4695730
MRE: A Study on Evolutionary Language Understanding
The lack of well-annotated data is always one of the biggest problems for most training-based dialogue systems. Without enough training data, it’s almost impossible for a trainable system to work. In this paper, we explore the evolutionary language understanding approach to build a natural language understanding machine in a virtual human training project. We build the initial training data with a finite state machine. The language understanding system is trained based on the automated data first and is improved as more and more real data come in, which is proved by the experimental results.
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.
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.
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.
9006375
Cooperative model-based language understanding
In this paper, we propose a novel Cooperative Model for natural language understanding in a dialogue system. We build this based on both Finite State Model (FSM) and Statistical Learning Model (SLM). FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility. Statistical approach is much more robust but less accurate. Cooperative Model incorporates all the three strategies together and thus can suppress all the shortcomings of different strategies and has all the advantages of the three strategies.
1310259
Mining and Re-ranking for Answering Biographical Queries on the Web
The rapid growth of the Web has made itself a huge and valuable knowledge base. Among them, biographical information is of great interest to society. However, there has not been an efficient and complete approach to automated biography creation by querying the web. This paper describes an automatic web-based question answering system for biographical queries. Ad-hoc improvements on pattern learning approaches are proposed for mining biographical knowledge. Using bootstrapping, our approach learns surface text patterns from the web, and applies the learned patterns to extract relevant information. To reduce human labeling cost, we propose a new IDF-inspired reranking approach and compare it with pattern's precision-based re-ranking approach. A comparative study of the two re-ranking models is conducted. The tested system produces promising results for answering biographical queries.
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.
31361781
Adaptive Processing of Tree-Structure Image Representation
Much research on image analysis and processing has been carried out for the last few decades. However, it is still challenging to represent the image contents effectively and satisfactorily. In this paper, a segmentation-free tree-structure image representation is presented. In order to learn the structure representation, a back-propagation through structure (BPTS) algorithm is adopted. Experiments on plant image classification and retrieval refining using only six visual features were conducted on a plant image database and a natural scene image database, respectively. Encouraging results have been achieved.
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.
14712985
Cryptanalysis of a Knapsack Based Two-Lock Cryptosystem
In this paper we break a knapsack based two-lock cryptosystem proposed at ICICS’03 [7]. The two-lock cryptosystem is a commutative encryption algorithm that is very useful for the construction of the general t-out-of-n oblivious transfers and millionaire protocol. However, our analysis shows that the proposed knapsack based two-lock cryptosystem is extremely insecure. The serious flaw is that the sender in the two-lock cryptosystem can retrieve the secret key of the receiver fairly easily. We have implemented the attack on a Pentium 4 2.5 GHz processor. For the parameters given in [7], it takes only several minutes to break that knapsack based two-lock cryptosystem.
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.