| 2026 | EACL | xLM: A Python Package for Non-Autoregressive Language Models. | Dhruvesh Patel, Durga Prasad Maram, Sai Sreenivas Chintha, Benjamin Rozonoyer, Andrew McCallum |
| 2025 | ICML | A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings. | Shib Sankar Dasgupta, Michael Boratko, Andrew McCallum |
| 2025 | SIGIR | Bridging Personalization and Control in Scientific Personalized Search. | Sheshera Mysore, Garima Dhanania, Kishor Patil, Surya Kallumadi, Andrew McCallum, Hamed Zamani |
| 2024 | ACL | Every Answer Matters: Evaluating Commonsense with Probabilistic Measures. | Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O'Gorman, Nalini Singh, Andrew McCallum, Xiang Li |
| 2024 | ACL | Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation. | Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md. Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum |
| 2024 | EMNLP | Analysis of Plan-based Retrieval for Grounded Text Generation. | Ameya Godbole, Nicholas Monath, Seungyeon Kim, Ankit Singh Rawat, Andrew McCallum, Manzil Zaheer |
| 2024 | EMNLP | Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval. | Jonghyun Song, Cheyon Jin, Wenlong Zhao, Andrew McCallum, Jay-Yoon Lee |
| 2024 | ICLR | Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders. | Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum |
| 2024 | ICML | Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching. | Rico Angell, Andrew McCallum |
| 2024 | ICML | A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. | Nicholas Monath, Will Sussman Grathwohl, Michael Boratko, Rob Fergus, Andrew McCallum, Manzil Zaheer |
| 2024 | WSDM | To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders. | Haw-Shiuan Chang, Nikhil Agarwal, Andrew McCallum |
| 2023 | ACL | Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling. | Haw-Shiuan Chang, Ruei-Yao Sun, Kathryn Ricci, Andrew McCallum |
| 2023 | ACL | Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond. | Haw-Shiuan Chang, Zonghai Yao, Alolika Gon, Hong Yu, Andrew McCallum |
| 2023 | ACL | Causal Matching with Text Embeddings: A Case Study in Estimating the Causal Effects of Peer Review Policies. | Raymond Zhang, Neha Nayak Kennard, Daniel Scott Smith, Daniel A. McFarland, Andrew McCallum, Katherine Keith |
| 2023 | AISTATS | Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining. | Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew McCallum |
| 2023 | EACL | Low-Resource Compositional Semantic Parsing with Concept Pretraining. | Subendhu Rongali, Mukund Sridhar, Haidar Khan, Konstantine Arkoudas, Wael Hamza, Andrew McCallum |
| 2023 | EACL | Longtonotes: OntoNotes with Longer Coreference Chains. | Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan |
| 2023 | EMNLP | PaRaDe: Passage Ranking using Demonstrations with LLMs. | Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui |
| 2023 | EMNLP | Machine Reading Comprehension using Case-based Reasoning. | Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum |
| 2023 | EMNLP | Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition. | Nishant Yadav, Nicholas Monath, Manzil Zaheer, Andrew McCallum |
| 2023 | ICLR | KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. | Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi |
| 2023 | KDD | Online Level-wise Hierarchical Clustering. | Nicholas Monath, Manzil Zaheer, Andrew McCallum |
| 2023 | RecSys | Large Language Model Augmented Narrative Driven Recommendations. | Sheshera Mysore, Andrew McCallum, Hamed Zamani |
| 2023 | SIGIR | Editable User Profiles for Controllable Text Recommendations. | Sheshera Mysore, Mahmood Jasim, Andrew McCallum, Hamed Zamani |
| 2022 | AAAI | An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity. | Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, Andrew McCallum |
| 2022 | AAAI | Sublinear Time Approximation of Text Similarity Matrices. | Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco |
| 2022 | ACL | Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions. | Haw-Shiuan Chang, Andrew McCallum |
| 2022 | ACL | Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings. | Shib Sankar Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum |
