246827
Handling Biographical Questions with Implicature
Traditional question answering systems adopt the following framework: parsing questions, searching for relevant documents, and identifying/generating answers. However, this framework does not work well for questions with hidden assumptions and implicatures. In this paper, we describe a novel idea, a cascading guidance strategy, which can not only identify potential traps in questions but further guide the answer extraction procedure by recognizing whether there are multiple answers for a question. This is the first attempt to solve implicature problem for complex QA in a cascading fashion using N-gram language models as features. We here investigate questions with implicatures related to biography facts in a web-based QA system, Power-Bio. We compare the performances of Decision Tree, Naive Bayes, SVM (Support Vector Machine), and ME (Maximum Entropy) classification methods. The integration of the cascading guidance strategy can help extract answers for questions with implicatures and produce satisfactory results in our experiments.
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.
6618252
Learning to Detect Conversation Focus of Threaded Discussions
In this paper we present a novel feature-enriched approach that learns to detect the conversation focus of threaded discussions by combining NLP analysis and IR techniques. Using the graph-based algorithm HITS, we integrate different features such as lexical similarity, poster trustworthiness, and speech act analysis of human conversations with feature-oriented link generation functions. It is the first quantitative study to analyze human conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Experimental results using a threaded discussion corpus from an undergraduate class show that it achieves significant performance improvements compared with the baseline system.
12677295
Security Analysis of the Generalized Self-shrinking Generator
In this paper, we analyze the generalized self-shrinking generator newly proposed in [8]. Some properties of this generator are described and an equivalent definition is derived, after which two attacks are developed to evaluate its security. The first attack is an improved clock-guessing attack using short keystream with the filter function (vector G) known. The complexity of this attack is O(20.694n), where n is the length of the LFSR used in the generator. This attack shows that the generalized self-shrinking generator can not be more secure than the self-shrinking generator, although much more computations may be required by it. Our second attack is a fast correlation attack with the filter function (vector G) unknown. We can restore both the initial state of the LFSR with arbitrary weight feedback polynomial and the filter function (vector G) with complexity much lower than the exhaustive search. For example, for a generator with 61-stage LFSR, given a keystream segment of 217.1 bits, the complexity is around 256, which is much lower than 2122, the complexity of the exhaustive search.
30786575
Content-Based Retrieval of Multimedia Information
The recent information explosion has led to massively increased demand for multimedia data storage and retrieval techniques. Content-based retrieval is an important alternative and complement to the traditional keyword-based searching for multimedia data and can greatly enhance information management. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and recently the Center for Multimedia Signal Processing (CMSP) have conducted systematic studies and research activities on this topic. Some of the works relating to content-based image/video retrieval and their applications are briefly presented in this paper.
1053846
Information technology applications in biomedical functional imaging
In parallel with the rapid advances in computer technology, biomedical functional imaging is having an ever-increasing impact on healthcare. Functional imaging allows us to see dynamic processes quantitatively in the living human body. However, as we need to deal with four-dimensional time-varying images, space requirements and computational complexity are extremely high. This makes information management, processing and communication difficult. Using the minimum amount of data to represent the required information, developing fast algorithms to process the data, organizing the data in such a way as to facilitate information management, and extracting the maximum amount of useful information from the recorded data have become important research tasks in biomedical information technology. For the last ten years, the Biomedical and Multimedia Information Technology (EMIT) Group at Sydney University and, recently, the Center for Multimedia Signal Processing at Hong Kong Polytechnic University have conducted systematic studies on these topics. Some of the results relating to functional imaging data acquisition, compression, storage, management, processing, modeling and simulation are briefly reported in this paper.