The Experts below are selected from a list of 90762 Experts worldwide ranked by ideXlab platform
Hong Yu - One of the best experts on this subject based on the ideXlab platform.
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Key Concept identification for medical information retrieval
Empirical Methods in Natural Language Processing, 2015Co-Authors: Jiaping Zheng, Hong YuAbstract:The difficult language in Electronic Health Records (EHRs) presents a challenge to patients’ understanding of their own conditions. One approach to lowering the barrier is to provide tailored patient education based on their own EHR notes. We are developing a system to retrieve EHR note-tailored online consumer oriented health education materials. We explored topic model and Key Concept identification methods to construct queries from the EHR notes. Our experiments show that queries using identified Key Concepts with pseudo-relevance feedback significantly outperform (over 10-fold improvement) the baseline system of using the full text note.
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EMNLP - Key Concept Identification for Medical Information Retrieval
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015Co-Authors: Jiaping Zheng, Hong YuAbstract:The difficult language in Electronic Health Records (EHRs) presents a challenge to patients’ understanding of their own conditions. One approach to lowering the barrier is to provide tailored patient education based on their own EHR notes. We are developing a system to retrieve EHR note-tailored online consumer oriented health education materials. We explored topic model and Key Concept identification methods to construct queries from the EHR notes. Our experiments show that queries using identified Key Concepts with pseudo-relevance feedback significantly outperform (over 10-fold improvement) the baseline system of using the full text note.
Santiago Grijalva - One of the best experts on this subject based on the ideXlab platform.
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Error analysis in electric power system available transfer capability computation
Decision Support Systems, 1999Co-Authors: Peter W Sauer, Santiago GrijalvaAbstract:A Key Concept in the restructuring of the electric power industry is the ability to accurately and rapidly quantify the capabilities of the transmission system. Transmission transfer capability is limited by a number of different mechanisms, including thermal, voltage, and stability constraints. This paper discusses the available transfer capability (ATC) definitions and determination guidelines approved by the North American Electric Reliability Council (NERC) and presents several Concepts for dealing with the potential errors and technical challenges of computation. © 1999 Elsevier Science B.V. All rights reserved.
Israr Ullah - One of the best experts on this subject based on the ideXlab platform.
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Key Concept identification a comprehensive analysis of frequency and topical graph based approaches
Information-an International Interdisciplinary Journal, 2018Co-Authors: Muhammad Aman, Abas Md B Said, Said Jadid Abdul Kadir, Israr UllahAbstract:Automatic Key Concept extraction from text is the main challenging task in information extraction, information retrieval and digital libraries, ontology learning, and text analysis. The statistical frequency and topical graph-based ranking are the two kinds of potentially powerful and leading unsupervised approaches in this area, devised to address the problem. To utilize the potential of these approaches and improve Key Concept identification, a comprehensive performance analysis of these approaches on datasets from different domains is needed. The objective of the study presented in this paper is to perform a comprehensive empirical analysis of selected frequency and topical graph-based algorithms for Key Concept extraction on three different datasets, to identify the major sources of error in these approaches. For experimental analysis, we have selected TF-IDF, KP-Miner and TopicRank. Three major sources of error, i.e., frequency errors, syntactical errors and semantical errors, and the factors that contribute to these errors are identified. Analysis of the results reveals that performance of the selected approaches is significantly degraded by these errors. These findings can help us develop an intelligent solution for Key Concept extraction in the future.