| 2022 | ACL | Event-Event Relation Extraction using Probabilistic Box Embedding. | EunJeong Hwang, Jay-Yoon Lee, Tianyi Yang, Dhruvesh Patel, Dongxu Zhang, Andrew McCallum |
| 2022 | COLING | Unsupervised Partial Sentence Matching for Cited Text Identification. | Kathryn Ricci, Haw-Shiuan Chang, Purujit Goyal, Andrew McCallum |
| 2022 | EMNLP | You can't pick your neighbors, or can you? When and How to Rely on Retrieval in the kNN-LM. | Andrew Drozdov, Shufan Wang, Razieh Rahimi, Andrew McCallum, Hamed Zamani, Mohit Iyyer |
| 2022 | EMNLP | Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization. | Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum |
| 2022 | ICLR | Modeling Label Space Interactions in Multi-label Classification using Box Embeddings. | Dhruvesh Patel, Pavitra Dangati, Jay-Yoon Lee, Michael Boratko, Andrew McCallum |
| 2022 | ICML | Interactive Correlation Clustering with Existential Cluster Constraints. | Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum |
| 2022 | ICML | Knowledge Base Question Answering by Case-based Reasoning over Subgraphs. | Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew McCallum |
| 2022 | LREC | Enhanced Distant Supervision with State-Change Information for Relation Extraction. | Jui Shah, Dongxu Zhang, Sam Brody, Andrew McCallum |
| 2022 | LREC | A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes. | Dongxu Zhang, Sunil Mohan, Michaela Torkar, Andrew McCallum |
| 2022 | NAACL | Entity Linking via Explicit Mention-Mention Coreference Modeling. | Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum |
| 2022 | NAACL | Inducing and Using Alignments for Transition-based AMR Parsing. | Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramn Fernandez Astudillo |
| 2022 | NAACL | DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions. | Neha Nayak Kennard, Tim O'Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Hamed Zamani, Andrew McCallum |
| 2021 | AAAI | Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications. | Haw-Shiuan Chang, Amol Agrawal, Andrew McCallum |
| 2021 | ACL | Long Document Summarization in a Low Resource Setting using Pretrained Language Models. | Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna, Pradhiksha Ashok Kumar, Rheeya Uppaal, Bradford Windsor, Eliot Brenner, Dominic Dotterrer, Rajarshi Das, Andrew McCallum |
| 2021 | ACL | Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models. | Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum |
| 2021 | ACL | MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network. | Nicholas FitzGerald, Daniel M. Bikel, Jan A. Botha, Daniel Gillick, Tom Kwiatkowski, Andrew McCallum |
| 2021 | ACL | Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference. | Robert L. Logan IV, Andrew McCallum, Sameer Singh, Daniel M. Bikel |
| 2021 | ACL | Modeling Fine-Grained Entity Types with Box Embeddings. | Yasumasa Onoe, Michael Boratko, Andrew McCallum, Greg Durrett |
| 2021 | ACL | Scaling Within Document Coreference to Long Texts. | Raghuveer Thirukovalluru, Nicholas Monath, Kumar Shridhar, Manzil Zaheer, Mrinmaya Sachan, Andrew McCallum |
| 2021 | AISTATS | Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering. | Sebastian Macaluso, Craig S. Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum |
| 2021 | AISTATS | DAG-Structured Clustering by Nearest Neighbors. | Nicholas Monath, Manzil Zaheer, Kumar Avinava Dubey, Amr Ahmed, Andrew McCallum |
| 2021 | EACL | Changing the Mind of Transformers for Topically-Controllable Language Generation. | Haw-Shiuan Chang, Jiaming Yuan, Mohit Iyyer, Andrew McCallum |
| 2021 | EACL | Multi-facet Universal Schema. | Rohan Paul, Haw-Shiuan Chang, Andrew McCallum |
| 2021 | EMNLP | Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP. | Trapit Bansal, Karthick Prasad Gunasekaran, Tong Wang, Tsendsuren Munkhdalai, Andrew McCallum |
| 2021 | EMNLP | Box Embeddings: An open-source library for representation learning using geometric structures. | Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, Andrew McCallum |
| 2021 | EMNLP | Case-based Reasoning for Natural Language Queries over Knowledge Bases. | Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum |
