LREC 2006 - Proceedings sorted by papers

Title	On Automatic Assignment of Verb Valency Frames in Czech
Authors	J. Semecký
Abstract	Many recent NLP applications, including machine translation and information retrieval, could benefit from semantic analysis of language data on the sentence level. This paper presents a method for automatic disambiguation of verb valency frames on Czech data. For each verb occurrence, we extracted features describing its local context. We experimented with diverse types of features, including morphological, syntax-based, idiomatic, animacy and WordNet-based features. The main contribution of the paper lies in determining which ones are most useful for the disambiguation task. The considered features were classified using decision trees, rule-based learning and a Naïve Bayes classifier. We evaluated the methods using 10-fold cross-validation on VALEVAL, a manually annotated corpus of frame annotations containing 7,778 sentences. Syntax-based features have shown to be the most effective. When we used the full set of features, we achieved an accuracy of 80.55% against the baseline 67.87% obtained by assigning the most frequent frame.
Keywords	verb, frame, disambiguation, machine learning
Full paper	On Automatic Assignment of Verb Valency Frames in Czech

Title

On Automatic Assignment of Verb Valency Frames in Czech

Authors

J. Semecký

Abstract

Many recent NLP applications, including machine translation and information retrieval, could benefit from semantic analysis of language data on the sentence level. This paper presents a method for automatic disambiguation of verb valency frames on Czech data. For each verb occurrence, we extracted features describing its local context. We experimented with diverse types of features, including morphological, syntax-based, idiomatic, animacy and WordNet-based features. The main contribution of the paper lies in determining which ones are most useful for the disambiguation task. The considered features were classified using decision trees, rule-based learning and a Naïve Bayes classifier. We evaluated the methods using 10-fold cross-validation on VALEVAL, a manually annotated corpus of frame annotations containing 7,778 sentences. Syntax-based features have shown to be the most effective. When we used the full set of features, we achieved an accuracy of 80.55% against the baseline 67.87% obtained by assigning the most frequent frame.

Keywords

verb, frame, disambiguation, machine learning

Full paper

On Automatic Assignment of Verb Valency Frames in Czech