Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)
1Huang, Y. ; Cox, A.M. ; Sbaffi, L.: Research data management policy and practice in Chinese university libraries.
In: Journal of the Association for Information Science and Technology. 72(2021) no.4, S.493-506.
Abstract: On April 2, 2018, the State Council of China formally released a national Research Data Management (RDM) policy "Measures for Managing Scientific Data". In this context and given that university libraries have played an important role in supporting RDM at an institutional level in North America, Europe, and Australasia, the aim of this article is to explore the current status of RDM in Chinese universities, in particular how university libraries have been involved in taking the agenda forward. This article uses a mixed-methods data collection approach and draws on a website analysis of university policies and services; a questionnaire for university librarians; and semi-structured interviews. Findings indicate that Research Data Service at a local level in Chinese Universities are in their infancy. There is more evidence of activity in developing data repositories than support services. There is little development of local policy. Among the explanations of this may be the existence of a national-level infrastructure for some subject disciplines, the lack of professionalization of librarianship, and the relatively weak resonance of openness as an idea in the Chinese context.
Inhalt: Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24413.
2Huang, Y. ; Bu, Y. ; Ding, Y. ; Lu, W.: From zero to one : a perspective on citing.
In: Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1098-1107.
Abstract: This article investigates the lengths of time that publications with different numbers of citations take to receive their first citation (the beginning stage), and then compares the lengths of time to receive two or more citations after receiving the first citation (the accumulative stage) in the field of computer science. We find that in the beginning stage, that is, from zero to one citation, high-, medium-, and low-cited publications do not obviously exhibit different lengths of time. However, in the accumulative stage, that is, from one to N citations, highly cited publications begin to receive citations much more rapidly than medium- and low-cited publications. Moreover, as N increases, the difference in receiving new citations among high-, medium-, and low-cited publications increases quite significantly.
Inhalt: Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24177.
3Song, J. ; Huang, Y. ; Qi, X. ; Li, Y. ; Li, F. ; Fu, K. ; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents.
In: Journal of the Association for Information Science and Technology. 67(2016) no.4, S.915-927.
Abstract: The objective of this paper is to propose a hierarchical topic evolution model (HTEM) that can organize time-varying topics in a hierarchy and discover their evolutions with multiple timescales. In the proposed HTEM, topics near the root of the hierarchy are more abstract and also evolve in the longer timescales than those near the leaves. To achieve this goal, the distance-dependent Chinese restaurant process (ddCRP) is extended to a new nested process that is able to simultaneously model the dependencies among data and the relationship between clusters. The HTEM is proposed based on the new process for time-stamped documents, in which the timestamp is utilized to measure the dependencies among documents. Moreover, an efficient Gibbs sampler is developed for the proposed HTEM. Our experimental results on two popular real-world data sets verify that the proposed HTEM can capture coherent topics and discover their hierarchical evolutions. It also outperforms the baseline model in terms of likelihood on held-out data.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23439/abstract.
Themenfeld: Data Mining
4Chen, Z. ; Huang, Y. ; Tian, J. ; Liu, X. ; Fu, K. ; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic.
In: Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1913-1922.
Abstract: Sentiment analysis mainly focuses on the study of one's opinions that express positive or negative sentiments. With the explosive growth of web documents, sentiment analysis is becoming a hot topic in both academic research and system design. Fine-grained sentiment analysis is traditionally solved as a 2-step strategy, which results in cascade errors. Although joint models, such as joint sentiment/topic and maximum entropy (MaxEnt)/latent Dirichlet allocation, are proposed to tackle this problem of sentiment analysis, they focus on the joint learning of both aspects and sentiments. Thus, they are not appropriate to solve the cascade errors for sentiment analysis at the sentence or subsentence level. In this article, we present a novel jointly fine-grained sentiment analysis framework at the subsentence level with Markov logic. First, we divide the task into 2 separate stages (subjectivity classification and polarity classification). Then, the 2 separate stages are processed, respectively, with different feature sets, which are implemented by local formulas in Markov logic. Finally, global formulas in Markov logic are adopted to realize the interactions of the 2 separate stages. The joint inference of subjectivity and polarity helps prevent cascade errors. Experiments on a Chinese sentiment data set manifest that our joint model brings significant improvements.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23301/abstract.
