Morfessor 2.0: Python Implementation and Extensions for Morfessor Baseline

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Journal ISSN
Volume Title
School of Electrical Engineering | D4 Julkaistu kehittämis- tai tutkimusraportti tai -selvitys
Date
2013
Major/Subject
Mcode
Degree programme
Language
en
Pages
38
Series
Aalto University publication series SCIENCE + TECHNOLOGY, 25/2013
Abstract
Morfessor is a family of probabilistic machine learning methods that find morphological segmentations for words of a natural language, based solely on raw text data. After the release of the public implementations of the Morfessor Baseline and Categories-MAP methods in 2005, they have become popular as automatic tools for processing morphologically complex languages for applications such as speech recognition and machine translation. This report describes a new implementation of the Morfessor Baseline method. The new version not only fixes the main restrictions of the previous software, but also includes recent methodological extensions such as semi-supervised learning, which can make use of small amounts of manually segmented words. Experimental results for the various features of the implementation are reported for English and Finnish segmentation tasks.
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Keywords
morpheme segmentation, morphology induction, unsupervised learning, semi-supervised learning, morfessor, machine learning
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