Andrew McCallum

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

NIPS Workshop on Learning with Structured Outputs

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

Dependency Parsing with Dilated Iterated Graph CNNs pdf

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

Learning Field Compatibilities to Extract Database Records from Unstructured Text

Andrew McCallum, Aron Culotta, Michael L. Wick, 2006, EMNLP.

Optimizing Semantic Coherence in Topic Models

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

Query-Aware MCMC

Andrew McCallum, Michael L. Wick, M. Wick, 2011, NIPS.

Faust: Flexible Acquistion and Understanding System for Text

Andrew McCallum, Christopher D. Manning, Daniel S. Weld, 2013 .

Structured Relation Discovery using Generative Models

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

Energy and Policy Considerations for Deep Learning in NLP pdf

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

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

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

A feasibility study on "a environmentally friendly ferry for use on inland waterways"

Andrew McCallum, A. McCallum, 2008 .

Joint Parsing and Semantic Role Labeling

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

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

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

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

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

Topic and Role Discovery in Social Networks

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

Expertise modeling for matching papers with reviewers

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

Resource-Bounded Information Gathering for Correlation Clustering

Andrew McCallum, Pallika H. Kanani, 2007, COLT.

Structured topic models: jointly modeling words and their accompanying modalities

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

NIPS Workshop on Syntax, Semantics, and Statistics

Andrew McCallum, Khashayar Rohanimanesh, Charles Sutton, 2003 .

On Discriminative and Semi-Supervised Dimensionality Reduction

Andrew McCallum, Chris Pal, Xuerui Wang, 2006 .

Learning with Scope, with Application to Information Extraction and Classification

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

Model Combination for Event Extraction in BioNLP 2011

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

Learning String Alignments for Entity Aliases

Rajarshi Das, Andrew McCallum, Nicholas Monath, 2017, AKBC@NIPS.

Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions pdf

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

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

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

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

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

ACOSS View: Welcome to the First Edition of Impact for 2003 - the Special ACOSS Congress Edition

Andrew McCallum, A. McCallum, 2003 .

Piecewise pseudolikelihood for efficient training of conditional random fields

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

Improved Dynamic Schedules for Belief Propagation

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

WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report

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

Bethe Projections for Non-Local Inference pdf

Andrew McCallum, David Belanger, Daniel Sheldon, 2015, UAI.

Using Reinforcement Learning to Spider the Web Efficiently

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

Learning to Predict the Quality of Contributions to Wikipedia

Andrew McCallum, Gerome Miklau, Gregory Druck, 2008 .

Sparse Forward-Backward for Fast Training of Conditional Random Fields

Andrew McCallum, Chris Pal, Charles Sutton, 2006 .

Fast and Robust Joint Models for Biomedical Event Extraction

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

Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding pdf

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

Efficient methods for topic model inference on streaming document collections

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

Universal Schema for Slot Filling and Cold Start: UMass IESL at TACKBP 2013

Andrew McCallum, Ari Kobren, Sameer Singh, 2013, TAC.

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

Andrew McCallum, Gopal Sharma, Amirmohammad Rooshenas, 2019, NeurIPS.

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..

Efficiently Inducing Features of Conditional Random Fields pdf

Andrew McCallum, A. McCallum, 2002, UAI.

Information Extraction from the World Wide Web

Andrew McCallum, William W. Cohen, Martin Baker, 2005 .

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

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

A Note on Topical N-grams

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

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

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

Compact Representation of Uncertainty in Clustering

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

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

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

e-Science for the Nanomanufacturing Community

James Allan, Mark D. Smucker, Andrew McCallum, 2008 .

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

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

Lightly-Supervised Attribute Extraction

Fernando Pereira, Mark Dredze, Andrew McCallum, 2007 .

Pachinko allocation: DAG-structured mixture models of topic correlations

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

Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction pdf

Andrew McCallum, Nicholas Monath, Boris Veytsman, 2020, AKBC.

