We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply ...
Main approaches to corpus-based semantic class mining include distributional similarity (DS) and pattern-based (PB). In this paper, we perform an empirical comparison of them, bas...
Computing the pairwise semantic similarity between all words on the Web is a computationally challenging task. Parallelization and optimizations are necessary. We propose a highly...
Patrick Pantel, Eric Crestan, Arkady Borkovsky, An...
Distributional similarity is a classic technique for entity set expansion, where the system is given a set of seed entities of a particular class, and is asked to expand the set u...
The paper examines different possibilities to take advantage of the taxonomic organization of a thesaurus to improve the accuracy of classifying new words into its classes. The re...
A query speller is crucial to search engine in improving web search relevance. This paper describes novel methods for use of distributional similarity estimated from query logs in...
Accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words. However, the na
Word clustering is a conventional and important NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distributional similarity a...
We present the design and evaluation of a translator’s amenuensis that uses comparable corpora to propose and rank nonliteral solutions to the translation of expressions from th...
Bogdan Babych, Anthony Hartley, Serge Sharoff, Olg...