We present a simple linguistically-motivated method for characterizing the semantic relations that hold between two nouns. The approach leverages the vast size of the Web in order to build lexically-specific features. The main idea is to look for verbs, prepositions, and coordinating conjunctions that can help make explicit the hidden relations between the target nouns. Using these features in instance-based classifiers, we demonstrate state-of-the-art results on various relational similarity problems, including mapping noun-modifier pairs to abstract relations like TIME, LOCATION and CONTAINER, characterizing noun-noun compounds in terms of abstract linguistic predicates like CAUSE, USE, and FROM, classifying the relations between nominals in context, and solving SAT verbal analogy problems. In essence, the approach puts together some existing ideas, showing that they apply generally to various semantic tasks, finding that verbs are especially useful features.
REF solved relational similarity problems using the Web as a corpus.
Solving Relational Similarity Problems Using the Web as a Corpus
Annual Meeting Of The Association For Computational Linguistics
Computer Science