Daqing He
In this talk I will talk about our participation (University of Maryland and John's Hopkins University team) to TREC High Accuracy Retrieval of Document (HARD) track in both 2003 and 2004. I will first intruduce the HARD experiment setting, then talk about interactive relevance feedback in HARD framework. I will also talk about building passage retrieval module for identifying sub-document unit that are highly relevant to user's queries. Our passage retrieval module was among the best in the track last year, but it is still far from matching to human performance. Finally, I will talk about some other interesting areas that HARD is trying to explore beyond plain batch news article document retrievals.
Mihai Rotaru
In this talk I will present our ongoing work in developing features and models for detecting student emotional states, given only information available during a spoken tutoring dialogue. Prior research has primarily focused on the use of turn-level prosodic features as predictors. We extend the turn-level prosodic feature set used in our previous studies, and additionally apply these same set of features at the word level. Even under a simplifying word-level emotion model, our preliminary results show an improvement in prediction using word level features compared to using turn level features.
We now have a conference page which has basic information about important dates of the conference. The page is at: http://nlp.cs.pitt.edu/dates.htm
I have also marked the blog for two of the conference deadlines (ACL & AAAI). Please feel free to add information to the conference page and also the blog as new information about conferences arrives.
Meeting time for Fall term has changed to be Mondays at 12:30 -- 2pm.
Next meeting will be on Sept. 13th.
In this meeting, I will talk about my Master's Thesis that I have
recently finished from the University of Minnesota. This talk will
essentially be same as my thesis defense. Thesis report and defense slides
are available online at - http://www.cs.pitt.edu/~amruta/pubs.html
Title: "Unsupervised Word Sense Discrimination by Clustering Similar
Contexts."
Abstract: Word sense discrimination is the problem of identifying
different contexts that refer to the same meaning of an ambiguous
word. For example, given multiple contexts that include the word
'sharp', we would hope to discriminate between those that refer to an
intellectual sharpness versus those that refer to a cutting sharpness.
Our methodology is based on the strong contextual hypothesis of Miller
and Charles (1991), which states that "two words are semantically
related to the extent that their contextual representations are
similar."
This thesis presents corpus--based unsupervised solutions that
automatically group together contextually similar instances of a word
as observed in a raw text. We do not utilize any manually created or
maintained knowledge--rich resources such as dictionaries, thesauri
or annotated corpora. As a result, our approach is well suited to the
fluid and dynamic nature of word meanings. It is also portable to
different domains and languages, and scales easily to larger samples
of text.
The overall objective of this thesis is to study the effect of various
feature types, context representations and clustering methods on the
accuracy of sense discrimination. We also apply dimensionality
reduction techniques to capture conceptual similarities among the
contexts and don't just rely on the surface forms of words in the text.
We will have an organizational meeting Wednesday, September 1, 12:15, Room 6329.