Morphological Learning as Principled Argument
published: Feb. 25, 2007, recorded: April 2006, views: 141
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We develop a morphological learner that evaluates evidence supporting specific claims that a string of letters is a distributional meaningful unit. The distributional evidence is evaluated by selectional properties of morphs, while evidence towards meaning is modelled by looking at the relationship between stems and words. To assess a proposed affix, it gets a probability measure of meaning by comparing all the possible stems the affix occur with to the particular subset that also occur as words. Since for a stem to be a word counts as evidence towards its meaning, the ratio formed by taking stems that are words to the whole set of possible stems for an affix gives a predictive probability measure for the affix that measures the chance that it has combined with a meaningful stem. This measure, taken in conjunction with the selectional statistics of stems and affixes, provides a basis for deciding on the best morphological structure for a given word. The results for English show a combined precision and recall of 45.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !