The Experts below are selected from a list of 27378 Experts worldwide ranked by ideXlab platform
Piek Vossen - One of the best experts on this subject based on the ideXlab platform.
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meaning_space at semeval 2018 task 10 combining explicitly Encoded Knowledge with information extracted from word embeddings
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: P J M Sommerauer, Antske Fokkens, Piek VossenAbstract:This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploiting explicitly Encoded Knowledge about concepts in WordNet and information Encoded in distributional semantic vectors. Rather than aiming for high performance, we explore which kind of semantic Knowledge is best captured by different methods. The results indicate that WordNet glosses on different levels of the hierarchy capture many attributes relevant for this task. In combination with exploiting word embedding similarities, this source of information yielded our best results. Our best performing system ranked 5th out of 13 final ranks. Our analysis yields insights into the different kinds of attributes represented by different sources of Knowledge.
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SemEval@NAACL-HLT - Meaning_space at SemEval-2018 Task 10: Combining explicitly Encoded Knowledge with information extracted from word embeddings
Proceedings of The 12th International Workshop on Semantic Evaluation, 2018Co-Authors: P J M Sommerauer, Antske Fokkens, Piek VossenAbstract:This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploiting explicitly Encoded Knowledge about concepts in WordNet and information Encoded in distributional semantic vectors. Rather than aiming for high performance, we explore which kind of semantic Knowledge is best captured by different methods. The results indicate that WordNet glosses on different levels of the hierarchy capture many attributes relevant for this task. In combination with exploiting word embedding similarities, this source of information yielded our best results. Our best performing system ranked 5th out of 13 final ranks. Our analysis yields insights into the different kinds of attributes represented by different sources of Knowledge.
P J M Sommerauer - One of the best experts on this subject based on the ideXlab platform.
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meaning_space at semeval 2018 task 10 combining explicitly Encoded Knowledge with information extracted from word embeddings
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: P J M Sommerauer, Antske Fokkens, Piek VossenAbstract:This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploiting explicitly Encoded Knowledge about concepts in WordNet and information Encoded in distributional semantic vectors. Rather than aiming for high performance, we explore which kind of semantic Knowledge is best captured by different methods. The results indicate that WordNet glosses on different levels of the hierarchy capture many attributes relevant for this task. In combination with exploiting word embedding similarities, this source of information yielded our best results. Our best performing system ranked 5th out of 13 final ranks. Our analysis yields insights into the different kinds of attributes represented by different sources of Knowledge.
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SemEval@NAACL-HLT - Meaning_space at SemEval-2018 Task 10: Combining explicitly Encoded Knowledge with information extracted from word embeddings
Proceedings of The 12th International Workshop on Semantic Evaluation, 2018Co-Authors: P J M Sommerauer, Antske Fokkens, Piek VossenAbstract:This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploiting explicitly Encoded Knowledge about concepts in WordNet and information Encoded in distributional semantic vectors. Rather than aiming for high performance, we explore which kind of semantic Knowledge is best captured by different methods. The results indicate that WordNet glosses on different levels of the hierarchy capture many attributes relevant for this task. In combination with exploiting word embedding similarities, this source of information yielded our best results. Our best performing system ranked 5th out of 13 final ranks. Our analysis yields insights into the different kinds of attributes represented by different sources of Knowledge.
Antske Fokkens - One of the best experts on this subject based on the ideXlab platform.
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meaning_space at semeval 2018 task 10 combining explicitly Encoded Knowledge with information extracted from word embeddings
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: P J M Sommerauer, Antske Fokkens, Piek VossenAbstract:This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploiting explicitly Encoded Knowledge about concepts in WordNet and information Encoded in distributional semantic vectors. Rather than aiming for high performance, we explore which kind of semantic Knowledge is best captured by different methods. The results indicate that WordNet glosses on different levels of the hierarchy capture many attributes relevant for this task. In combination with exploiting word embedding similarities, this source of information yielded our best results. Our best performing system ranked 5th out of 13 final ranks. Our analysis yields insights into the different kinds of attributes represented by different sources of Knowledge.
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SemEval@NAACL-HLT - Meaning_space at SemEval-2018 Task 10: Combining explicitly Encoded Knowledge with information extracted from word embeddings
Proceedings of The 12th International Workshop on Semantic Evaluation, 2018Co-Authors: P J M Sommerauer, Antske Fokkens, Piek VossenAbstract:This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploiting explicitly Encoded Knowledge about concepts in WordNet and information Encoded in distributional semantic vectors. Rather than aiming for high performance, we explore which kind of semantic Knowledge is best captured by different methods. The results indicate that WordNet glosses on different levels of the hierarchy capture many attributes relevant for this task. In combination with exploiting word embedding similarities, this source of information yielded our best results. Our best performing system ranked 5th out of 13 final ranks. Our analysis yields insights into the different kinds of attributes represented by different sources of Knowledge.
Jeremiah G. Tilles - One of the best experts on this subject based on the ideXlab platform.
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Knowledge-Based Avoidance of Drug-Resistant HIV Mutants
Ai Magazine, 1999Co-Authors: Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-rubin, Michael J. Pazzani, Darryl M. See, Jeremiah G. TillesAbstract:We describe an AI system (CTSHIV) that connects the scientific AIDS literature describing specific human immunodeficiency virus (HIV) drug resistances directly to the customized treatment strategy of a specific HIV patient. Rules in the CTSHIV Knowledge base encode Knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance to the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment to infer current drug resistance. A rule-directed search through mutation sequence space identifies nearby drug-resistant mutant strains that might arise. The possible combination drug-treatment regimens currently approved by the U.S. Food and Drug Administration are considered and ranked by their estimated ability to avoid identified current and nearby drug-resistant mutants. The highest-ranked treatments are recommended to the attending physician. The result is more precise treatment of individual HIV patients and a decreased tendency to select for drug-resistant genes in the global HIV gene pool. Initial results from a small human clinical trial are encouraging, and further clinical trials are planned. From an AI viewpoint, the case study demonstrates the extensibility of Knowledge-based systems because it illustrates how existing Encoded Knowledge can be used to support new Knowledge-based applications that were unanticipated when the original Knowledge was Encoded.
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Knowledge-based avoidance of drug-resistant HIV mutants : Special articles on innovative applications
Ai Magazine, 1999Co-Authors: Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-rubin, Michael J. Pazzani, Darryl See, Jeremiah G. TillesAbstract:We describe an AI system (CTSHIV) that connects the scientific AIDS literature describing specific human immunodeficiency virus (HIV) drug resistances directly to the customized treatment strategy of a specific HIV patient. Rules in the CTSHIV Knowledge base encode Knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance to the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment to infer current drug resistance. A rule-directed search through mutation sequence space identifies nearby drug-resistant mutant strains that might arise. The possible combination drug-treatment regimens currently approved by the U.S. Food and Drug Administration are considered and ranked by their estimated ability to avoid identified current and nearby drug-resistant mutants. The highest-ranked treatments are recommended to the attending physician. The result is more precise treatment of individual HIV patients and a decreased tendency to select for drug-resistant genes in the global HIV gene pool. Initial results from a small human clinical trial are encouraging, and further clinical trials are planned. From an Al viewpoint, the case study demonstrates the extensibility of Knowledge-based systems because it illustrates how existing Encoded Knowledge can be used to support new Knowledge-based applications that were unanticipated when the original Knowledge was Encoded.
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AAAI/IAAI - Knowledge-based avoidance of drug-resistant HIV mutants
1998Co-Authors: Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-rubin, Michael J. Pazzani, Darryl M. See, Jeremiah G. TillesAbstract:We describe an artificial intelligence (AI) system (CTSHIV) that connects the scientific AIDS literature describing specific HIV drug resistances directly to the Customized Treatment Strategy of a specific HIV patient. Rules in the CTSHIV Knowledge base encode Knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance in the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment in order to infer current drug resistance. A search through mutation sequence space identifies nearby drug resistant mutant strains that might arise. The possible drug treatment regimens currently approved by the US Food and Drug Administration (FDA) are considered and ranked by their estimated ability to avoid identified current and nearby drug resistant mutants. The highest-ranked treatments are recommended to the attending physician. The result is more precise treatment of individual HIV patients, and a decreased tendency to select for drug resistant genes in the global HIV gene pool. The application is currently in use in human clinical trials on HIV patients. Initial results from a small clinical trial are encouraging and further clinical trials are planned. From an AI viewpoint the case study demonstrates the extensibility of Knowledge-based systems because it illustrates how existing Encoded Knowledge can be used to support new applications that were unanticipated when the original Knowledge was Encoded.
Gitte Graetzer - One of the best experts on this subject based on the ideXlab platform.
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multinational organizations as rule following bureaucracies the example of catholic orders
Journal of International Management, 2014Co-Authors: Katja Rost, Gitte GraetzerAbstract:Within the literature, organizational rules are mostly taken for granted even though the reduction of office management into rules and the provision of their blueprints may be the main enabler for the management of organizations that conduct operations in multiple countries. Using the example of Catholic Orders and their monasteries, we analyze whether rule-following bureaucracy contributes to the management of multinational organizations (MNOs). The introduction of organizational rules and the redefinition of labor within these rules produced early medieval monasteries that were the most efficient organizations of this time, allowing them to spread rapidly throughout the world. Our main hypothesis is that governance by rules is a superior governance mechanism for MNOs. MNOs with more bureaucratic rules have accumulated a richer pool of Encoded Knowledge to deal with heterogeneous problems and, thus, are better forearmed to deal with complexity. The empirical findings mostly support this assumption. Bureaucratic governance may be thus an important but neglected topic for the management of modern MNOs.
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Multinational Organizations as Rule-following Bureaucracies — The Example of Catholic Orders
Journal of International Management, 2014Co-Authors: Katja Rost, Gitte GraetzerAbstract:Within the literature, organizational rules are mostly taken for granted even though the reduction of office management into rules and the provision of their blueprints may be the main enabler for the management of organizations that conduct operations in multiple countries. Using the example of Catholic Orders and their monasteries, we analyze whether rule-following bureaucracy contributes to the management of multinational organizations (MNOs). The introduction of organizational rules and the redefinition of labor within these rules produced early medieval monasteries that were the most efficient organizations of this time, allowing them to spread rapidly throughout the world. Our main hypothesis is that governance by rules is a superior governance mechanism for MNOs. MNOs with more bureaucratic rules have accumulated a richer pool of Encoded Knowledge to deal with heterogeneous problems and, thus, are better forearmed to deal with complexity. The empirical findings mostly support this assumption. Bureaucratic governance may be thus an important but neglected topic for the management of modern MNOs.