Unsupervised and Knowledge-free Morpheme Segmentation and Analysis
نویسنده
چکیده
This paper presents a revised version of an unsupervised and knowledge-free morpheme boundary detection algorithm based on letter successor variety (LSV) and a trie classifier (Bordag, 2006a). Additionally a morphemic analysis based on contextual similarity provides knowledge about relatedness of the found morphs. For the boundary detection the challenge of increasing recall of found morphs while retaining a high precision is tackled by adding a compound splitter, iterating the LSV analysis and dividing the trie classifier into two distinctly applied clasifiers. The result is a significantly improved overall performance and a decreased reliance on corpus size. Further possible improvements and analyses are discussed.
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