An Introduction to Conditional Random Fields

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

Tractable Learning and Inference with High-Order Representations

Andrew McCallum, Aron Culotta, 2006 .

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

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

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

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

Classification with Hybrid Generative/Discriminative Models

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

Information Extraction with HMM Structures Learned by Stochastic Optimization

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

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

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

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.

Exploring the use of conditional random field models and HMMs for historical handwritten document recognition

Andrew McCallum, R. Manmatha, Shaolei Feng, 2006, Second International Conference on Document Image Analysis for Libraries (DIAL'06).

Multi-Conditional Learning for Joint Probability Models with Latent Variables

Andrew McCallum, Chris Pal, Xuerui Wang, 2006 .

Overcoming Incomplete Perception with Utile Distinction Memory

Andrew McCallum, A. McCallum, 1993, ICML.

Reducin gLabelin gEffor tfo rStructure dPrediction Tasks

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

Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression

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

FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs

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

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

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

Corrective feedback and persistent learning for information extraction

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

Joint Group and Topic Discovery from Relations and Text

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

Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing

Jeff A. Bilmes, Andrew McCallum, Fernando Pereira, 2006 .

Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling pdf

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

Human-machine cooperation with epistemological DBs: supporting user corrections to knowledge bases

Andrew McCallum, Michael Wick, Karl Schultz, 2012 .

Data Structures & Algorithms for Exact Inference in Hierarchical Clustering.

Andrew McGregor, Andrew McCallum, Kyle Cranmer, 2020 .

Table extraction for answer retrieval

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

Generalized component analysis for text with heterogeneous attributes

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

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

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

Ecient Web Spidering with Reinforcement Learning

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

Distributed MAP Inference for Undirected Graphical Models

Fernando Pereira, Andrew McCallum, Sameer Singh, 2010 .

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

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

Robust Biomedical Event Extraction with Dual Decomposition and Minimal Domain Adaptation

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

Entity-centric Attribute Feedback for Interactive Knowledge Bases

Andrew McCallum, Ari Kobren, Nicholas Monath, 2017, AKBC@NIPS.

Polylingual Topic Models

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

Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches

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

Joint inference of entities, relations, and coreference

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

Training Structured Prediction Energy Networks with Indirect Supervision

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

Multilingual Relation Extraction using Compositional Universal Schema pdf

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

Monte Carlo MCMC: Efficient Inference by Approximate Sampling

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

Introduction to Statistical Relational Learning

Andrew McCallum, Charles Sutton, Charles Sutton, 2007 .

Compositional Vector Space Models for Knowledge Base Inference

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

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

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

Training for Fast Sequential Prediction Using Dynamic Feature Selection pdf

Andrew McCallum, Emma Strubell, Luke Vilnis, 2014, ArXiv.

Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema

Andrew McCallum, Ajay Nagesh, Emma Strubell, 2016, TAC.

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

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

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).

Universal schema for entity type prediction

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

Transfer Learning for Enhancing Information Flow in Organizations and Social Networks

Andrew McCallum, Chris Pal, Xuerui Wang, 2007 .

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

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

Roll Call Vote Prediction with Knowledge Augmented Models

Andrew McCallum, Arpit Singh, Sheshera Mysore, 2019, CoNLL.

A Machine Learning Approach to Building Domain-Specific Search Engines

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

Reducing Weight Undertraining in Structured Discriminative Learning

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

Speeding up MAP with Column Generation and Block Regularization

Andrew McCallum, David Belanger, Sebastian Riedel, 2012 .

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

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

Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria

Gideon S. Mann, Andrew McCallum, Gregory Druck, 2009, ACL/IJCNLP.

Optimal Transport-based Alignment of Learned Character Representations for String Similarity pdf

Rajarshi Das, Andrew McCallum, Ari Kobren, 2019, ACL.

Community-based Link Prediction with Text

Andrew McCallum, David Mimno, Hanna Wallach, 2007 .