| 2021 | EMNLP | MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text. | Tim O'Gorman, Zach Jensen, Sheshera Mysore, Kevin Huang, Rubayyat Mahbub, Elsa A. Olivetti, Andrew McCallum |
| 2021 | EMNLP | Improved Latent Tree Induction with Distant Supervision via Span Constraints. | Zhiyang Xu, Andrew Drozdov, Jay-Yoon Lee, Tim O'Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum |
| 2021 | KDD | Scalable Hierarchical Agglomerative Clustering. | Nicholas Monath, Kumar Avinava Dubey, Guru Guruganesh, Manzil Zaheer, Amr Ahmed, Andrew McCallum, Gkhan Mergen, Marc Najork, Mert Terzihan, Bryon Tjanaka, Yuan Wang, Yuchen Wu |
| 2021 | NAACL | Clustering-based Inference for Biomedical Entity Linking. | Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum |
| 2021 | NAACL | Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning. | Xuelu Chen, Michael Boratko, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, Andrew McCallum |
| 2021 | UAI | Min/max stability and box distributions. | Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum |
| 2021 | UAI | Exact and approximate hierarchical clustering using A. | Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum |
| 2020 | AAAI | Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision. | Trapit Bansal, Patrick Verga, Neha Choudhary, Andrew McCallum |
| 2020 | AAAI | Energy and Policy Considerations for Modern Deep Learning Research. | Emma Strubell, Ananya Ganesh, Andrew McCallum |
| 2020 | COLING | Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks. | Trapit Bansal, Rishikesh Jha, Andrew McCallum |
| 2020 | EMNLP | Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks. | Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, Andrew McCallum |
| 2020 | EMNLP | ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning. | Michael Boratko, Xiang Li, Tim O'Gorman, Rajarshi Das, Dan Le, Andrew McCallum |
| 2020 | EMNLP | Probabilistic Case-based Reasoning in Knowledge Bases. | Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum |
| 2020 | EMNLP | Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders. | Andrew Drozdov, Subendhu Rongali, Yi-Pei Chen, Tim O'Gorman, Mohit Iyyer, Andrew McCallum |
| 2020 | EMNLP | An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text. | Daivik Swarup, Ahsaas Bajaj, Sheshera Mysore, Tim O'Gorman, Rajarshi Das, Andrew McCallum |
| 2020 | KDD | AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types. | Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han |
| 2019 | ACL | A2N: Attending to Neighbors for Knowledge Graph Inference. | Trapit Bansal, Da-Cheng Juan, Sujith Ravi, Andrew McCallum |
| 2019 | ACL | Energy and Policy Considerations for Deep Learning in NLP. | Emma Strubell, Ananya Ganesh, Andrew McCallum |
| 2019 | ACL | Optimal Transport-based Alignment of Learned Character Representations for String Similarity. | Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum |
| 2019 | CoNLL | Roll Call Vote Prediction with Knowledge Augmented Models. | Pallavi Patil, Kriti Myer, Ronak Zala, Arpit Singh, Sheshera Mysore, Andrew McCallum, Adrian Benton, Amanda Stent |
| 2019 | EMNLP | Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders. | Andrew Drozdov, Patrick Verga, Yi-Pei Chen, Mohit Iyyer, Andrew McCallum |
| 2019 | ICLR | Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering. | Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum |
| 2019 | ICLR | Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension. | Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum |
| 2019 | ICLR | Smoothing the Geometry of Probabilistic Box Embeddings. | Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum |
| 2019 | ICML | Supervised Hierarchical Clustering with Exponential Linkage. | Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum |
| 2019 | KDD | Paper Matching with Local Fairness Constraints. | Ari Kobren, Barna Saha, Andrew McCallum |
| 2019 | KDD | Scalable Hierarchical Clustering with Tree Grafting. | Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum |
| 2019 | KDD | Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. | Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed |
| 2019 | NAACL | Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders. | Andrew Drozdov, Patrick Verga, Mohit Yadav, Mohit Iyyer, Andrew McCallum |
