7488499
Chinese-Korean Word Alignment Based on Linguistic Comparison
Word alignment problem between parallel corpora is based on the similar characteristics of two aligned words in two languages. We investigate linguistic-knowledge-based word similarity measures while other previous works heavily rely on statistical information, and their limits will be discussed. Linguistic knowledge is acquired from linguistic comparison of all layers between two languages, for Chinese and Korean in this paper.
9984897
Structure Alignment Using Bilingual Chunking
A new statistical method called "bilingual chunking" for structure alignment is proposed. Different with the existing approaches which align hierarchical structures like sub-trees, our method conducts alignment on chunks. The alignment is finished through a simultaneous bilingual chunking algorithm. Using the constrains of chunk correspondence between source language (SL) and target language (TL), our algorithm can dramatically reduce search space, support time synchronous DP algorithm, and lead to highly consistent chunking. Furthermore, by unifying the POS tagging and chunking in the search process, our algorithm alleviates effectively the influence of POS tagging deficiency to the chunking result.The experimental results with English-Chinese structure alignment show that our model can produce 90% in precision for chunking, and 87% in precision for chunk alignment.
392551
Finding Target Language Correspondence for Lexicalized EBMT System
This paper presents a three-phase approach to find the correspondence in Target Language (TL) sentence for a fragment of Source Language (SL) sentence in a lexicalized EBMT system. To be practical, it exploits surface information as much as possible instead of using parsers. Experiments show that, although not so perfect, it is very robust and effective. The three phases are: First, align the sentence pair at word level in order to provide anchors for phrase alignment. Second, based on the aligned anchors, find all the TL fragments which may possibly correspond to a SL fragment. And finally, using a score function, select the best TL fragment as the correspondence of a SL fragment.
10984042
TREC-9 Experiments at KAIST: QA, CLIR and Batch Filtering
43442956
Representation characterizations of chordal bipartite graphs
A bipartite graph is chordal bipartite if it does not contain an induced cycle of length at least six. We give three representation characterizations of chordal bipartite graphs. More precisely, we show that a bipartite graph is chordal bipartite if and only if the complement is the intersection graph of a family of pairwise compatible claws in a weighted hypercircle. (A hypercircle is a graph which consists of internally vertex disjoint paths between two distinguished vertices, and a claw in a hypercircle is a connected subgraph containing exactly one of the two distinguished vertices.) We also introduce two classes of bipartite graphs, both containing interval bigraphs and interval containment bigraphs. They are compatible subtree intersection bigraphs and compatible subtree containment bigraphs. We show that these two classes are identical to the class of chordal bipartite graphs.
5308839
Knowledge Integration as a Key Problem in an ERP Implementation
While previous studies have focused mainly on the potential benefits and critical success factors associated with ERP implementation, very few have explored the important issues of impediments encountered, especially from a knowledge integration perspective. We have adopted a knowledge integration view that focuses not on the distribution and adoption of particular technological artifacts (ERP systems), but on the knowledge integration processes involved in implementation. The focus of this case study is to understand the nature, structure and process of knowledge integration that occurs during ERP implementation. The paper has identified the integration of knowledge as a key problem in ERP implementation. We discovered four reasons: (1) knowledge is embedded in complex organizational processes; (2) knowledge is embedded in legacy systems; (3) knowledge is embedded in externally based processes; and (4) knowledge is embedded in the ERP system. Based on our analysis, we further suggest that to overcome these impediments to knowledge integration requires the development of interpersonal relations (one-to-one based) and community relations (group-based).
2129354
Visualization of neuronal fiber connections from DT-MRI with global optimization
Diffusion Tensor MRI (DT-MRI) provides valuable 3D data describing diffusion characteristics of water molecules in the human brain. From DT-MRI, it is hoped that neuronal fiber connections among cortical regions can be reliably extracted and adequately interpreted. To achieve this goal, several significant challenges persist. In this paper, by means of dynamic programming we have developed a global fiber reconstruction algorithm enabling efficient visualization of neuronal connections queried by both the start and end points on the fly. Besides an inherent ability to handle noisy datasets, our algorithm also naturally addresses situations where neuronal fibers branch or cross each other. We demonstrate the efficacy of our approach with visualization of neuronal connections among activated brain cortical regions detected by functional MRI (fMRI).
806919
Using Learning to Facilitate the Evolution of Features for Recognizing Visual Concepts
This paper describes a hybrid methodology that integrates genetic algorithms (GAs) and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system.
2023739
Joint video scene segmentation and classification based on hidden Markov model
Video classification and segmentation are fundamental steps for efficient accessing, retrieval and browsing of large amounts of video data. We have developed a scene classification scheme using a hidden Markov model (HMM) based classifier. By utilizing the temporal behaviors of different scene classes, the HMM classifier can effectively classify video segments into one of the pre-defined scene classes. In this paper, we describe two approaches for joint video classification and segmentation based on a HMM, which works by searching for the most likely class transition path utilizing the dynamic programming technique.