The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

Jakub Sido - One of the best experts on this subject based on the ideXlab platform.

  • uwb at semeval 2020 task 1 lexical semantic change detection
    International Conference on Computational Linguistics, 2020
    Co-Authors: Ondrej Prazak, Pavel Priban, Stephen Taylor, Jakub Sido
    Abstract:

    In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic Vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the Transformed Vector for the target word from the earlier corpus and the Vector for the target word in the later corpus.

  • uwb at semeval 2020 task 1 lexical semantic change detection
    arXiv: Computation and Language, 2020
    Co-Authors: Ondrej Prazak, Stephen Taylor, Pavel Přibaň, Jakub Sido
    Abstract:

    In this paper, we describe our method for the detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary change detection, and $4^{th}$ in Sub-task 2: ranked change detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic Vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and measuring the cosines between the Transformed Vector for the target word from the earlier corpus and the Vector for the target word in the later corpus.

Ondrej Prazak - One of the best experts on this subject based on the ideXlab platform.

  • uwb at semeval 2020 task 1 lexical semantic change detection
    International Conference on Computational Linguistics, 2020
    Co-Authors: Ondrej Prazak, Pavel Priban, Stephen Taylor, Jakub Sido
    Abstract:

    In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic Vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the Transformed Vector for the target word from the earlier corpus and the Vector for the target word in the later corpus.

  • uwb at semeval 2020 task 1 lexical semantic change detection
    arXiv: Computation and Language, 2020
    Co-Authors: Ondrej Prazak, Stephen Taylor, Pavel Přibaň, Jakub Sido
    Abstract:

    In this paper, we describe our method for the detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary change detection, and $4^{th}$ in Sub-task 2: ranked change detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic Vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and measuring the cosines between the Transformed Vector for the target word from the earlier corpus and the Vector for the target word in the later corpus.

Stephen Taylor - One of the best experts on this subject based on the ideXlab platform.

  • uwb at semeval 2020 task 1 lexical semantic change detection
    International Conference on Computational Linguistics, 2020
    Co-Authors: Ondrej Prazak, Pavel Priban, Stephen Taylor, Jakub Sido
    Abstract:

    In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic Vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the Transformed Vector for the target word from the earlier corpus and the Vector for the target word in the later corpus.

  • uwb at semeval 2020 task 1 lexical semantic change detection
    arXiv: Computation and Language, 2020
    Co-Authors: Ondrej Prazak, Stephen Taylor, Pavel Přibaň, Jakub Sido
    Abstract:

    In this paper, we describe our method for the detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary change detection, and $4^{th}$ in Sub-task 2: ranked change detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic Vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and measuring the cosines between the Transformed Vector for the target word from the earlier corpus and the Vector for the target word in the later corpus.

Pavel Priban - One of the best experts on this subject based on the ideXlab platform.

  • uwb at semeval 2020 task 1 lexical semantic change detection
    International Conference on Computational Linguistics, 2020
    Co-Authors: Ondrej Prazak, Pavel Priban, Stephen Taylor, Jakub Sido
    Abstract:

    In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic Vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the Transformed Vector for the target word from the earlier corpus and the Vector for the target word in the later corpus.

Georgios N. Kouziokas - One of the best experts on this subject based on the ideXlab platform.

  • A new W-SVM kernel combining PSO-neural network Transformed Vector and Bayesian optimized SVM in GDP forecasting
    Engineering Applications of Artificial Intelligence, 2020
    Co-Authors: Georgios N. Kouziokas
    Abstract:

    Abstract Considering that in the literature there is a very limited number of studies proposing new SVM kernels especially in regression problems, the scope of this research is to investigate the development of a novel Support Vector Machine Kernel. The proposed new W-SVM (Weighted-SVM) kernel was developed by applying a suitably Transformed weight Vector derived from particle swarm optimized neural networks in order to satisfy the kernel conditions of Mercer’s theorem and then incorporated to a Bayesian Optimized (BO) kernel for building the new proposed W-SVM kernel. The proposed SVM kernel was applied in Gross Domestic Product growth forecasting. The new kernel has led to significantly improved forecasting results compared to all the other conventional ANN, SVM, and optimized BO-SVM, PSO-ANN machine learning models.