| 2019 | NAACL | OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference. | Dongxu Zhang, Subhabrata Mukherjee, Colin Lockard, Xin Luna Dong, Andrew McCallum |
| 2018 | ACL | A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset. | Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock |
| 2018 | ACL | Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. | Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum |
| 2018 | ACL | Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking. | Shikhar Murty, Patrick Verga, Luke Vilnis, Irena Radovanovic, Andrew McCallum |
| 2018 | CoNLL | Embedded-State Latent Conditional Random Fields for Sequence Labeling. | Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, Andrew McCallum |
| 2018 | EMNLP | An Interface for Annotating Science Questions. | Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock |
| 2018 | EMNLP | Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets. | Nathan Greenberg, Trapit Bansal, Patrick Verga, Andrew McCallum |
| 2018 | EMNLP | Linguistically-Informed Self-Attention for Semantic Role Labeling. | Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum |
| 2018 | ICLR | Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. | Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum |
| 2018 | NAACL | Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection. | Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, Andrew McCallum |
| 2018 | NAACL | Training Structured Prediction Energy Networks with Indirect Supervision. | Amirmohammad Rooshenas, Aishwarya Kamath, Andrew McCallum |
| 2018 | NAACL | Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction. | Patrick Verga, Emma Strubell, Andrew McCallum |
| 2017 | ACL | Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks. | Rajarshi Das, Manzil Zaheer, Siva Reddy, Andrew McCallum |
| 2017 | EACL | Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks. | Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum |
| 2017 | EACL | Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema. | Patrick Verga, Arvind Neelakantan, Andrew McCallum |
| 2017 | EMNLP | Dependency Parsing with Dilated Iterated Graph CNNs. | Emma Strubell, Andrew McCallum |
| 2017 | EMNLP | Fast and Accurate Entity Recognition with Iterated Dilated Convolutions. | Emma Strubell, Patrick Verga, David Belanger, Andrew McCallum |
| 2017 | ICLR | Learning a Natural Language Interface with Neural Programmer. | Arvind Neelakantan, Quoc V. Le, Martn Abadi, Andrew McCallum, Dario Amodei |
| 2017 | ICML | End-to-End Learning for Structured Prediction Energy Networks. | David Belanger, Bishan Yang, Andrew McCallum |
| 2017 | KDD | A Hierarchical Algorithm for Extreme Clustering. | Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum |
| 2016 | ICML | Structured Prediction Energy Networks. | David Belanger, Andrew McCallum |
| 2016 | NAACL | Multilingual Relation Extraction using Compositional Universal Schema. | Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, Andrew McCallum |
| 2016 | RecSys | Untitled record | Trapit Bansal, David Belanger, Andrew McCallum |
| 2015 | ACL | Compositional Vector Space Models for Knowledge Base Completion. | Arvind Neelakantan, Benjamin Roth, Andrew McCallum |
| 2015 | ACL | Learning Dynamic Feature Selection for Fast Sequential Prediction. | Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum |
| 2015 | ICTIR | Embedded Representations of Lexical and Knowledge-Base Semantics. | Andrew McCallum |
| 2015 | UAI | Bethe Projections for Non-Local Inference. | Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum |
| 2014 | ACL | Learning Soft Linear Constraints with Application to Citation Field Extraction. | Sam Anzaroot, Alexandre Passos, David Belanger, Andrew McCallum |
| 2014 | CoNLL | Lexicon Infused Phrase Embeddings for Named Entity Resolution. | Alexandre Passos, Vineet Kumar, Andrew McCallum |
| 2014 | EMNLP | Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space. | Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, Andrew McCallum |
| 2014 | UAI | Message Passing for Soft Constraint Dual Decomposition. | David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum |
| 2013 | ACL | Transition-based Dependency Parsing with Selectional Branching. | Jinho D. Choi, Andrew McCallum |
