most citedMeasuring Semantic Similarity by Latent Relational Analysis

168 citations · 205 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG200515 cited

Corpus-based Learning of Analogies and Semantic Relations

Peter D. Turney, Michael L. Littman

We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found…

cs.LG2005168 cited

Measuring Semantic Similarity by Latent Relational Analysis

Peter D. Turney

This paper introduces Latent Relational Analysis (LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. Wh…

cs.NE2005

Self-Replicating Strands that Self-Assemble into User-Specified Meshes

Robert Ewaschuk, Peter Turney

It has been argued that a central objective of nanotechnology is to make products inexpensively, and that self-replication is an effective approach to very low-cost manufacturing.…

cs.LG2005

Combining Independent Modules in Lexical Multiple-Choice Problems

Peter D. Turney, Michael L. Littman, Jeffrey Bigham +1

Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best…

cs.CL20048 cited

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

Peter D. Turney

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction…

cs.CL200414 cited

Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities

Peter D. Turney

This paper describes the National Research Council (NRC) Word Sense Disambiguation (WSD) system, as applied to the English Lexical Sample (ELS) task in Senseval-3. The NRC system a…