009, ECML/PKDD.

A unified approach for schema matching, coreference and canonicalization

Andrew McCallum, Michael L. Wick, Khashayar Rohanimanesh, 2008, KDD.

An Entity Based Model for Coreference Resolution

Andrew McCallum, Aron Culotta, Michael L. Wick, 2009, SDM.

Advances in Learning and Inference for Partition-wise Models of Coreference Resolution

Michael L. Wick, A. McCallum, 2009 .

Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

Andrew McCallum, Michael Wick, Aron Culotta, 2007 .

Rapid development of Hindi named entity recognition using conditional random fields and feature induction

Wei Li, Andrew McCallum, A. McCallum, 2003, TALIP.

An Introduction to Conditional Random Fields

Charles Sutton, A. McCallum, 2010, Found. Trends Mach. Learn..

Training Structured Prediction Energy Networks with Indirect Supervision

Andrew McCallum, Aishwarya Kamath, Amirmohammad Rooshenas, 2018, NAACL.

Simple, robust, scalable semi-supervised learning via expectation regularization

Gideon S. Mann, A. McCallum, 2007, ICML '07.

Polylingual Topic Models

David A. Smith, Hanna M. Wallach, A. McCallum, 2009, EMNLP.

Bayesian Modeling of Dependency Trees Using Hierarchical Pitman-Yor Priors

Andrew McCallum, Hanna M. Wallach, Charles Sutton, 2008 .

Learning to Create Customized Authority Lists

Andrew McCallum, David Cohn, Huan Chang, 2000, ICML.

Monte Carlo MCMC: Efficient Inference by Approximate Sampling

Michael L. Wick, A. McCallum, Sameer Singh, 2012, EMNLP.

RelNet: End-to-end Modeling of Entities & Relations pdf

Andrew McCallum, Trapit Bansal, Arvind Neelakantan, 2017, AKBC@NIPS.

Editable User Profiles for Controllable Text Recommendations

A. McCallum, Hamed Zamani, Mahmood Jasim, 2023, SIGIR.

Text Classification from Labeled and Unlabeled Documents using EM

Tom Michael Mitchell, S. Thrun, A. McCallum, 2000, Machine Learning.

Dependency Parsing with Dilated Iterated Graph CNNs pdf

Andrew McCallum, Emma Strubell, A. McCallum, 2017, SPNLP@EMNLP.

Creating customized authority lists

David A. Cohn, Andrew McCallum, Huan Chang, 1999, ICML 1999.

Row-less Universal Schema

Andrew McCallum, Patrick Verga, A. McCallum, 2016, AKBC@NAACL-HLT.