- MAP inference in Large Factor Graphs with Reinforcement Learning Khashayar Rohanimanesh, Michael Wick, Sameer Singh, and Andrew
- SemiSupervised Learning Using Prior Probabilities and EM Rebecca Bruce
- Selective Sampling + Semisupervised Learning = Robust MultiView Learning Ion Muslea, Steven Minton, Craig A. Knoblock
- Group and Topic Discovery from Relations and Text Xuerui Wang, Natasha Mohanty, Andrew McCallum
- Learning Extractors from Unlabeled Text using Relevant Databases Kedar Bellare and Andrew McCallum
- Collective Multi-Label Classification Nadia Ghamrawi
- Detection of errors in training data by using a decision list and Adaboost Hiroyuki Shinnou
- Canonicalization of Database Records using Adaptive Similarity Measures
- Scalable Probabilistic Databases with Factor Graphs and MCMC
- Automating the Construction of Internet Portals with Machine Learning
- Bootstrapping for Text Learning Tasks Rosie Jones 1
- Learning Hidden Markov Model Structure for Information Extraction Kristie Seymore y
- Learning to Classify Text from Labeled and Unlabeled Documents Kamal Nigam y
- Ecient Web Spidering with Reinforcement Learning Jason Rennie y
- Employing EM and PoolBased Active Learning for Text Classification
- Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields
- Multivariate Information Bottleneck Nir Friedman Ori Mosenzon Noam Slonim Naftali Tishby
- Corrective Feedback and Persistent Learning for Information Extraction
- FACTORIE: Efficient Probabilistic Programming for Relational Factor Graphs via Imperative Declarations of
- Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text
- Extracting social networks and contact information from email and the Web
- FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs
- Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference
- A Unified Approach for Schema Matching, Coreference and Canonicalization
- Community-based Link Prediction with Text David Mimno, Hanna Wallach, Andrew McCallum
- Joint Group and Topic Discovery from Relations and Text
- Expertise Modeling for Matching Papers with Reviewers David Mimno, Andrew McCallum
- Simple, Robust, Scalable Semi-supervised Learning via Expectation Regularization
- Transfer Learning for Enhancing Information Flow in Organizations and Social Networks
- Journal of Artificial Intelligence Research 29 (2007) ???? Submitted 12/06; published 09/07 Topic and Role Discovery in Social Networks
- Efficient Computation of Entropy Gradient for Semi-Supervised Conditional Random Fields
- Sparse Message Passing Algorithms for Weighted Maximum Satisfiability Aron Culotta CULOTTA@CS.UMASS.EDU
- Learning Field Compatibilities to Extract Database Records from Unstructured Text
- Practical Markov Logic Containing First-Order Quantifiers with Application to Identity Uncertainty
- A Continuous-Time Model of Topic Co-occurrence Trends Wei Li, Xuerui Wang and Andrew McCallum
- Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends
- Exploring the Use of Conditional Random Field Models and HMMs for Historical Handwritten Document Recognition
- 1 Semi-Supervised Text Classification Using Kamal Nigam
- Direct Maximization of Rank-Based Metrics for Information Retrieval
- Practical Markov Logic Containing First-Order Quantifiers
- Semi-Supervised Sequence Modeling with Syntactic Topic Models Wei Li and Andrew McCallum
- Multi-Way Distributional Clustering via Pairwise Interactions Ron Bekkerman ronb@cs.umass.edu
- Disambiguating Web Appearances of People in a Social Network
- Conditional Models of Identity Uncertainty with Application to Noun Coreference
- An Integrated, Conditional Model of Information Extraction and Coreference
- An Exploration of Entity Models, Collective Classification and Relation Description
- Semi-Supervised Learning using Markov Random Fields Wei Li and Andrew McCallum
- Classification with Hybrid Generative/Discriminative Models
- Rapid Development of Hindi Named Entity Recognition using Conditional Random Fields and
- A Note on the Unification of Information Extraction and Data Mining using Conditional-Probability, Relational Models
- Early Results for Named Entity Recognition with Conditional Random Fields,
- A Note on Topical N-grams Xuerui Wang and Andrew McCallum
- Reducing labeling effort for structured prediction tasks Aron Culotta and Andrew McCallum
- Learning with Scope, with Application to Information Extraction and Classi cation
- Lightly-Supervised Attribute Extraction Kedar Bellare1
- Interactive Information Extraction with Constrained Conditional Random Fields
- Improving Text Classification by Shrinkage in a Hierarchy of Classes Andrew McCallum \Lambday
- Learning to Create Customized Authority Lists Huan Chang huan@cmu.edu
- Multi-Label Text Classi cation with a Mixture Model Trained by EM
- Penn/UMass/CHOP Biocreative II systems 1 Penn/UMass/CHOP Biocreative II systems
- Probabilistic Representations for Integrating Unreliable Data Sources David Mimno, Andrew McCallum and Gerome Miklau
- A Comparison of Event Models for Naive Bayes Text Classification Andrew McCallum zy
- Object Consolodation by Graph Partitioning with a Conditionally-Trained Distance Metric
- Information Extraction with HMMs and Shrinkage Dayne Freitag
- Confidence Estimation for Information Extraction Aron Culotta
- Topic and Role Discovery in Social Networks Andrew McCallum, Andres Corrada-Emmanuel, Xuerui Wang
- SampleRank: Learning Preferences from Atomic Gradients
- Constrained Kronecker Deltas for Fast Approximate Inference and Estimation
- Improving Author Coreference by Resource-bounded Information Gathering from the Web
- Mixtures of Hierarchical Topics with Pachinko Allocation David Mimno mimno@cs.umass.edu
- On Discriminative and Semi-Supervised Dimensionality Reduction
- Accurate Information Extraction from Research Papers using Conditional Random Fields
- Journal of Machine Learning Research ?? (2008) ??-?? Submitted 04/08; Published ??/?? Pachinko Allocation
- Gene Prediction with Conditional Random Fields Aron Culotta, David Kulp and Andrew McCallum
- A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
- Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations
- Topical N-grams: Phrase and Topic Discovery, with an Application to Information Retrieval
- Generalized Component Analysis for Text with Heterogeneous Attributes
- Reducing Annotation Effort using Generalized Expectation Criteria Gregory Druck, Gideon Mann, Andrew McCallum
- A Discriminative Approach to Ontology Mapping Michael Wick, Khashayar
- Bibliometric Impact Measures Leveraging Topic Analysis Gideon S. Mann, David Mimno, Andrew McCallum
- Efficient Clustering of HighDimensional Data Sets
- Building DomainSpecific Search Engines with Machine Learning Techniques
- Distributional Clustering of Words for Text Classification L. Douglas Baker yz
- Generalized Expectation Criteria for Bootstrapping Extractors using Record-Text Alignment
- Information Extraction with HMM Structures Learned by Stochastic Optimization
- SampleRank: Training Factor Graphs with Atomic Gradients
- Bi-directional Joint Inference for Entity Resolution and Segmentation
- Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
- CIIR Technical Report IR-402, University of Masschusetts, 2005. Feature Bagging: Preventing Weight Undertraining
- A Hierarchical Probabilistic Model for Novelty Detection in Text
- A Machine Learning Approach to Building Domain-Speci c Search Engines
- Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference
- Automatic Categorization of Email into Folders: Benchmark Experiments on Enron and SRI Corpora
- Learning Clusterwise Similarity with First-Order Features
- Dynamic Conditional Random Fields for Jointly Labeling Multiple Sequences
- Efficiently Inducing Features of Conditional Random Fields Andrew McCallum
- The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks
- First-Order Probabilistic Models for Coreference Resolution Aron Culotta and Michael Wick and Robert Hall and Andrew McCallum
- Machine Learning, , 1--34 () fl Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- CC Prediction with Graphical Models Chris Pal and Andrew McCallum
- Mining a Digital Library for Influential Authors David Mimno, Andrew McCallum
- Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models
- Group and Topic Discovery from Relations and Their Attributes
- Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning
- Semi-supervised Clustering with User Feedback Just Research
- Nonparametric Bayes Pachinko Allocation Department of Computer Science
- Joint Deduplication of Multiple Record Types in Relational Data
- Organizing the OCA: Learning Faceted Subjects from a Library of Digital Books
- Feature Set Reduction for Document Classification Problems Karel Fuka, Rudolf Hanka
- SIGN DETECTION IN NATURAL IMAGES WITH CONDITIONAL RANDOM FIELDS
- Maximum Entropy Markov Models for Information Extraction and Segmentation
- Toward Optimal Active Learning through Sampling Estimation of Error Reduction
- A Conditional Model of Deduplication for Multi-Type Relational Data
- Query-Aware MCMC Michael Wick
- Structured Relation Discovery using Generative Models Aria Haghighi+
- Selecting Actions for Resource-bounded Information Extraction using Reinforcement Learning
- Toward Interactive Training and Evaluation Gregory Druck