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.

Semi-Supervised Sequence Modeling with Syntactic Topic Models

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

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).

Scalable probabilistic databases with factor graphs and MCMC

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

Using Transitional Proximity for Faster Reinforcement Learning

Andrew McCallum, A. McCallum, 1992, ML.

Interactive Information Extraction with Constrained Conditional Random Fields

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

Extracting social networks and contact information from email and the Web

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

Reducing Labeling Effort for Structured Prediction Tasks

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

Canonicalization of database records using adaptive similarity measures

Matthew Marzilli, Andrew McCallum, Aron Culotta, 2007, KDD '07.

Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand

Andrew McCallum, Shaohan Hu, Pallika H. Kanani, 2010, PAKDD.

Mixtures of hierarchical topics with Pachinko allocation

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

Alternating Projections for Learning with Expectation Constraints pdf

Andrew McCallum, Kedar Bellare, Gregory Druck, 2009, UAI.

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

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

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

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

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

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

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.

Constraint-Driven Rank-Based Learning for Information Extraction

Andrew McCallum, Sameer Singh, Sebastian Riedel, 2010, HLT-NAACL.

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

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

Modeling Relations and Their Mentions without Labeled Text

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

Linguistically-Informed Self-Attention for Semantic Role Labeling

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

Row-less Universal Schema

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

A joint model for discovering and linking entities

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

Bayesian Modeling of Dependency Trees Using Hierarchical Pitman-Yor Priors

Andrew McCallum, Charles Sutton, Hanna Wallach, 2008 .

Chinese Segmentation and New Word Detection using Conditional Random Fields

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

Cora: Computer science research paper search engine

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

Selecting actions for resource-bounded information extraction using reinforcement learning

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

Collective Segmentation and Labeling of Distant Entities in Information Extraction

Andrew McCallum, Charles Sutton, Charles Sutton, 2004 .

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

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

Word Representations via Gaussian Embedding pdf

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

Towards Asynchronous Distributed MCMC Inference for Large Graphical Models

Andrew McCallum, Sameer Singh, 2011 .

A unified approach for schema matching, coreference and canonicalization

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

Learning Soft Linear Constraints with Application to Citation Field Extraction pdf

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

OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference

Andrew McCallum, Subhabrata Mukherjee, Xin Dong, 2019, NAACL-HLT.

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

Fernando Pereira, Andrew McCallum, Sameer Singh, 2012 .

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

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

Dynamic Conditional Random Fields for Jointly Labeling Multiple Sequences

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

Text Classification by Bootstrapping with Keywords, EM and Shrinkage

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

Energy and Policy Considerations for Modern Deep Learning Research

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

Reinforcement learning with selective perception and hidden state

Andrew McCallum, Dana H. Ballard, D. Ballard, 1996 .

Inference by Minimizing Size, Divergence, or their Sum

Andrew McCallum, David A. Smith, Sebastian Riedel, 2010, UAI.

Semi-Supervised Clustering with User Feedback

Andrew McCallum, Rich Caruana, David Cohn, 2003 .

Instance-Based State Identification for Reinforcement Learning

Andrew McCallum, A. McCallum, 1994, NIPS.

Improving Author Coreference by Resource-Bounded Information Gathering from the Web

Christopher Joseph Pal, Andrew McCallum, Pallika H. Kanani, 2007, IJCAI.

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

Andrew McCallum, Ramesh Sitaraman, Pallika Kanani, 2009 .

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

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

MAP Inference in Chains using Column Generation

Andrew McCallum, Sebastian Riedel, David Belanger, 2012, NIPS.

Learning a Natural Language Interface with Neural Programmer pdf

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

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

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

Efficient Strategies for Improving Partitioning-Based Author Coreference by Incorporating Web Pages as Graph Nodes

Andrew McCallum, Pallika Kanani, 2007 .

Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders

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

Confidence Estimation for Information Extraction

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

An Entity Based Model for Coreference Resolution

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

Anytime Belief Propagation Using Sparse Domains pdf

Andrew McCallum, Sameer Singh, Sebastian Riedel, 2013, ArXiv.

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

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

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

Andrew McCallum, Charles Sutton, Charles Sutton, 2005 .

Joint Inference for Natural Language Processing

Andrew McCallum, A. McCallum, 2009, CoNLL.

Learning Dynamic Feature Selection for Fast Sequential Prediction pdf

Andrew McCallum, Kate Silverstein, Emma Strubell, 2015, ACL.

An Introduction to Conditional Random Fields for Relational Learning

Andrew McCallum, Charles Sutton, Charles Sutton, 2007 .

SampleRank: Training Factor Graphs with Atomic Gradients

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

Resource-bounded information acquisition and learning

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

Reports of the 2013 AAAI Spring Symposium Series

Sean Andrist, Takashi Kido, Andrew McCallum, 2013, AI Mag..

Learning to Select Actions for Resource-bounded Information Extraction

Andrew McCallum, P. Kinani, 2011 .

A2N: Attending to Neighbors for Knowledge Graph Inference

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

Active Learning by Labeling Features

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

Gene Prediction with Conditional Random Fields

Andrew McCallum, Aron Culotta, David Kulp, 2005 .

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.

Probabilistic Databases of Universal Schema

Andrew McCallum, Sebastian Riedel, Limin Yao, 2012, AKBC-WEKEX@NAACL-HLT.

An Exploration of Entity Models, Collective Classification and Relation Description

James Allan, Andrew McCallum, Hema Raghavan, 2004 .

Piecewise training for structured prediction

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

The author-recipient-topic model for topic and role discovery in social networks

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

Fast, Piecewise Training for Discriminative Finite-state and Parsing Models

Andrew McCallum, Charles Sutton, 2005 .

First-Order Probabilistic Models for Coreference Resolution

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

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.

Machine Translation Using Overlapping Alignments and SampleRank

Andrew McCallum, Marc Dymetman, Nicola Cancedda, 2009 .

Disambiguating Web appearances of people in a social network

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

Information extraction, data mining and joint inference

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

Latent Relation Representations for Universal Schemas pdf

Andrew McCallum, Sebastian Riedel, Limin Yao, 2013, ICLR.

Learning from labeled features using generalized expectation criteria

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

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

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

Object Consolodation by Graph Partitioning with a Conditionally›Trained Distance Metric

Andrew McCallum, Ben Wellner, 2003 .

Learning to Speed Up MAP Decoding with Column Generation

Andrew McCallum, David Belanger, Sebastian Riedel, 2012 .

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

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

A New Dataset for Fine Grained Citation Field Extraction (Author's Manuscript)

Andrew McCallum, Sam Anzaroot, 2013 .

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

Andrew McCallum, Sameer Singh, Michael Wick, 2008 .

Embedded-State Latent Conditional Random Fields for Sequence Labeling

Andrew McCallum, Shikhar Murty, Luke Vilnis, 2018, CoNLL.

Learning to construct knowledge bases from the World Wide Web

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

Combining joint models for biomedical event extraction

Andrew McCallum, Mihai Surdeanu, Christopher D. Manning, 2012, BMC Bioinformatics.

Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State

Andrew McCallum, A. McCallum, 1995, ICML.

Accurate Information Extraction from Research Papers using Conditional Random Fields

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

Information Extraction

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

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

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

Scalable Hierarchical Clustering with Tree Grafting

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

Group and topic discovery from relations and text

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

Automating the Construction of Internet Portals with Machine Learning

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

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.

Dynamic Knowledge-Base Alignment for Coreference Resolution

Andrew McCallum, Sameer Singh, Jinho D. Choi, 2013, CoNLL.

Probabilistic Representations for Integrating Unreliable Data Sources

Andrew McCallum, Gerome Miklau, David Mimno, 2007 .

