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        <title>Automatic Classification of Attributes in German Adjective-Noun Phrases</title>
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        <namePart type="given">Yana</namePart>
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        <name type="personal">
            <namePart type="given">Sina</namePart>
            <namePart type="family">Zarrieß</namePart>
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            <namePart type="given">Johan</namePart>
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            <namePart type="given">Lasha</namePart>
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    <abstract>Adjectives such as heavy (as in heavy rain) and windy (as in windy day) provide possible values for the attributes intensity and climate, respectively. The attributes themselves are not overtly realized and are in this sense implicit. While these attributes can be easily inferred by humans, their automatic classification poses a challenging task for computational models. We present the following contributions: (1) We gain new insights into the attribute selection task for German. More specifically, we develop computational models for this task that are able to generalize to unseen data. Moreover, we show that classification accuracy depends, inter alia, on the degree of polysemy of the lexemes involved, on the generalization potential of the training data and on the degree of semantic transparency of the adjective-noun pairs in question. (2) We provide the first resource for computational and linguistic experiments with German adjective-noun pairs that can be used for attribute selection and related tasks. In order to safeguard against unwelcome memorization effects, we present an automatic data augmentation method based on a lexical resource that can increase the size of the training data to a large extent.</abstract>
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%0 Conference Proceedings
%T Automatic Classification of Attributes in German Adjective-Noun Phrases
%A Falk, Neele
%A Strakatova, Yana
%A Huber, Eva
%A Hinrichs, Erhard
%Y Zarrieß, Sina
%Y Bos, Johan
%Y van Noord, Rik
%Y Abzianidze, Lasha
%S Proceedings of the 14th International Conference on Computational Semantics (IWCS)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Groningen, The Netherlands (online)
%F falk-etal-2021-automatic
%X Adjectives such as heavy (as in heavy rain) and windy (as in windy day) provide possible values for the attributes intensity and climate, respectively. The attributes themselves are not overtly realized and are in this sense implicit. While these attributes can be easily inferred by humans, their automatic classification poses a challenging task for computational models. We present the following contributions: (1) We gain new insights into the attribute selection task for German. More specifically, we develop computational models for this task that are able to generalize to unseen data. Moreover, we show that classification accuracy depends, inter alia, on the degree of polysemy of the lexemes involved, on the generalization potential of the training data and on the degree of semantic transparency of the adjective-noun pairs in question. (2) We provide the first resource for computational and linguistic experiments with German adjective-noun pairs that can be used for attribute selection and related tasks. In order to safeguard against unwelcome memorization effects, we present an automatic data augmentation method based on a lexical resource that can increase the size of the training data to a large extent.
%U https://aclanthology.org/2021.iwcs-1.23
%P 239-249

Markdown (Informal)

[Automatic Classification of Attributes in German Adjective-Noun Phrases](https://aclanthology.org/2021.iwcs-1.23) (Falk et al., IWCS 2021)

• Automatic Classification of Attributes in German Adjective-Noun Phrases (Falk et al., IWCS 2021)

ACL

• Neele Falk, Yana Strakatova, Eva Huber, and Erhard Hinrichs. 2021. Automatic Classification of Attributes in German Adjective-Noun Phrases. In Proceedings of the 14th International Conference on Computational Semantics (IWCS), pages 239–249, Groningen, The Netherlands (online). Association for Computational Linguistics.