Abstract
In this paper, we present the first gold-standard corpus of Russian noun compounds annotated with compositionality information. We used Universal Dependency treebanks to collect noun compounds according to part of speech patterns, such as ADJ-NOUN or NOUN-NOUN and annotated them according to the following schema: a phrase can be either compositional, non-compositional, or ambiguous (i.e., depending on the context it can be interpreted both as compositional or non-compositional). Next, we conduct a series of experiments to evaluate both unsupervised and supervised methods for predicting compositionality. To expand this manually annotated dataset with more non-compositional compounds and streamline the annotation process we use active learning. We show that not only the methods, previously proposed for English, are easily adapted for Russian, but also can be exploited in active learning paradigm, that increases the efficiency of the annotation process.
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Notes
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The dataset and the code: https://github.com/slangtech/ru-comps.
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We used a Wikipedia dump from 02.05.2019, which consists of 1,542,621 articles.
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Acknowledgements
Dmitry Puzyrev and Ekaterina Artemova were supported by the framework of the HSE University Basic Research Program and Russian Academic Excellence Project “5–100”.
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Puzyrev, D., Shelmanov, A., Panchenko, A., Artemova, E. (2019). Noun Compositionality Detection Using Distributional Semantics for the Russian Language. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_20
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