Product record normalization across different web sites.

Wong, Tik Shun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 57-62).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Thesis Contributions --- p.10Chapter 1.3 --- Thesis Organization --- p.11Chapter 2 --- Literature Review --- p.12Chapter 2.1 --- Related Work on Product Record Normalization --- p.12Chapter 2.2 --- Related Work on Information Extraction --- p.15Chapter 2.2.1 --- Information Extraction Methods for Unstructured Documents --- p.16Chapter 2.2.2 --- Wrappers for Information Extraction --- p.16Chapter 2.2.3 --- Supervised Methods for Information Extraction --- p.17Chapter 2.2.4 --- Semi-supervised Methods for Information Extraction --- p.20Chapter 2.2.5 --- Unsupervised Methods for Information Extraction --- p.21Chapter 2.2.6 --- Probabilistic Methods for Information Extraction --- p.23Chapter 3 --- Background and Problem Definition --- p.26Chapter 3.1 --- Background --- p.26Chapter 3.2 --- Problem Definition --- p.29Chapter 4 --- Our Approach --- p.31Chapter 4.1 --- Generative Model --- p.31Chapter 4.2 --- Our Inference Method --- p.34Chapter 5 --- Experiments --- p.41Chapter 5.1 --- Experimental Setup --- p.41Chapter 5.2 --- Experimental Results --- p.49Chapter 5.3 --- The Effect of Reference Product Prior --- p.52Chapter 5.4 --- The Effect of Layout Information --- p.53Chapter 6 --- Conclusions and Future Work --- p.55Bibliography --- p.57Chapter A --- Detailed Performance of Product Record Normalization --- p.6

Unsupervised extraction and normalization of product attributes from web pages.

Xiong, Jiani."July 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 59-63).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Motivation --- p.4Chapter 1.3 --- Our Approach --- p.8Chapter 1.4 --- Potential Applications --- p.12Chapter 1.5 --- Research Contributions --- p.13Chapter 1.6 --- Thesis Organization --- p.15Chapter 2 --- Literature Survey --- p.16Chapter 2.1 --- Supervised Extraction Approaches --- p.16Chapter 2.2 --- Unsupervised Extraction Approaches --- p.19Chapter 2.3 --- Attribute Normalization --- p.21Chapter 2.4 --- Integrated Approaches --- p.22Chapter 3 --- Problem Definition and Preliminaries --- p.24Chapter 3.1 --- Problem Definition --- p.24Chapter 3.2 --- Preliminaries --- p.27Chapter 3.2.1 --- Web Pre-processing --- p.27Chapter 3.2.2 --- Overview of Our Framework --- p.31Chapter 3.2.3 --- Background of Graphical Models --- p.32Chapter 4 --- Our Proposed Framework --- p.36Chapter 4.1 --- Our Proposed Graphical Model --- p.36Chapter 4.2 --- Inference --- p.41Chapter 4.3 --- Product Attribute Information Determination --- p.47Chapter 5 --- Experiments and Results --- p.49Chapter 6 --- Conclusion --- p.57Bibliography --- p.59Chapter A --- Dirichlet Process --- p.64Chapter B --- Hidden Markov Models --- p.6

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design