LREC 2006 - Proceedings sorted by papers

Title	Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences
Authors	K. Inui, T. Hirano, R. Iida, A. Fujita, Y. Matsumoto
Abstract	One of the crucial issues in semantic parsing is how to reduce costs of collecting a sufficiently large amount of labeled data. This paper presents a new approach to cost-saving annotation of example sentences with predicate-argument structure information, taking Japanese as a target language. In this scheme, a large collection of unlabeled examples are first clustered and selectively sampled, and for each sampled cluster, only one representative example is given a label by a human annotator. The advantages of this approach are empirically supported by the results of our preliminary experiments, where we use an existing similarity function and naive sampling strategy.
Keywords
Full paper	Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences

Title

Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences

Authors

K. Inui, T. Hirano, R. Iida, A. Fujita, Y. Matsumoto

Abstract

One of the crucial issues in semantic parsing is how to reduce costs of collecting a sufficiently large amount of labeled data. This paper presents a new approach to cost-saving annotation of example sentences with predicate-argument structure information, taking Japanese as a target language. In this scheme, a large collection of unlabeled examples are first clustered and selectively sampled, and for each sampled cluster, only one representative example is given a label by a human annotator. The advantages of this approach are empirically supported by the results of our preliminary experiments, where we use an existing similarity function and naive sampling strategy.

Keywords

Full paper

Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences