The Experts below are selected from a list of 49176 Experts worldwide ranked by ideXlab platform
Cynthia Brandt - One of the best experts on this subject based on the ideXlab platform.
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knowledge based biomedical word sense disambiguation an evaluation and application to clinical Document Classification
Journal of the American Medical Informatics Association, 2013Co-Authors: Vijay Garla, Cynthia BrandtAbstract:Background Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text Classification. Methods We evaluated our system on biomedical WSD datasets and determined the contribution of our WSD system to clinical Document Classification on the 2007 Computational Medicine Challenge corpus. Results Our system compared favorably with other knowledge-based methods. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts. Conclusions We developed a WSD system that achieves high disambiguation accuracy on standard biomedical WSD datasets and showed that our WSD system improves clinical Document Classification. Data sharing We integrated our WSD system with MetaMap and the clinical Text Analysis and Knowledge Extraction System, two popular biomedical natural language processing systems. All codes required to reproduce our results and all tools developed as part of this study are released as open source, available under .
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knowledge based biomedical word sense disambiguation an evaluation and application to clinical Document Classification
IEEE International Conference on Healthcare Informatics Imaging and Systems Biology, 2012Co-Authors: Vijay Garla, Cynthia BrandtAbstract:Motivation: Word Sense Disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text processing tasks. In this study, we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS), and we evaluated the contribution of WSD to clinical text Classification. Results: We evaluated our system on biomedical WSD datasets; our system compares favorably to other knowledge-based methods. We evaluated the contribution of our WSD system to clinical Document Classification on the 2007 Computational Medicine Challenge corpus. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts. Availability: We integrated our WSD system with MetaMap and cTAKES, two popular biomedical natural language processing systems. We released all code required to reproduce our results and all tools developed as part of this study as open source, available under [URL removed for blinded submission].
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The Yale cTAKES extensions for Document Classification: architecture and application
Journal of the American Medical Informatics Association, 2011Co-Authors: Vijay Garla, Zachariah Dorey-stein, Farah Kidwai, Matthew Scotch, Julie A. Womack, Amy C. Justice, Cynthia BrandtAbstract:Background Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical Document Classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for Document Classification pose technical challenges. Methods The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and the development of both rule and machine-learning based Document classifiers. The authors describe and evaluate their system, the Yale cTAKES Extensions (YTEX), on the Classification of radiology reports that contain findings suggestive of hepatic decompensation. Results and discussion The F1-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at .
Vijay Garla - One of the best experts on this subject based on the ideXlab platform.
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knowledge based biomedical word sense disambiguation an evaluation and application to clinical Document Classification
Journal of the American Medical Informatics Association, 2013Co-Authors: Vijay Garla, Cynthia BrandtAbstract:Background Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text Classification. Methods We evaluated our system on biomedical WSD datasets and determined the contribution of our WSD system to clinical Document Classification on the 2007 Computational Medicine Challenge corpus. Results Our system compared favorably with other knowledge-based methods. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts. Conclusions We developed a WSD system that achieves high disambiguation accuracy on standard biomedical WSD datasets and showed that our WSD system improves clinical Document Classification. Data sharing We integrated our WSD system with MetaMap and the clinical Text Analysis and Knowledge Extraction System, two popular biomedical natural language processing systems. All codes required to reproduce our results and all tools developed as part of this study are released as open source, available under .
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knowledge based biomedical word sense disambiguation an evaluation and application to clinical Document Classification
IEEE International Conference on Healthcare Informatics Imaging and Systems Biology, 2012Co-Authors: Vijay Garla, Cynthia BrandtAbstract:Motivation: Word Sense Disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text processing tasks. In this study, we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS), and we evaluated the contribution of WSD to clinical text Classification. Results: We evaluated our system on biomedical WSD datasets; our system compares favorably to other knowledge-based methods. We evaluated the contribution of our WSD system to clinical Document Classification on the 2007 Computational Medicine Challenge corpus. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts. Availability: We integrated our WSD system with MetaMap and cTAKES, two popular biomedical natural language processing systems. We released all code required to reproduce our results and all tools developed as part of this study as open source, available under [URL removed for blinded submission].
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The Yale cTAKES extensions for Document Classification: architecture and application
Journal of the American Medical Informatics Association, 2011Co-Authors: Vijay Garla, Zachariah Dorey-stein, Farah Kidwai, Matthew Scotch, Julie A. Womack, Amy C. Justice, Cynthia BrandtAbstract:Background Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical Document Classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for Document Classification pose technical challenges. Methods The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and the development of both rule and machine-learning based Document classifiers. The authors describe and evaluate their system, the Yale cTAKES Extensions (YTEX), on the Classification of radiology reports that contain findings suggestive of hepatic decompensation. Results and discussion The F1-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at .
Jiaheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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bag of concepts representation for Document Classification based on automatic knowledge acquisition from probabilistic knowledge base
Knowledge Based Systems, 2020Co-Authors: Pengfei Li, Yuecong Xu, Qi Li, Jiaheng ZhangAbstract:Abstract Text representation, a crucial step for text mining and natural language processing, concerns about transforming unstructured textual data into structured numerical vectors to support various machine learning and data mining algorithms. For Document Classification, one classical and commonly adopted text representation method is Bag-of-Words (BoW) model. BoW represents Document as a fixed-length vector of terms, where each term dimension is a numerical value such as term frequency or tf-idf weight. However, BoW simply looks at surface form of words. It ignores the semantic, conceptual and contextual information of texts, and also suffers from high dimensionality and sparsity issues. To address the aforementioned issues, we propose a novel Document representation scheme called Bag-of-Concepts (BoC), which automatically acquires useful conceptual knowledge from external knowledge base, then conceptualizes words and phrases in the Document into higher level semantics (i.e. concepts) in a probabilistic manner, and eventually represents a Document as a distributed vector in the learned concept space. By utilizing background knowledge from knowledge base, BoC representation is able to provide more semantic and conceptual information of texts, as well as better interpretability for human understanding. We also propose Bag-of-Concept-Clusters (BoCCl) model which clusters semantically similar concepts together and performs entity sense disambiguation to further improve BoC representation. In addition, we combine BoCCl and BoW representations using an attention mechanism to effectively utilize both concept-level and word-level information and achieve optimal performance for Document Classification.
Pengfei Li - One of the best experts on this subject based on the ideXlab platform.
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bag of concepts representation for Document Classification based on automatic knowledge acquisition from probabilistic knowledge base
Knowledge Based Systems, 2020Co-Authors: Pengfei Li, Yuecong Xu, Qi Li, Jiaheng ZhangAbstract:Abstract Text representation, a crucial step for text mining and natural language processing, concerns about transforming unstructured textual data into structured numerical vectors to support various machine learning and data mining algorithms. For Document Classification, one classical and commonly adopted text representation method is Bag-of-Words (BoW) model. BoW represents Document as a fixed-length vector of terms, where each term dimension is a numerical value such as term frequency or tf-idf weight. However, BoW simply looks at surface form of words. It ignores the semantic, conceptual and contextual information of texts, and also suffers from high dimensionality and sparsity issues. To address the aforementioned issues, we propose a novel Document representation scheme called Bag-of-Concepts (BoC), which automatically acquires useful conceptual knowledge from external knowledge base, then conceptualizes words and phrases in the Document into higher level semantics (i.e. concepts) in a probabilistic manner, and eventually represents a Document as a distributed vector in the learned concept space. By utilizing background knowledge from knowledge base, BoC representation is able to provide more semantic and conceptual information of texts, as well as better interpretability for human understanding. We also propose Bag-of-Concept-Clusters (BoCCl) model which clusters semantically similar concepts together and performs entity sense disambiguation to further improve BoC representation. In addition, we combine BoCCl and BoW representations using an attention mechanism to effectively utilize both concept-level and word-level information and achieve optimal performance for Document Classification.
Tamjidul Hoque - One of the best experts on this subject based on the ideXlab platform.
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multiclass patent Document Classification
Artificial Intelligence Review, 2017Co-Authors: Chaitanya Anne, Avdesh Mishra, Tamjidul HoqueAbstract:Text Classification is used in information extraction and retrieval from a given text, and text Classification has been considered as an important step to manage a vast number of records given in digital form that is far-reaching and expanding. This article addresses patent Document Classification problem into fifteen different categories or classes, where some classes overlap with each other for practical reasons. For the development of the Classification model using machine learning techniques, useful features have been extracted from the given Documents. The features are used to classify patent Document as well as to generate useful tag-words. The overall objective of this work is to systematize NASA’s patent management, by developing a set of automated tools that can assist NASA to manage and market its portfolio of intellectual properties (IP), and to enable easier discovery of relevant IP by users. We have identified an array of methods that can be applied such as k -Nearest Neighbors (kNN), two variations of the Support Vector Machine (SVM) algorithms, and two tree based Classification algorithms: Random Forest and J48. The major research steps in this paper consist of filtering techniques for variable selection, information gain and feature correlation analysis, and training and testing potential models using effective classifiers. Further, the obstacles associated with the imbalanced data were mitigated by adding pseudo-synthetic data wherever appropriate, which resulted in a superior SVM classifier based model.