Scott M. Thede

A Second-Order Hidden Markov Model for Part-of-Speech Tagging

This paper describes an extension to the hidden Markov model for part-of-speech tagging using second-order approximations for both contextual and lexical probabilities. This model increases the accuracy of the tagger to state of the art levels. These approximations make use of more contextual information than standard statistical systems. New methods of smoothing the estimated probabilities are also introduced to address the sparse data problem.

Predicting Part-of Speech Information about Unknown Words using Statistical Methods

This paper examines the feasibility of using statistical methods to train a part-of-speech predictor for unknown words. By using statistical methods, without incorporating hand-crafted linguistic information, the predictor could be used with any language for which there is a large tagged training corpus. Encouraging results have been obtained by testing the predictor on unknown words from the Brown corpus. The relative value of information sources such as affixes and context is discussed. This part-of-speech predictor will be used in a part-of-speech tagger to handle out-of-lexicon words.

Familiarity and pronounceability of nouns and names.

Ratings of familiarity and pronounceability were obtained from a random sample of 199 surnames (selected from over 80,000 entries in the Purdue University phone book) and 199 nouns (from the Kučera-Francis, 1967, word database). The distributions of ratings for nouns versus names are substantially different: Nouns were rated as more familiar and easier to pronounce than surnames. Frequency and familiarity were more closely related in the proper name pool than the word pool, although both correlations were modest. Ratings of familiarity and pronounceability were highly related for both groups. A production experiment showed that rated pronounceability was highly related to the time taken to produce a name. These data confirm the common belief that there are differences in the statistical and distributional properties of words as compared to proper names. The value of using frequency and the ratings of familiarity and pronounceability for predicting variations in actual pronunciations of words and names are discussed.

Familiarity and pronounceability of nouns and names

Proper names have several properties that create problems for speech recognition systems: the number of names is large and ever changing, names can be borrowed directly from other languages and may not conform to usual pronunciation rules, and the variety of pronunciations for names can be high. Because the set of proper names is so dynamic and machines are notoriously poor at phoneme recognition, a promising approach to designing a name recognition system is to incorporate statistical aspects of proper names (e.g., frequency, familiarity). Unfortunately, there exists relatively little data on the distribution of names. Ratings of familiarity and pronounceability were obtained for a randomly chosen sample of 199 surnames (from 80 987 entries in the Purdue phonebook) and 199 nouns (from Kucera–Francis). The ratings for nouns versus names are substantially different: nouns were rated as more familiar and easier to pronounce than surnames. Frequency and familiarity were more closely related in the proper nam...

The computer science small department initiative (CS_SDI) report

This special session will report on the work of the Computer Science Small Department Initiative (CS_SDI) in three areas. The first is the identification of strengths and challenges that are typically found in small departments (5 or fewer FTE CS faculty). The second area is relating the CC2001 guidelines directly to small departments. The third area is a set of departmental guidelines for areas such as faculty load and maintaining a departmental lab. The website for this work can be found at http://csis.olivet.edu/cs_sdi.