In this paper we present a word decompounding method that is based on distributional semantics. Our method does not require any linguistic knowledge and is initialized using a large monolingual corpus. The core idea of our approach is that parts of compounds (like "candle" and "stick") are seman-tically similar to the entire compound, which helps to exclude spurious splits (like "candles" and "tick"). We report results for German and Dutch: For German, our unsupervised method comes on par with the performance of a rule-based and a supervised method and significantly outperforms two unsupervised baselines. For Dutch, our method performs only slightly below a rule-based optimized compound splitter.
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