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Key Concept Identification: A Sentence Parse Tree-Based Technique for Candidate Feature Extraction From Unstructured Texts
IEEE Access, 2018Co-Authors: Muhammad Aman, Said Jadid Abdul Kadir, Abas Bin Md Said, Israr UllahAbstract:The effectiveness of automatic Key Concept or Keyphrase identification from unstructured text documents mainly depends on a comprehensive and meaningful list of candidate features extracted from the documents. However, the conventional techniques for candidate feature extraction limit the performance of Keyphrase identification algorithms and need improvement. The objective of this paper is to propose a novel parse tree-based approach for candidate feature extraction to overcome the shortcomings of the existing techniques. Our proposed technique is based on generating a parse tree for each sentence in the input text. Sentence parse trees are then cut into sub-trees to extract branches for candidate phrases (i.e., noun, verb, and so on). The sub-trees are combined using parts-of-speech tagging to generate the flat list of candidate phrases. Finally, filtering is performed using heuristic rules and redundant phrases are eliminated to generate final list of candidate features. Experimental analysis is conducted for validation of the proposed scheme using three manually annotated and publicly available data sets from different domains, i.e., Inspec, 500N-KPCrowed, and SemEval-2010. The proposed technique is fine-tuned to determine the optimal value for the parameter context window size and then it is compared with the existing conventional n-gram and noun-phrase-based techniques. The results show that the proposed technique outperforms the existing approaches and significant improvements of 13.51% and 30.67%, 12.86% and 5.48%, and 13.16% and 31.46% are achieved, in terms of precision, recall, and F-measure when compared with noun-phrasebased scheme and n-gram-based scheme, respectively. These results give us confidence to further validate the proposed technique by developing a Keyphrase extraction algorithm in the future.
Tatseng Chua - One of the best experts on this subject based on the ideXlab platform.
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Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval
IEEE Transactions on Knowledge and Data Engineering, 2016Co-Authors: Weinan Zhang, Zhaoyan Ming, Yu Zhang, Tatseng ChuaAbstract:In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, work well for long and complex queries, such as the questions. The main reasons are the verboseness in natural language queries and the word mismatch between the queries and the candidate questions in the CQA archive during retrieval. To address these two problems, existing solutions try to refine the search queries by distinguishing the Key Concepts in the queries and expanding the queries with relevant content. However, using the existing query refinement approaches can only identify the Key and non-Key Concepts, while the differences between the Key Concepts are overlooked. Moreover, the existing query expansion approaches, not only overlook the weights of Key Concepts in the queries, but also fail to consider Concept level expansion for them. In this paper, we explore a Key Concept identification approach for query refinement and a pivot language translation based approach to explore Key Concept paraphrasing. We further propose a new question retrieval model which can seamlessly integrate the Key Concepts and their paraphrases. The experimental results demonstrate that the integrated retrieval model significantly outperforms the state-of-the-art models in question retrieval.
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exploring Key Concept paraphrasing based on pivot language translation for question retrieval
National Conference on Artificial Intelligence, 2015Co-Authors: Weinan Zhang, Zhaoyan Ming, Yu Zhang, Tatseng ChuaAbstract:Question retrieval in current community-based question answering (CQA) services does not, in general, work well for long and complex queries. One of the main difficulties lies in the word mismatch between queries and candidate questions. Existing solutions try to expand the queries at word level, but they usually fail to consider Concept level enrichment. In this paper, we explore a pivot language translation based approach to derive the paraphrases of Key Concepts. We further propose a unified question retrieval model which integrates the Key Concepts and their paraphrases for the query question. Experimental results demonstrate that the paraphrase enhanced retrieval model significantly outperforms the state-of-the-art models in question retrieval.
Peter W Sauer - One of the best experts on this subject based on the ideXlab platform.
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Error analysis in electric power system available transfer capability computation
Decision Support Systems, 1999Co-Authors: Peter W Sauer, Santiago GrijalvaAbstract:A Key Concept in the restructuring of the electric power industry is the ability to accurately and rapidly quantify the capabilities of the transmission system. Transmission transfer capability is limited by a number of different mechanisms, including thermal, voltage, and stability constraints. This paper discusses the available transfer capability (ATC) definitions and determination guidelines approved by the North American Electric Reliability Council (NERC) and presents several Concepts for dealing with the potential errors and technical challenges of computation. © 1999 Elsevier Science B.V. All rights reserved.
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Technical challenges of computing available transfer capability (ATC) in electric power systems
Proceedings of the Hawaii International Conference on System Sciences, 1997Co-Authors: Peter W SauerAbstract:A Key Concept in the restructuring of the electric power industry is the ability to accurately and rapidly quantify the capabilities of the transmission system. Transmission transfer capability is limited by a number of different mechanisms, including thermal, voltage, and stability constraints. This paper discusses the ATC definitions and determination guidelines approved by the North American Electric Reliability Council (NERC) and presents several Concepts for dealing with the technical challenges of computation
