Submitted in 2014 but was rejected in early 2015. Preparing for submission to ISR.
Abstract:
To make good decisions, businesses try to gather good intelligence information. Yet managing and processing a large amount of unstructured information and data stand in the way of greater business knowledge. An effective business intelligence tool must be able to access quality information from a variety of sources in a variety of forms, and it must support people as they search for and analyze that information. The EBizPort system was designed to address information needs for the business/IT community. EBizPort's collection-building process is designed to acquire credible, timely, and relevant information. The user interface provides access to collected and metasearched resources using innovative tools for summarization, categorization, and visualization. The effectiveness, efficiency, usability, and information quality of the EBizPort system were measured. EBizPort significantly outperformed Brint, a business search portal, in search effectiveness, information quality, user satisfaction, and usability. Users particularly liked EBizPort's clean and user-friendly interface. Results from our evaluation study suggest that the visualization function added value to the search and analysis process, that the generalizable collection-building technique can be useful for domain-specific information searching on the Web, and that the search interface was important for Web search and browse support.
Abstract:
Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.
Abstract:
Most information retrieval techniques have been developed for English and other Western languages. As the second largest Internet language, Chinese provides a good setting for study of how search engine techniques developed for English could be generalized for use in other languages to facilitate Internet searching and browsing in a multilingual world. This paper reviews different techniques used in search engines and proposes an integrated approach to development of a Chinese medical portal: CMedPort. The techniques integrated into CMedPort include meta-search engines, cross-regional search, summarization and categorization. A user study was conducted to compare the effectiveness, efficiency and user satisfaction of CMedPort and three major Chinese search engines. Preliminary results from the user study show that CMedPort achieves similar accuracy in searching tasks, and higher effectiveness and efficiency in browsing tasks than Openfind, a Taiwan search engine portal. We believe that the proposed approach can be used to support Chinese information seeking in Web-based digital library applications. © Springer-Verlag Berlin Heidelberg 2003.
