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Detecting light verb constructions across languages

Published online by Cambridge University Press:  15 July 2019

István Nagy T. ,
Anita Rácz  and
Veronika Vincze
István Nagy T.
Affiliation:
Black Swan Hungary, Budapest, Hungary
Anita Rácz
Affiliation:
Institute of Informatics, University of Szeged, Szeged, Hungary
Veronika Vincze*
Affiliation:
MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
*
*Corresponding author. Email: [email protected]
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Abstract

Light verb constructions (LVCs) are verb and noun combinations in which the verb has lost its meaning to some degree and the noun is used in one of its original senses, typically denoting an event or an action. They exhibit special linguistic features, especially when regarded in a multilingual context. In this paper, we focus on the automatic detection of LVCs in raw text in four different languages, namely, English, German, Spanish, and Hungarian. First, we analyze the characteristics of LVCs from a linguistic point of view based on parallel corpus data. Then, we provide a standardized (i.e., language-independent) representation of LVCs that can be used in machine learning experiments. After, we experiment on identifying LVCs in different languages: we exploit language adaptation techniques which demonstrate that data from an additional language can be successfully employed in improving the performance of supervised LVC detection for a given language. As there are several annotated corpora from several domains in the case of English and Hungarian, we also investigate the effect of simple domain adaptation techniques to reduce the gap between domains. Furthermore, we combine domain adaptation techniques with language adaptation techniques for these two languages. Our results show that both out-domain and additional language data can improve performance. We believe that our language adaptation method may have practical implications in several fields of natural language processing, especially in machine translation.

Keywords

SemanticsMachine learningLexicographyMultilinguality
Type
Article
Information
Natural Language Engineering , Volume 26 , Issue 3 , May 2020 , pp. 319 - 348
Copyright
© Cambridge University Press 2019

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