101077
Extractive Summarization using Inter- and Intra- Event Relevance
Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences describe. Each event has its own internal structure, and meanwhile often relates to other events semantically, temporally, spatially, causally or conditionally. In this paper, we define an event as one or more event terms along with the named entities associated, and present a novel approach to derive intra- and inter- event relevance using the information of internal association, semantic relatedness, distributional similarity and named entity clustering. We then apply PageRank ranking algorithm to estimate the significance of an event for inclusion in a summary from the event relevance derived. Experiments on the DUC 2001 test data shows that the relevance of the named entities involved in events achieves better result when their relevance is derived from the event terms they associate. It also reveals that the topic-specific relevance from documents themselves outperforms the semantic relevance from a general purpose knowledge base like Word-Net.
9032052
Deriving Event Relevance from the Ontology Constructed with Formal Concept Analysis
In this paper, we present a novel approach to derive event relevance from event ontology constructed with Formal Concept Analysis (FCA), a mathematical approach to data analysis and knowledge representation. The ontology is built from a set of relevant documents and according to the named entities associated to the events. Various relevance measures are explored, from binary to scaled, and from symmetrical to asymmetrical associations. We then apply the derived event relevance to the task of multi-document summarization. The experiments on DUC 2004 data set show that the relevant-event-based approaches outperform the independent-event-based approach.
18109532
Building Document Graphs for Multiple News Articles Summarization: An Event-Based Approach
Since most of news articles report several events and these events are referred in many related documents, we propose an event-based approach to visualize documents as graph on different conceptual granularities. With graph-based ranking algorithm, we illustrate the application of document graph to multi-document summarization. Experiments on DUC data indicate that our approach is competitive with state-of-the-art summarization techniques. This graphical representation which does not require training corpora can be potentially adapted to other languages.
13976369
Design of a nanosensor array architecture
This paper describes a nanowire sensor array architecture for high-speed, high-accuracy sensor systems. The chip has very simple processing elements (PEs) in a massively parallel architecture, in which each PE is directly connected to seven sensors. A sampling rate of 100 ns is enough to realized high-speed sensing feedback for electronic nose. We aim to create a very simple architecture, because a compact design is required ton integrate as many PEs as possible on a single chip. A widely used, easy to implement estimator-minimum distance classifier is introduced to realize the pattern recognition. A sample design is implemented in VHDL and has been simulated and synthesized using TSMC 0.25 standard cell library and a commercial 0.16 standard cell library.
7928906
Augmenting Exercise Systems with Virtual Exercise Environment
Adhering to an exercise program is a challenge for everybody who wants to build a healthier body and lifestyle through physical exercise. We have developed an Virtual Exercise Environment (VEE) that augments stationary exercise equipment with virtual reality techniques to make exercising more enjoyable. Our VEE system consists of a recording system to capture video, distance and incline data about real trails, and a playback system which "displays" both video and terrain data in the form of video speed and resistance. Trails are played back according to the speed the user generates on the stationary exercise equipment. The system uses commodity capture and display devices and supports standard interfaces for existing exercise equipment. User studies have shown that users enjoy the ability to guage their progress and performance via their progress through trail playback in the VEE.
9571243
trNon-greedy active learning for text categorization using convex ansductive experimental design
In this paper we propose a non-greedy active learning method for text categorization using least-squares support vector machines (LSSVM). Our work is based on transductive experimental design (TED), an active learning formulation that effectively explores the information of unlabeled data. Despite its appealing properties, the optimization problem is however NP-hard and thus--like most of other active learning methods--a greedy sequential strategy to select one data example after another was suggested to find a suboptimum. In this paper we formulate the problem into a continuous optimization problem and prove its convexity, meaning that a set of data examples can be selected with a guarantee of global optimum. We also develop an iterative algorithm to efficiently solve the optimization problem, which turns out to be very easy-to-implement. Our text categorization experiments on two text corpora empirically demonstrated that the new active learning algorithm outperforms the sequential greedy algorithm, and is promising for active text categorization applications.
2338255
Fast exact maximum likelihood estimation for mixture of language models
A common language modeling approach assumes the data D is generated from a mixture of several language models. EM algorithm is usually used to find the maximum likelihood estimation of one unknown mixture component, given the mixture weights and the other language models. In this paper, we provide an efficient algorithm of O(k) complexity to find the exact solution, where k is the number of words occurred at least once in D. Another merit is that the probabilities of many words are exactly zeros, which means that the mixture language model also serves as a feature selection technique.
7498053
Multi-labelled classification using maximum entropy method
Many classification problems require classifiers to assign each single document into more than one category, which is called multi-labelled classification. The categories in such problems usually are neither conditionally independent from each other nor mutually exclusive, therefore it is not trivial to directly employ state-of-the-art classification algorithms without losing information of relation among categories. In this paper, we explore correlations among categories with maximum entropy method and derive a classification algorithm for multi-labelled documents. Our experiments show that this method significantly outperforms the combination of single label approach.