5Shen, M. ; Liu, D.-R. ; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies.
In: Journal of Intelligent Information Systems.
Abstract: Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is- part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
Inhalt: Vgl.: http://www.springerlink.com/content/f493xxq201163354/.
Themenfeld: Computerlinguistik ; Wissensrepräsentation
6Liu, R.-L. ; Huang, Y.-C.: Ranker enhancement for proximity-based ranking of biomedical texts.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.12, S.2479-2495.
Abstract: Biomedical decision making often requires relevant evidence from the biomedical literature. Retrieval of the evidence calls for a system that receives a natural language query for a biomedical information need and, among the huge amount of texts retrieved for the query, ranks relevant texts higher for further processing. However, state-of-the-art text rankers have weaknesses in dealing with biomedical queries, which often consist of several correlating concepts and prefer those texts that completely talk about the concepts. In this article, we present a technique, Proximity-Based Ranker Enhancer (PRE), to enhance text rankers by term-proximity information. PRE assesses the term frequency (TF) of each term in the text by integrating three types of term proximity to measure the contextual completeness of query terms appearing in nearby areas in the text being ranked. Therefore, PRE may serve as a preprocessor for (or supplement to) those rankers that consider TF in ranking, without the need to change the algorithms and development processes of the rankers. Empirical evaluation shows that PRE significantly improves various kinds of text rankers, and when compared with several state-of-the-art techniques that enhance rankers by term-proximity information, PRE may more stably and significantly enhance the rankers.
7Wu, L.-L. ; Chuang, Y.-L. ; Chen, P.-Y.: Motivation for using search engines : a two-factor model.
In: Journal of the American Society for Information Science and Technology. 59(2008) no.11, S.1829-1840.
Abstract: From a user-centered perspective, an effective search engine needs to attract new users to try out its features, and retain those users so that they continue using the features. In this article, we investigate the relations between users'motivation for using (i.e., trying out and continuing to use) a search engine and the engine's functional features. Based on Herzberg's two-factor theory (F. Herzberg, 2003; F. Herzberg, M. Bernard, & B. Snyderman, 1959), the features can be categorized as hygiene factors and motivation factors. Hygiene factors support the query process and provide a basic task context for information seeking that allows users to access relevant information. Motivation factors, on the other hand, help users navigate (i.e., browse) and comprehend the retrieved information, related to the task-content aspect of information seeking. Given the consistent findings that hygiene factors induce work motivation for a shorter period of time, it is hypothesized that hygiene factors are more effective in attracting users; while motivation factors are more effective in retaining than in attracting users. A survey, with 758 valid participants, was conducted to test the hypotheses. The empirical results provide substantial support for the proposed hypotheses and suggest that the two-factor theory can account for the motivation for using a search engine.
Anmerkung: Vgl.: Wu, L.-L., A. Chuang u. P.-Y. Chen: Correction to Wu, L., Chuang, A., & Chen, P. (2008). Motivation for using search engines: A two factor model. Journal of American Society for Information Science and Technology, 59(11), 1829-1840. In: Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.214-216.
8Huang, Y.-L.: ¬A theoretic and empirical research of cluster indexing for Mandarine Chinese full text document.
In: Bulletin of library and information science. 1998, no.24, S.44-68.
Abstract: Since most popular commercialized systems for full text retrieval are designed with full text scaning and Boolean logic query mode, these systems use an oversimplified relationship between the indexing form and the content of document. Reports the use of Singular Value Decomposition (SVD) to develop a Cluster Indexing Model (CIM) based on a Vector Space Model (VSM) in orer to explore the index theory of cluster indexing for chinese full text documents. From a series of experiments, it was found that the indexing performance of CIM is better than traditional VSM, and has almost equivalent effectiveness of the authority control of index terms
Anmerkung: In Chinesisch
Themenfeld: Automatisches Klassifizieren ; Volltextretrieval