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Matthew Titsworth

dblp:57/10432 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Information extraction and text analysis · 50% Probabilistic and Bayesian machine learning · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › clustering
bayesian hierarchical clustering		 0.112009
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution · NIPS 2009
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian nonparametric model		 0.112009
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution · NIPS 2009
Natural language and speech › Information extraction and text analysis
coreference resolution		 0.112009
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution · NIPS 2009
Natural language and speech › Information extraction and text analysis › coreference resolution
event coreference resolution		 0.112009
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution · NIPS 2009

Methods — techniques the papers use, named apart from their topics

nonparametric bayesian modeling · 0.1dirichlet process · 0.1
YearPublicationVenuePosition
2009 Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution
abstract
We present a sequence of unsupervised, nonparametric Bayesian models for clustering complex linguistic objects. In this approach, we consider a potentially infinite number of features and categorical outcomes. We evaluate these models for the task of within- and cross-document event coreference on two corpora. All the models we investigated show significant improvements when compared against an existing baseline for this task.
Cosmin Adrian Bejan, Matthew Titsworth, Andrew Hickl, Sanda M. Harabagiu
 NIPS2