# Andrew Mccallum

Professor

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## Papers307 papers

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

Supervised Hierarchical Clustering with Exponential Linkage

Scalable Hierarchical Clustering with Tree Grafting

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

A2N: Attending to Neighbors for Knowledge Graph Inference

Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders

Roll Call Vote Prediction with Knowledge Augmented Models

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

Training Structured Prediction Energy Networks with Indirect Supervision.

Linguistically-Informed Self-Attention for Semantic Role Labeling.

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

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

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

Embedded-State Latent Conditional Random Fields for Sequence Labeling.

An Interface for Annotating Science Questions.

Training Structured Prediction Energy Networks with Indirect Supervision.

Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders.

SIMULTANEOUSLY SELF-ATTENDING TO ALL MENTIONS FOR FULL-ABSTRACT BIOLOGICAL RELATION EXTRACTION

Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures

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

A Hierarchical Algorithm for Extreme Clustering

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions.

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

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

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

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

Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching.

Structured prediction energy networks

Reports on the 2015 AAAI Spring Symposium Series.

Word Representations via Gaussian Embedding

Latent Relation Representations for Universal Schemas

Assessing confidence of knowledge base content with an experimental study in entity resolution

Relation Extraction with Matrix Factorization and Universal Schemas.

Anytime Belief Propagation Using Sparse Domains.

A joint model for discovering and linking entities

Universal schema for entity type prediction

Joint inference of entities, relations, and coreference

Dynamic Knowledge-Base Alignment for Coreference Resolution.

A discriminative hierarchical model for fast coreference at large scale

An Introduction to Conditional Random Fields.

MAP Inference in Chains using Column Generation.

Resource-bounded information acquisition and learning

**2**|EI|Bibtex

Monte Carlo MCMC: efficient inference by sampling factors

Monte Carlo MCMC: efficient inference by approximate sampling

Parse, price and cut: delayed column and row generation for graph based parsers

Probabilistic databases of universal schema

Unsupervised relation discovery with sense disambiguation

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

**1**|EI|Bibtex

Combining joint models for biomedical event extraction.

Selecting actions for resource-bounded information extraction using reinforcement learning

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

Robust biomedical event extraction with dual decomposition and minimal domain adaptation

Large-scale cross-document coreference using distributed inference and hierarchical models

Generalized expectation criteria for lightly supervised learning

**5**|EI|Bibtex

Model combination for event extraction in BioNLP 2011

Optimizing semantic coherence in topic models

SampleRank: Training Factor Graphs with Atomic Gradients.

Toward interactive training and evaluation

Structured relation discovery using generative models

Fast and robust joint models for biomedical event extraction

SampleRank: Training Factor Graphs with Atomic Gradients

**50**|Bibtex

Inter-Event Dependencies support Event Extraction from Biomedical Literature

**1**|Bibtex

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

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

Inference by Minimizing Size, Divergence, or their Sum

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

Scalable probabilistic databases with factor graphs and MCMC

Scalable Probabilistic Databases with Factor Graphs and MCMC

Modeling relations and their mentions without labeled text

Distantly Labeling Data for Large Scale Cross-Document Coreference

Resource-Bounded information extraction: acquiring missing feature values on demand

Constraint-driven rank-based learning for information extraction

Collective cross-document relation extraction without labelled data

Alternating projections for learning with expectation constraints