| 2013 | CIKM | Joint inference of entities, relations, and coreference. | Sameer Singh, Sebastian Riedel, Brian Martin, Jiaping Zheng, Andrew McCallum |
| 2013 | CIKM | Assessing confidence of knowledge base content with an experimental study in entity resolution. | Michael L. Wick, Sameer Singh, Ari Kobren, Andrew McCallum |
| 2013 | CIKM | A joint model for discovering and linking entities. | Michael L. Wick, Sameer Singh, Harshal Pandya, Andrew McCallum |
| 2013 | CIKM | Universal schema for entity type prediction. | Limin Yao, Sebastian Riedel, Andrew McCallum |
| 2013 | CoNLL | Dynamic Knowledge-Base Alignment for Coreference Resolution. | Jiaping Zheng, Luke Vilnis, Sameer Singh, Jinho D. Choi, Andrew McCallum |
| 2013 | NAACL | Relation Extraction with Matrix Factorization and Universal Schemas. | Sebastian Riedel, Limin Yao, Andrew McCallum, Benjamin M. Marlin |
| 2012 | ACL | A Discriminative Hierarchical Model for Fast Coreference at Large Scale. | Michael L. Wick, Sameer Singh, Andrew McCallum |
| 2012 | ACL | Unsupervised Relation Discovery with Sense Disambiguation. | Limin Yao, Sebastian Riedel, Andrew McCallum |
| 2012 | EMNLP | Parse, Price and Cut--Delayed Column and Row Generation for Graph Based Parsers. | Sebastian Riedel, David A. Smith, Andrew McCallum |
| 2012 | EMNLP | Monte Carlo MCMC: Efficient Inference by Approximate Sampling. | Sameer Singh, Michael L. Wick, Andrew McCallum |
| 2012 | NAACL | Monte Carlo MCMC: Efficient Inference by Sampling Factors. | Sameer Singh, Michael L. Wick, Andrew McCallum |
| 2012 | NAACL | Human-Machine Cooperation: Supporting User Corrections to Automatically Constructed KBs. | Michael L. Wick, Karl Schultz, Andrew McCallum |
| 2012 | NAACL | Probabilistic Databases of Universal Schema. | Limin Yao, Sebastian Riedel, Andrew McCallum |
| 2012 | WSDM | Selecting actions for resource-bounded information extraction using reinforcement learning. | Pallika H. Kanani, Andrew McCallum |
| 2011 | ACL | Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models. | Sameer Singh, Amarnag Subramanya, Fernando C. N. Pereira, Andrew McCallum |
| 2011 | CIKM | Toward interactive training and evaluation. | Gregory Druck, Andrew McCallum |
| 2011 | EMNLP | Optimizing Semantic Coherence in Topic Models. | David M. Mimno, Hanna M. Wallach, Edmund M. Talley, Miriam Leenders, Andrew McCallum |
| 2011 | EMNLP | Fast and Robust Joint Models for Biomedical Event Extraction. | Sebastian Riedel, Andrew McCallum |
| 2011 | EMNLP | Structured Relation Discovery using Generative Models. | Limin Yao, Aria Haghighi, Sebastian Riedel, Andrew McCallum |
| 2011 | ICML | SampleRank: Training Factor Graphs with Atomic Gradients. | Michael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum |
| 2010 | AMTA | Machine Translation Using Overlapping Alignments and SampleRank. | Benjamin Roth, Andrew McCallum, Marc Dymetman, Nicola Cancedda |
| 2010 | EMNLP | Collective Cross-Document Relation Extraction Without Labelled Data. | Limin Yao, Sebastian Riedel, Andrew McCallum |
| 2010 | ICML | High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models. | Gregory Druck, Andrew McCallum |
| 2010 | NAACL | Constraint-Driven Rank-Based Learning for Information Extraction. | Sameer Singh, Limin Yao, Sebastian Riedel, Andrew McCallum |
| 2010 | PAKDD | Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand. | Pallika H. Kanani, Andrew McCallum, Shaohan Hu |
| 2010 | UAI | Inference by Minimizing Size, Divergence, or their Sum. | Sebastian Riedel, David A. Smith, Andrew McCallum |
| 2009 | ACL | Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria. | Gregory Druck, Gideon S. Mann, Andrew McCallum |
| 2009 | CoNLL | Joint Inference for Natural Language Processing. | Andrew McCallum |
| 2009 | EMNLP | Generalized Expectation Criteria for Bootstrapping Extractors using Record-Text Alignment. | Kedar Bellare, Andrew McCallum |
| 2009 | EMNLP | Active Learning by Labeling Features. | Gregory Druck, Burr Settles, Andrew McCallum |
| 2009 | EMNLP | Polylingual Topic Models. | David M. Mimno, Hanna M. Wallach, Jason Naradowsky, David A. Smith, Andrew McCallum |
