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Hsu, M.-H.; Chen, H.-H.: Efficient and effective prediction of social tags to enhance Web search (2011) 0.04
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Abstract

As the web has grown into an integral part of daily life, social annotation has become a popular manner for web users to manage resources. This method of management has many potential applications, but it is limited in applicability by the cold-start problem, especially for new resources on the web. In this article, we study automatic tag prediction for web pages comprehensively and utilize the predicted tags to improve search performance. First, we explore the stabilizing phenomenon of tag usage in a social bookmarking system. Then, we propose a two-stage tag prediction approach, which is efficient and is effective in making use of early annotations from users. In the first stage, content-based ranking, candidate tags are selected and ranked to generate an initial tag list. In the second stage, random-walk re-ranking, we adopt a random-walk model that utilizes tag co-occurrence information to re-rank the initial list. The experimental results show that our algorithm effectively proposes appropriate tags for target web pages. In addition, we present a framework to incorporate tag prediction in a general web search. The experimental results of the web search validate the hypothesis that the proposed framework significantly enhances the typical retrieval model.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.8, S.1473-1487

Theme

Social tagging

Chen, H.-H.; Lin, W.-C.; Yang, C.; Lin, W.-H.: Translating-transliterating named entities for multilingual information access (2006) 0.02

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Date: 4. 6.2006 19:52:22
Source: Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.645-659

Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007) 0.02
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Abstract

Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1838-1850
Ku, L.-W.; Ho, H.-W.; Chen, H.-H.: Opinion mining and relationship discovery using CopeOpi opinion analysis system (2009) 0.02
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Abstract

We present CopeOpi, an opinion-analysis system, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. Objects that yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. CopeOpi discovers relationships among objects based on their opinion-tracking plots and collocations. Event bursts are detected from the tracking plots, and the strength of opinion relationships is determined by the coverage of these plots. To evaluate opinion mining, we use the NTCIR corpus annotated with opinion information at sentence and document levels. CopeOpi achieves sentence- and document-level f-measures of 62% and 74%. For relationship discovery, we collected 1.3M economics-related documents from 93 Web sources over 22 months, and analyzed collocation-based, opinion-based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock-price variations, and selected these as the gold standard for evaluation. Results show that opinion-based and collocation-based models complement each other, and that integrated models perform the best. The top 25, 50, and 100 pairs discovered achieve precision rates of 1, 0.92, and 0.79, respectively.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.7, S.1486-1503

Huang, H.-H.; Wang, J.-J.; Chen, H.-H.: Implicit opinion analysis : extraction and polarity labelling (2017) 0.01

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Source: Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2076-2087

Chen, H.-H.; Kuo, J.-J.; Huang, S.-J.; Lin, C.-J.; Wung, H.-C.: ¬A summarization system for Chinese news from multiple sources (2003) 0.01

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Source: Journal of the American Society for Information Science and technology. 54(2003) no.13, S.1224-1236

Lee, Y.-Y.; Ke, H.; Yen, T.-Y.; Huang, H.-H.; Chen, H.-H.: Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement (2020) 0.01

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Source: Journal of the Association for Information Science and Technology. 71(2020) no.6, S.657-670

Lee, L.-H.; Chen, H.-H.: Mining search intents for collaborative cyberporn filtering (2012) 0.00

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Source: Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.366-376

Liu, J.S.; Chen, H.-H.; Ho, M.H.-C.; Li, Y.-C.: Citations with different levels of relevancy : tracing the main paths of legal opinions (2014) 0.00

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Source: Journal of the Association for Information Science and Technology. 65(2014) no.12, S.2479-2488

Lee, L.-H.; Juan, Y.-C.; Tseng, W.-L.; Chen, H.-H.; Tseng, Y.-H.: Mining browsing behaviors for objectionable content filtering (2015) 0.00

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Source: Journal of the Association for Information Science and Technology. 66(2015) no.5, S.930-942

Bian, G.-W.; Chen, H.-H.: Cross-language information access to multilingual collections on the Internet (2000) 0.00

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Date: 16. 2.2000 14:22:39

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