The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Sudha Morwal - One of the best experts on this subject based on the ideXlab platform.
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Named Entity Recognition using Hidden Markov Model (HMM)
International Journal on Natural Language Computing, 2012Co-Authors: Sudha MorwalAbstract:Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the branch of artificial intelligence. It has many applications mainly in machine translation, text to speech synthesis, natural language understanding, Information Extraction, Information retrieval, question answering etc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM Model for building NER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific.
Zhong Yixin - One of the best experts on this subject based on the ideXlab platform.
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grammatical category disambiguation based on second order Hidden Markov Model
Systems Man and Cybernetics, 2001Co-Authors: Sun Jian, Wang Wei, Zhong YixinAbstract:Grammatical category disambiguation is an important field because of its basis in many applications, for example, parsing, machine translation, phrase recognition and so on. We put forward an improved second-order Hidden Markov Model that can capture more context information and develop one part-of-speech tagging system based on the Model. In order to reduce the number of Model parameters, word equivalence classes are used. The parameters of Model are achieved by the Baum-Welch algorithm using untagged text. Results show that it improves the accuracy of tagging.
Samer Mohammed - One of the best experts on this subject based on the ideXlab platform.
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automatic recognition of gait phases using a multiple regression Hidden Markov Model
IEEE-ASME Transactions on Mechatronics, 2018Co-Authors: Ferhat Attal, Yacine Amirat, Abdelghani Chibani, Samer MohammedAbstract:This paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a multiple-regression Hidden Markov Model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem, in which each phase, called a segment, is Modeled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labeling, which is a laborious time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall, and precision. Experiments conducted with five subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (Gaussian mixture Model, k-means, and Hidden Markov Model), while remaining competitive with respect to standard supervised classification methods (support vector machine, random forest, and k-nearest neighbor).
Tru H Cao - One of the best experts on this subject based on the ideXlab platform.
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a high order Hidden Markov Model for emotion detection from textual data
Pacific Rim Knowledge Acquisition Workshop, 2012Co-Authors: Tru H CaoAbstract:Emotion detection from text is still an appealing challenge. The approaches to this problem have been done firstly based on just emotional keywords, and then extended with utilizing also other generic terms. However, they still lack of some useful semantic features, such as a psychological characteristic that emotion is the result of a mental state sequence. Recent works focus on using rules to exploit those features, but have the coverage problem. In this paper, we propose a method using the high-order Hidden Markov Model whose states are automatically generated to Model the process that a mental state sequence causes an emotion. Our experiments on the ISEAR dataset have shown a better result in comparison with the state-of-the-art methods.
Branko G Celler - One of the best experts on this subject based on the ideXlab platform.
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automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model
Engineering Applications of Artificial Intelligence, 2016Co-Authors: Mitchell Yuwono, Yong Qin, Jing Zhou, Ying Guo, Branko G CellerAbstract:Ball bearings are integral elements in most rotating manufacturing machineries. While detecting defective bearing is relatively straightforward, discovering the source of defect requires advanced signal processing techniques. This paper proposes an automatic bearing defect diagnosis method based on Swarm Rapid Centroid Estimation (SRCE) and Hidden Markov Model (HMM). Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Liftering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98.02%, 96.03%, and 2.65%, respectively, on the bearing fault vibration data provided by Case School of Engineering of the Case Western Reserve University (CSE) which warrants further investigation. Graphical abstractDisplay Omitted HighlightsThis paper proposes an automatic fault diagnosis algorithm for rolling bearing defects.The classification algorithm was Hidden Markov Model optimized with swarm clustering.The features were defect harmonics extracted using wavelet kurtogram and cepstral liftering.The bearing fault vibration data was obtained from Case Western Reserve University.Sensitivity and specificity of 98.02% and 96.03% were achieved on the test data.