Browsing by Author "Yeh, JY"

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Chinese text summarization using a trainable summarizer and latent semantic analysis
(2002)
Yeh, JY
;
Ke, HR
;
Yang, WP
;
資訊工程學系
;
圖書館
In this paper, two novel approaches are proposed to extract important sentences from a document to create its summary. The first is a corpus-based approach using feature analysis. It brings up three new ideas: 1) to employ ranked position to emphasize the significance of sentence position, 2) to reshape word unit to achieve higher accuracy of keyword importance, and 3) to train a score function by the genetic algorithm for obtaining a suitable combination of feature weights. The second approach combines the ideas of latent semantic analysis and text relationship maps to interpret conceptual structures of a document. Both approaches are applied to Chinese text summarization. The two approaches were evaluated by using a data corpus composed of 100 articles about politics from New Taiwan Weekly, and when the compression ratio was 30%, average recalls of 52.0% and 45.6% were achieved respectively.
Comparison and combination of textual and visual features for interactive cross-language image retrieval
(2005)
Cheng, PC
;
Yeh, JY
;
Ke, HR
;
Chien, BC
;
Yang, WP
This paper concentrates on the user-centered search task at ImageCLEF 2004. In this work, we combine both textual and visual features for cross-language image retrieval, and propose two interactive retrieval systems T_ICLEF and VCT_ICLEF. The first one incorporates a relevance feedback mechanism based on textual information while the second one combines textual and image information to help users find a target image. The experimental results show that VCT_ICLEF had a better performance in almost all cases. Overall, it helped users find the topic image within a fewer iterations with a maximum of 2 iterations saved. Our user survey also reported that a combination of textual and visual information is helpful to indicate to the system what a user really wanted in mind.
Temperature-dependent photoluminescence of highly strained InGaAsN/GaAs quantum wells (lambda=1.28-1.45 mu m) with GaAsP strain-compensated layers
(2005-08-01)
Lai, FI
;
Kuo, HC
;
Chang, YH
;
Tsai, MY
;
Chu, CP
The effects of nitrogen incorporation into the In(0.4)Ga(0.6)As(1-x)N(x)/GaAs single quantum wells (SQWs), where x = 0.5 and 2%, grown on GaAs substrates by metalorganic chemical vapor deposition (MOCVD) were investigated using photoluminescence (PL) and high-resolution transmission electron microscopy (HRTEM). The evolution of the excitation-dependent PL and PL-peak position with temperature between 10 and 300 K shows that quantum-dot-like states occurred at that high nitrogen incorporation (x = 2%) and were confirmed by an HRTEM image which showed small dark regions about 2 - 3 nm in size was found in the interface of In(0.4)Ga(0.6)As(0.98)N(0.02) and GaAs. Our investigations indicate that high nitrogen incorporation into the In(0.4)Ga(0.6)As(1-x)N(x)/GaAs system influenced carrier localization and might cause the formation of the dot-like states.
Text summarization using a trainable summarizer and latent semantic analysis
(2005-01-01)
Yeh, JY
;
Ke, HR
;
Yang, WP
;
Meng, IH
;
資訊工程學系
This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSA + T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively. (C) 2004 Elsevier Ltd. All rights reserved.