activity
20022005
most citedThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

1.6k citations · 3.7k across the 33 of their papers we have counts for

collaborators
Showing cs.LGShow all

25 papers · 1 filter

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.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.LG2003160 cited

Coherent Keyphrase Extraction via Web Mining

Peter D. Turney

Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic k…

cs.LG200313 cited

Learning 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.LG200243 cited

Robust Classification with Context-Sensitive Features

Peter D. Turney

This paper addresses the problem of classifying observations when features are context-sensitive, especially when the testing set involves a context that is different from the trai…