124544
Towards Modeling Threaded Discussions using Induced Ontology Knowledge
Online discussion boards are a popular form of web-based computer-mediated communication, especially in the areas of distributed education and customer support. Automatic analysis for discussion understanding would enable better information assessment and assistance. This paper describes an extensive study of the relationship between individual messages and full discussion threads. We present a new approach to classifying discussions using a Rocchio-style classifier with little cost for data labeling. In place of a labeled data set, we employ a coarse domain ontology that is automatically induced from a canonical text in a novel way and use it to build discussion topic profiles. We describe a new classify-by-dominance strategy for classifying discussion threads and demonstrate that in the presence of noise it can perform better than the standard classify-as-a-whole approach with an error rate reduction of 16.8%. This analysis of human conversation via online discussions provides a basis for the development of future information extraction and question answering techniques.
9916704
An intelligent discussion-bot for answering student queries in threaded discussions
This paper describes a discussion-bot that provides answers to students' discussion board questions in an unobtrusive and human-like way. Using information retrieval and natural language processing techniques, the discussion-bot identifies the questioner's interest, mines suitable answers from an annotated corpus of 1236 archived threaded discussions and 279 course documents and chooses an appropriate response. A novel modeling approach was designed for the analysis of archived threaded discussions to facilitate answer extraction. We compare a self-out and an all-in evaluation of the mined answers. The results show that the discussion-bot can begin to meet students' learning requests. We discuss directions that might be taken to increase the effectiveness of the question matching and answer extraction algorithms. The research takes place in the context of an undergraduate computer science course.
8658211
A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study
Positron emission tomography (PET) is an important tool for enabling quantification of human brain function. However, quantitative studies using tracer kinetic modeling require the measurement of the tracer time-activity curve in plasma (PTAC) as the model input function. It is widely believed that the insertion of arterial lines and the subsequent collection and processing of the biomedical signal sampled from the arterial blood are not compatible with the practice of clinical PET, as it is invasive and exposes personnel to the risks associated with the handling of patient blood and radiation dose. Therefore, it is of interest to develop practical noninvasive measurement techniques for tracer kinetic modeling with PET. In this paper, a technique is proposed to extract the input function together with the physiological parameters from the brain dynamic images alone. The identifiability of this method is tested rigorously by using Monte Carlo simulation. The results show that the proposed method is able to quantify all the required parameters by using the information obtained from two or more regions of interest (ROIs) with very different dynamics in the PET dynamic images. There is no significant improvement in parameter estimation for the local cerebral metabolic rate of glucose (LCMRGlc) if there are more than three ROIs. The proposed method can provide very reliable estimation of LCMRGlc, which is our primary interest in this study.