| 2009 | KDD | Efficient methods for topic model inference on streaming document collections. | Limin Yao, David M. Mimno, Andrew McCallum |
| 2009 | UAI | Alternating Projections for Learning with Expectation Constraints. | Kedar Bellare, Gregory Druck, Andrew McCallum |
| 2009 | SDM | An Entity Based Model for Coreference Resolution. | Michael L. Wick, Aron Culotta, Khashayar Rohanimanesh, Andrew McCallum |
| 2008 | ACL | Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields. | Gideon S. Mann, Andrew McCallum |
| 2008 | KDD | Unsupervised deduplication using cross-field dependencies. | Robert J. Hall, Charles Sutton, Andrew McCallum |
| 2008 | KDD | A unified approach for schema matching, coreference and canonicalization. | Michael L. Wick, Khashayar Rohanimanesh, Karl Schultz, Andrew McCallum |
| 2008 | SIGIR | Learning from labeled features using generalized expectation criteria. | Gregory Druck, Gideon S. Mann, Andrew McCallum |
| 2008 | UAI | Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression. | David M. Mimno, Andrew McCallum |
| 2007 | COLT | Resource-Bounded Information Gathering for Correlation Clustering. | Pallika H. Kanani, Andrew McCallum |
| 2007 | ICCV | People-LDA: Anchoring Topics to People using Face Recognition. | Vidit Jain, Erik G. Learned-Miller, Andrew McCallum |
| 2007 | ICDAR | Cryptogram Decoding for OCR Using Numerization Strings. | Gary B. Huang, Erik G. Learned-Miller, Andrew McCallum |
| 2007 | ICDM | Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval. | Xuerui Wang, Andrew McCallum, Xing Wei |
| 2007 | ICML | Simple, robust, scalable semi-supervised learning via expectation regularization. | Gideon S. Mann, Andrew McCallum |
| 2007 | ICML | Mixtures of hierarchical topics with Pachinko allocation. | David M. Mimno, Wei Li, Andrew McCallum |
| 2007 | ICML | Piecewise pseudolikelihood for efficient training of conditional random fields. | Charles Sutton, Andrew McCallum |
| 2007 | IJCAI | Improving Author Coreference by Resource-Bounded Information Gathering from the Web. | Pallika H. Kanani, Andrew McCallum, Chris Pal |
| 2007 | KDD | Canonicalization of database records using adaptive similarity measures. | Aron Culotta, Michael L. Wick, Robert J. Hall, Matthew Marzilli, Andrew McCallum |
| 2007 | KDD | Semi-supervised classification with hybrid generative/discriminative methods. | Gregory Druck, Chris Pal, Andrew McCallum, Xiaojin Zhu |
| 2007 | KDD | Expertise modeling for matching papers with reviewers. | David M. Mimno, Andrew McCallum |
| 2007 | KDD | Generalized component analysis for text with heterogeneous attributes. | Xuerui Wang, Chris Pal, Andrew McCallum |
| 2007 | NAACL | First-Order Probabilistic Models for Coreference Resolution. | Aron Culotta, Michael L. Wick, Andrew McCallum |
| 2007 | NAACL | Efficient Computation of Entropy Gradient for Semi-Supervised Conditional Random Fields. | Gideon S. Mann, Andrew McCallum |
| 2007 | UAI | Nonparametric Bayes Pachinko Allocation. | Wei Li, David M. Blei, Andrew McCallum |
| 2007 | UAI | Improved Dynamic Schedules for Belief Propagation. | Charles Sutton, Andrew McCallum |
| 2006 | AAAI | Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification. | Andrew McCallum, Chris Pal, Gregory Druck, Xuerui Wang |
| 2006 | EMNLP | Learning Field Compatibilities to Extract Database Records from Unstructured Text. | Michael L. Wick, Aron Culotta, Andrew McCallum |
| 2006 | ICASSP | Sparse Forward-Backward Using Minimum Divergence Beams for Fast Training Of Conditional Random Fields. | Chris Pal, Charles Sutton, Andrew McCallum |
| 2006 | ICML | Pachinko allocation: DAG-structured mixture models of topic correlations. | Wei Li, Andrew McCallum |
| 2006 | ICML | Joint Group and Topic Discovery from Relations and Text. | Andrew McCallum, Xuerui Wang, Natasha Mohanty |
| 2006 | ICPR | Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning. | B. Michael Kelm, Chris Pal, Andrew McCallum |
| 2006 | KDD | Information extraction, data mining and joint inference. | Andrew McCallum |
| 2006 | KDD | Topics over time: a non-Markov continuous-time model of topical trends. | Xuerui Wang, Andrew McCallum |
| 2006 | NAACL | Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text. | Aron Culotta, Andrew McCallum, Jonathan Betz |
