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        <title>A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations</title>
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    <abstract>We present a semi-supervised machine-learning approach for the classification of adjectives into property- vs. relation-denoting adjectives, a distinction that is highly relevant for ontology learning. The feasibility of this classification task is evaluated in a human annotation experiment. We observe that token-level annotation of these classes is expensive and difficult. Yet, a careful corpus analysis reveals that adjective classes tend to be stable, with few occurrences of class shifts observed at the token level. As a consequence, we opt for a type-based semi-supervised classification approach. The class labels obtained from manual annotation are projected to large amounts of unannotated token samples. Training on heuristically labeled data yields high classification performance on our own data and on a data set compiled from WordNet. Our results suggest that it is feasible to automatically distinguish adjectives denoting properties and relations, using small amounts of annotated data.</abstract>
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%0 Conference Proceedings
%T A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations
%A Hartung, Matthias
%A Frank, Anette
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 may
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F hartung-frank-2010-semi
%X We present a semi-supervised machine-learning approach for the classification of adjectives into property- vs. relation-denoting adjectives, a distinction that is highly relevant for ontology learning. The feasibility of this classification task is evaluated in a human annotation experiment. We observe that token-level annotation of these classes is expensive and difficult. Yet, a careful corpus analysis reveals that adjective classes tend to be stable, with few occurrences of class shifts observed at the token level. As a consequence, we opt for a type-based semi-supervised classification approach. The class labels obtained from manual annotation are projected to large amounts of unannotated token samples. Training on heuristically labeled data yields high classification performance on our own data and on a data set compiled from WordNet. Our results suggest that it is feasible to automatically distinguish adjectives denoting properties and relations, using small amounts of annotated data.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/685_Paper.pdf

Markdown (Informal)

[A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations](http://www.lrec-conf.org/proceedings/lrec2010/pdf/685_Paper.pdf) (Hartung & Frank, LREC 2010)

• A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations (Hartung & Frank, LREC 2010)

ACL

• Matthias Hartung and Anette Frank. 2010. A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).