Unsupervised Learning of Morphology
نویسندگان
چکیده
some morphological pattern that recurs among the groups. Such emergent patterns provide enough clues for segmentation and can sometimes be formulated as rules or morphological paradigms. (c) Features and Classes: In this family of methods, a word is seen as made up of a set of features—n-grams in Mayfield and McNamee (2003) and McNamee and Mayfield (2007), and initial/terminal/mid-substring in De Pauw and Wagacha (2007). Features which occur on many words have little selective power across the words, whereas features which occur seldom pinpoint a specific word or stem. To formalize this intuition, Mayfield and McNamee and McNamee and Mayfield use TF-IDF, and De Pauw and Wagacha use entropy. Classifying an unseen word reduces to using its features to select which word(s) it may be morphologically related to. This decides whether the unseen word is a morphological variant of some other word, and allows extracting the “variation” by which they are related, such as an affix. (d) Phonological Categories and Separation: In this family of methods, the phonemes (approximated by graphemes) are first classed into categories, foremostly, vowel versus consonant. Thereafter, each word is separated into its vowel skeleton and its consonant skeleton, after which various
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ورودعنوان ژورنال:
- Computational Linguistics
دوره 37 شماره
صفحات -
تاریخ انتشار 2011