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Abstract

A distinguishing feature of many multiword expressions (MWEs) is their semantic non-compositionality. Determining the semantic compositionality of MWEs is important for many natural language processing tasks. We address the task of modeling semantic compositionality of Croatian MWEs. We adopt a composition-based approach within the distributional semantics framework. We build and evaluate models based on Latent Semantic Analysis and the recently proposed neural network-based Skip-gram model, and experiment with different composition functions. We show that the compositionality scores predicted by the Skip-gram additive models correlate well with human judgments (=0.50). When framed as a classification task, the model achieves an accuracy of 0.64.

Content

Vgl. unter: http://takelab.fer.hr/data/cromwesc/. The dataset is available from here: TakeLab-CroMWEsc.tar.gz. The archive contains one file, which contains a list of 200 Croatian multiword expressions annotated with semantic compositionality scores. Twenty expressions were annotated by 24 annotators (denoted by "*") and the rest of them were annotated by 6 annotators. Besides median, we provide mode, mean, and standard deviation for each expression. Consult the above mentioned paper for details.