I will first give an idiosyncratic overview of NLP in the UK, based on my visits to York, Oxford, Sheffield, Aberdeen, the Open U, Cambridge, Ulster, and Dublin City Universities during my sabbatical.
I will also briefly describe my joint research with Johanna Moore's group at the University of Edinburgh, which investigates whether previous findings and methods in the area of tutorial dialogue can be generalized across dialogue corpora that differ in domain (physics versus electricity), modality (spoken versus typed), and tutor type (human versus computer).
TBA
In my thesis study, I investigate how to evaluate and how to build user simulations to help dialog system development.When evaluating user simulations, I use both human judges and automatic evaluation measures to assess the simulation model qualities. When building user simulations, I examine three factors that impact simulation models in the tasks of dialog strategy learning and dialog system development. (This is a practice talk for Hua's postdoc interview.)
The second study is based on the author's chapter on intertextuality in "Rhetoric Online." The study defines intertextuality as a form of interreference among texts in which an already familiar text is invoked or played upon in a new textual context. Intertextuality makes use of resources in the larger intertext (the textual context in which the discourse appears) to involve users in active co-construction of the textıs meaning. A number of contrasting examples of this phenomenon will be introduced and discussed, so that the kinds of variation in online intertextuality can be identified.
Opinion analysis focuses on extracting subjective expressions and sentiments from text. Large-scale availability of public opinion over the internet weblogs, review websites, discussion forums and face to face conversations has motivated a great amount of research in this area in recent times.
Much of the research in opinion analysis focuses on finding expressions that are opinion-indicators (for example, expressions like ³great², ³boring², or ³failed to kill the spirit²). We feel that such approaches can be augmented by discourse-level analysis of opinions. Discourse-level analysis allows opinion expressions in different parts of the discourse to be interpreted in an interdependent, coherent fashion and allows us to capture the varied ways in which people reveal their opinions.
In order to achieve discourse-level opinion analysis, we first develop a scheme to relate different opinion expressions in a discourse. We validate this design by showing that human annotators can reliably recognize the opinion relations. Then, we implement our scheme with a machine learning framework of collective inference to show that a computerıs ability to recognize opinion expressions is indeed enhanced when the discourse level information is used. Finally, we show that, by performing feature engineering using a simple machine learning framework, the discourse-level opinion relations of our scheme can be automatically detected.