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Yeh, J.-Y.; Ke, H.-R.; Yang, W.-P.; Meng, I.-H.: Text summarization using a trainable summarizer and latent semantic analysis (2005)
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- Abstract
- 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.
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Chen, Y.-N.; Ke, H.-R.: ¬A study on mental models of taggers and experts for article indexing based on analysis of keyword usage (2014)
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- Abstract
- This article explores the mental models of article indexing of taggers and experts in keyword usage. Better understanding of the mental models of taggers and experts and their usage gap may inspire better selection of appropriate keywords for organizing information resources. Using a data set of 3,972 tags from CiteULike and 6,708 descriptors from Library and Information Science Abstracts (LISA) from 1,489 scholarly articles of 13 library and information science journals, social network analysis and frequent-pattern tree methods were used to capture and build up the mental models of article indexing of taggers and experts when using keywords, and to generalize their structures and patterns. When measured with respect to the terms used, a power-law distribution, a comparison of terms used as tags and descriptors, social network analysis (including centrality, overall structure, and role equivalent) and frequent-pattern tree analysis, little similarity was found between the mental models of taggers and experts. Twenty-five patterns of path-based rules and 12 identical rules of frequent-pattern trees were shared by taggers and experts. Title- and topic-related keyword categories were the most popular keyword categories used in path-based rules of frequent-pattern trees, and also the most popular members of 25 patterns and the starting point of the 12 identical rules.
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Huang, S.-H.; Ke, H.-R.; Yang, W.-P.: Enhancing semantic digital library query using a content and service inference model (CSIM) (2005)
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- Abstract
- Although digital library (DL) information is becoming increasingly annotated using metadata, semantic query with respect to the structure of metadata has seldom been addressed. The correlation of the two important aspects of DL--content and services--can generate additional semantic relationships. This study proposes a content and service inference model (CSIM) to derive 15 relationships between content and services, and defines functions to manipulate these relationships. Adding the manipulation functions to query predicates facilitates the description of structural semantics of DL content. Moreover, in search for DL services, inferences concerning CSIM relationships can be made to reuse DL service components. Highly promising with experimental results demonstrates that CSIM outperforms the conventional keyword-based method in both content and service queries. Applying CSIM in DL significantly improves semantic queries and alleviates the administrative load when developing novel DL services such as DL query interface, library resource-planning and virtual union catalog system.
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