Abstract
AbstractMining quality phrases is one of the basic tasks of natural language processing. Current research mainly focuses on universal languages but is rarely conducted for low-resource languages such as Indonesian. To the best of our knowledge, there is no evaluation dataset available for phrase extraction task in Indonesian. Phrase extraction is a challenging task for Indonesian due to the lack of language analyzing tools and large data set. Therefore, we propose a framework to construct Indonesian phrase extraction corpus using Wikipedia as high-quality resource and match extracted phrases with our POS-tagged corpus. Our linguistic experts manually classified the extracted POS patterns. With the annotated patterns, we re-extract phrases and construct a corpus with 8379 Indonesian phrases in total. In addition, we experiment with three deep learning models achieved superior performances for phrase extraction and finalize the baselines for Indonesian phrase extraction task.KeywordsIndonesianPhrase extractionCorpus construction
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