Smoothing the Geometry of Probabilistic Box Embeddings

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

An Interoperable Multimedia Catalog System for Electronic Commerce.

Andrew McCallum, William W. Cohen, Dallan W. Quass, 2000 .

Conditional Models of Identity Uncertainty with Application to Noun Coreference

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

Composition of Conditional Random Fields for Transfer Learning

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

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.

Message Passing for Soft Constraint Dual Decomposition

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

A Conditional Model of Deduplication for Multi-Type Relational Data

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

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

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

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

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

Using Maximum Entropy for Text Classification

Andrew McCallum, John Lafferty, Kamal Nigam, 1999 .

Embedded Representations of Lexical and Knowledge-Base Semantics

Andrew McCallum, 2015, ICTIR.

MAP inference in Large Factor Graphs with Reinforcement Learning

Andrew McCallum, Michael Wick, Sameer Singh, 2008 .

Instance-Based Utile Distinctions for Reinforcement Learning

Andrew McCallum, A. McCallum, 1995 .

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

Andrew McCallum, Michael Wick, Aron Culotta, 2007 .

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

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

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

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

Learning to Create Customized Authority Lists

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

Topic models for taxonomies

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

Information extraction from research papers using conditional random fields

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

Changing the Mind of Transformers for Topically-Controllable Language Generation

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

Scalable Hierarchical Agglomerative Clustering

Andrew McCallum, Yuan Wang, Marc Najork, 2021, KDD.

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

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

Word2Box: Learning Word Representation Using Box Embeddings pdf

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

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

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

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

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

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

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

Entity Linking and Discovery via Arborescence-based Supervised Clustering pdf

Andrew McCallum, Rico Angell, Dhruv Agarwal, 2021, ArXiv.

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning pdf

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

Improved Latent Tree Induction with Distant Supervision via Span Constraints pdf

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

Exact and Approximate Hierarchical Clustering Using A

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

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

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

DAG-Structured Clustering by Nearest Neighbors

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

Min/max stability and box distributions

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

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

Andrew McCallum, Daniel Gillick, Jan A. Botha, 2021, ACL/IJCNLP.

Sublinear Time Approximation of Text Similarity Matrices pdf

Andrew McCallum, Cameron Musco, Archan Ray, 2021, ArXiv.

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

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

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

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

A Dataset for Discourse Structure in Peer Review Discussions pdf

Rajarshi Das, Andrew McCallum, Hamed Zamani, 2021, ArXiv.

Clustering-based Inference for Biomedical Entity Linking

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

Multi-facet Universal Schema

Andrew McCallum, Rohan Paul, Haw-Shiuan Chang, 2021, EACL.

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

Andrew McCallum, Hamed Zamani, Sheshera Mysore, 2021, ArXiv.

Domain Specific Knowledge Base Construction via Crowdsourcing

Andrew McCallum, Ari Kobren, Siddarth Sampangi, 2014 .

Correlations and Anticorrelations in LDA Inference

Andrew McCallum, Alexandre Passos, Hanna Wallach, 2011 .

Gradient-based Hierarchical Clustering

Akshay Krishnamurthy, Andrew McCallum, Ari Kobren, 2017 .

Optimization and Learning in F ACTORIE

Andrew McCallum, Luke Vilnis, Alexandre Passos, 2014 .

Pachinko Allocation: Scalable Mixture Models of Topic Correlations

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

1 Semi-Supervised Text Classification Using EM

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

Modeling Career Path Trajectories

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

Unsupervised Coreference of Publication Venues

Andrew McCallum, Charles Sutton, Charles Sutton, 2007 .

Scalable Bottom-Up Hierarchical Clustering pdf

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

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

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

Predicting Chemical Protein Relations with Biaffine Relation Attention Networks

Andrew McCallum, Patrick Verga, 2017 .

Inducing Value Sparsity for Parallel Inference in Tree-shaped Models

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