| 2006 | NAACL | Reducing Weight Undertraining in Structured Discriminative Learning. | Charles Sutton, Michael Sindelar, Andrew McCallum |
| 2005 | AAAI | Reducing Labeling Effort for Structured Prediction Tasks. | Aron Culotta, Andrew McCallum |
| 2005 | AAAI | Semi-Supervised Sequence Modeling with Syntactic Topic Models. | Wei Li, Andrew McCallum |
| 2005 | CIKM | Joint deduplication of multiple record types in relational data. | Aron Culotta, Andrew McCallum |
| 2005 | CIKM | Collective multi-label classification. | Nadia Ghamrawi, Andrew McCallum |
| 2005 | CoNLL | Joint Parsing and Semantic Role Labeling. | Charles Sutton, Andrew McCallum |
| 2005 | ICML | Multi-way distributional clustering via pairwise interactions. | Ron Bekkerman, Ran El-Yaniv, Andrew McCallum |
| 2005 | IJCAI | Topic and Role Discovery in Social Networks. | Andrew McCallum, Andrs Corrada-Emmanuel, Xuerui Wang |
| 2005 | IMC | Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation. | Yu Gu, Andrew McCallum, Donald F. Towsley |
| 2005 | KDD | Group and topic discovery from relations and text. | Xuerui Wang, Natasha Mohanty, Andrew McCallum |
| 2005 | NAACL | Composition of Conditional Random Fields for Transfer Learning. | Charles Sutton, Andrew McCallum |
| 2005 | WWW | Disambiguating Web appearances of people in a social network. | Ron Bekkerman, Andrew McCallum |
| 2005 | UAI | A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance. | Andrew McCallum, Kedar Bellare, Fernando C. N. Pereira |
| 2005 | UAI | Piecewise Training for Undirected Models. | Charles Sutton, Andrew McCallum |
| 2004 | AAAI | Interactive Information Extraction with Constrained Conditional Random Fields. | Trausti T. Kristjansson, Aron Culotta, Paul A. Viola, Andrew McCallum |
| 2004 | COLING | Chinese Segmentation and New Word Detection using Conditional Random Fields. | Fuchun Peng, Fangfang Feng, Andrew McCallum |
| 2004 | ICML | Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. | Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum |
| 2004 | NAACL | Confidence Estimation for Information Extraction. | Aron Culotta, Andrew McCallum |
| 2004 | NAACL | Accurate Information Extraction from Research Papers using Conditional Random Fields. | Fuchun Peng, Andrew McCallum |
| 2004 | UAI | An Integrated, Conditional Model of Information Extraction and Coreference with Appli. | Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay |
| 2003 | CoNLL | Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons. | Andrew McCallum, Wei Li |
| 2003 | IJCAI | Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference. | Andrew McCallum, Ben Wellner |
| 2003 | SIGIR | Table extraction using conditional random fields. | David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft |
| 2003 | UAI | Efficiently Inducing Features of Conditional Random Fields. | Andrew McCallum |
| 2001 | ICML | Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. | John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira |
| 2001 | ICML | Toward Optimal Active Learning through Sampling Estimation of Error Reduction. | Nicholas Roy, Andrew McCallum |
| 2000 | AAAI | Information Extraction with HMM Structures Learned by Stochastic Optimization. | Dayne Freitag, Andrew McCallum |
| 2000 | ICML | Learning to Create Customized Authority Lists. | Huan Chang, David Cohn, Andrew McCallum |
| 2000 | ICML | Maximum Entropy Markov Models for Information Extraction and Segmentation. | Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira |
| 2000 | KDD | Efficient clustering of high-dimensional data sets with application to reference matching. | Andrew McCallum, Kamal Nigam, Lyle H. Ungar |
| 1999 | IJCAI | A Machine Learning Approach to Building Domain-Specific Search Engines. | Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore |
| 1998 | AAAI | Learning to Extract Symbolic Knowledge from the World Wide Web. | Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Sen Slattery |
| 1998 | AAAI | Learning to Classify Text from Labeled and Unlabeled Documents. | Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom M. Mitchell |
| 1998 | ICML | Improving Text Classification by Shrinkage in a Hierarchy of Classes. | Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng |