We present an algorithm that takes an unannotated corpus as its input, and returns a ranked list of probable morphologically related pairs as its output. The algorithm tries to discover morphologically related pairs by looking for pairs that are both orthographically and semantically similar, where orthographic similarity is measured in terms of minimum edit distance, and semantic similarity is measured in terms of mutual information. The procedure does not rely on a morpheme concatenation model, nor on distributional properties of word substrings (such as affix frequency). Experiments with German and English input give encouraging results, both in terms of precision (proportion of good pairs found at various cutoff points of the ranked list), and in terms of a qualitative analysis of the types of morphological patterns discovered by the algorithm.

No abstract

Communication and information exchange is a vital factor in human society. Communication disorders severely influence the quality of life. Whereas experienced typists will produce some 300 keystrokes per minute, persons with motor impairments achieve only much lower rates. Predictive typing systems for English speaking areas have proven useful and efficient, but for all other European languages there exist no predictive typing programs powerful enough to substantially improve the communication rate and the IT access for disabled persons. FASTY aims at offering a communication support system significantly increasing typing speed, adaptable to users with different language and strongly varying needs. In this way the large group of non-English-speaking disabled citizens will be supported in living a more independent and self determined life.

No abstract