363 citations · 730 across the 6 of their papers we have counts for
7 papers
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…
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…
Combining Independent Modules to Solve Multiple-choice Synonym and Analogy 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…
Measuring Praise and Criticism: Inference of Semantic Orientation from Association
Peter D. Turney, Michael L. Littman
The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation…
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…
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus
Peter D. Turney, Michael L. Littman
The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic ori…