Learning a second language is, for the most part, a day-in day-out struggle against the mountain of new vocabulary a learner must acquire. Furthermore, since the number of new words to learn is so great, learners must acquire them autonomously. Evidence suggests that for languages with writing systems, native-like vocabulary sizes are only developed through reading widely, and that reading is only fruitful once learners have acquired the core vocabulary required for it to become smooth. Learners of Japanese have an especially high barrier in the form of the Japanese writing system, in particular its use of kanji characters. Recent work on dictionary accessibility has focused on compensating for learner errors in pronouncing unknown words, however much difficulty remains. / This thesis uses the rich visual nature of the Japanese orthography to support the study of vocabulary in several ways. Firstly, it proposes a range of kanji similarity measures and evaluates them over several new data sets, finding that the stroke edit distance and tree edit distance metrics best approximate human judgements. Secondly, it uses stroke edit distance construct a model of kanji misrecognition, which we use as the basis for a new form of kanji search by similarity. Analysing query logs, we find that this new form of search was rapidly adopted by users, indicating its utility. We finally combine kanji confusion and pronunciation models into a new adaptive testing platform, Kanji Tester, modelled after aspects of the Japanese Language Proficiency Test. As the user tests themselves, the system adapts to their error patterns and uses this information to make future tests more difficult. Investigating logs of use, we find a weak positive correlation between ability estimates and time the system has been used. Furthermore, our adaptive models generated questions which were significantly more difficult than their control counterparts. / Overall, these contributions make a concerted effort to improve tools for learner self-study, so that learners can successfully overcome the reading hurdle and propel themselves towards greater proficiency. The data collected from these tools also forms a useful basis for further study of learner error and vocabulary development.

This thesis deals with the use of Conditional Random Fields (CRFs; Lafferty et al. (2001)) for Natural Language Processing (NLP). CRFs are probabilistic models for sequence labelling which are particularly well suited to NLP. They have many compelling advantages over other popular models such as Hidden Markov Models and Maximum Entropy Markov Models (Rabiner, 1990; McCallum et al., 2001), and have been applied to a number of NLP tasks with considerable success (e.g., Sha and Pereira (2003) and Smith et al. (2005)). Despite their apparent success, CRFs suffer from two main failings. Firstly, they often over-fit the training sample. This is a consequence of their considerable expressive power, and can be limited by a prior over the model parameters (Sha and Pereira, 2003; Peng and McCallum, 2004). Their second failing is that the standard methods for CRF training are often very slow, sometimes requiring weeks of processing time. This efficiency problem is largely ignored in current literature, although in practise the cost of training prevents the application of CRFs to many new more complex tasks, and also prevents the use of densely connected graphs, which would allow for much richer feature sets. (For complete abstract open document)

Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references (leaves [69]-72).

Thesis (M.S.)--Rochester Institute of Technology, 1991. / Typescript. Includes bibliographical references (leaves 115-116).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 110-123). Also available in electronic version.

Thesis (M.C.S.) - Carleton University, 2005. / Includes bibliographical references (p. 91-97). Also available in electronic format on the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 218-220). Also available via the Internet.

Thesis (M.S.)--Rochester Institute of Technology, 1983. / Typescript. Includes bibliographical references (leaves 115-119).

Thesis (M.S.)--University of North Carolina Wilmington, 2008. / Title from PDF title page (viewed September 22, 2008) Includes bibliographical references (p. 72-74)

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains vii, 36 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 33-36).