73 citations · 171 across the 6 of their papers we have counts for
6 papers
Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction
Hal Daumé, Daniel Marcu
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., lin…
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
Hal Daumé, Daniel Marcu
We develop a Bayesian framework for tackling the supervised clustering problem, the generic problem encountered in tasks such as reference matching, coreference resolution, identit…
A Large-Scale Exploration of Effective Global Features for a Joint Entity Detection and Tracking Model
Hal Daumé, Daniel Marcu
Entity detection and tracking (EDT) is the task of identifying textual mentions of real-world entities in documents, extending the named entity detection and coreference resolution…
A Noisy-Channel Model for Document Compression
Hal Daumé, Daniel Marcu
We present a document compression system that uses a hierarchical noisy-channel model of text production. Our compression system first automatically derives the syntactic structure…
Induction of Word and Phrase Alignments for Automatic Document Summarization
Hal Daumé, Daniel Marcu
Current research in automatic single document summarization is dominated by two effective, yet naive approaches: summarization by sentence extraction, and headline generation via b…
Search-based Structured Prediction
Hal Daumé, John Langford, Daniel Marcu
We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computation…