367503
Large-Scale Dependency Knowledge Acquisition and its Extrinsic Evaluation Through Word Sense Disambiguation
Knowledge plays a central role in intelligent systems. Manual knowledge acquisition is very inefficient and expensive. In this paper, we present (1) an automatic method to acquire a large amount of lexical-dependency knowledge, and (2) an innovative knowledge representation model to effectively minimize the impact of noise and improve knowledge quality. We also propose a new type of knowledge base evaluation-extrinsic evaluation, which evaluates knowledge by its impact to an external application. In our experiments we adopt Word Sense Disambiguation (WSD) as the extrinsic evaluation measure. Due to the lack of sufficient knowledge, existing WSD methods either are brittle and only capable of processing a limited number of topics or words, or provide only mediocre performance in real-world settings. With the support of acquired knowledge, our unsupervised WSD system significantly outperformed the best unsupervised systems participating in SemEval 2007, and achieved the disambiguation accuracy approaching top-performing supervised systems.
10058829
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation.
2346851
CARE: An Automobile Crash Data Analysis Tool
The Critical Analysis Reporting Environment provides an efficient tool for transportation safer engineers and policymakers to use in analyzing the categorical crash data typically obtained from police reports. CAPE has proven successful in the traffic safety community for two reasons: its simplicity and its efficiency. It is currently being used in several states.
17110258
An ontology for scientific information in a Grid environment: the earth system Grid
In the emerging world of Grid Computing, shared computational, data, other distributed resources are becoming available to enable scientific advancement through collaborative research and collaboratories. This paper describes the increasing role of ontologies in the context of Grid Computing for obtaining, comparing and analyzing data. We present ontology entities and a declarative model that provide the outline for an ontology of scientific information. Relationships between concepts are also given. The implementation of some concepts described in this ontology is discussed within the context of the Earth System Grid II (ESG)[1].
19011421
A Host Architecture for Automobile License Plate Recognition
Automobile license plate recognition (ALPR) is becoming increasingly important to homeland security and law enforcement. There are a number of commercial vendors with tag recognition hardware and software in the ALPR market. Many of these vendors have developed open tag recognition solutions that run on laptop PCs with commodity video-capture devices as peripherals. Such PC-based solutions are quite conducive to an open architecture for data transmission and sharing between applications. In this paper, we present an architecture that supports such a commodity tag recognition solution as its centerpiece. We then discuss a recent pilot project where this architecture was implemented and tested.
9793653
Paradoxical Relationship between Output and Input Regularity for the FitzHugh-Nagumo Model
We examine the effects of changing the coefficient of variation (CV) of the inter-stimulus interval on the CV of the output interspike interval (ISI), using constant magnitude, supra-threshold point-process stimuli of the membrane potential variable in the FitzHugh-Nagumo model. The coefficient of variation of the input is changed within the context of a displaced exponential distribution. We find that for some values of mean inter-stimulus interval, CV of ISI has an inverse relationship with input coefficient of variation, whereas for other mean stimulation rates, CV of ISI increases with input coefficient of variation. Over a wide range of input regularity CV of ISI is approximately constant, and is less than 0.4.
18886117
An Effective Human Motion Classification Approach using Knowledge Representation in Qualitative Normalised Templates
Classification of human motion in video data is essential in numerous applications. However, problems arise as the human exhibits complex and dynamic motion that is nonlinear and time varying. In this paper, we propose a knowledge-based human motion classification framework that employs fuzzy qualitative reasoning to address these problems. Our approach utilises the rich contextual information (e.g. structural and transitional characteristic of human motion) captured in video sequence to effectively study and recognise human motion. With the aid of domain knowledge, a set of fuzzy rules are defined in the knowledge base. This work is in contrast with previous attempts that depend solely on the trajectories of the body parts. Experimental results on two classes of motion (e.g. walking and running) that result in similar motions; and a comparison with the conventional method has demonstrated and validated the effectiveness of the proposed method in improving the perception of human motion.