scholarly journals A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languagescation of inertial and magnetic sensors in online pattern recognition problems

2019 ◽  
Author(s):  
András Dobó
Keyword(s):  
Language Processing ◽  
Similarity Measures ◽  
Magnetic Sensors ◽  
Feature Vectors ◽  
Reduction Techniques ◽  
Novel Variants ◽  
Semantic Models ◽  
Dimensionality Reduction Techniques ◽  
The Creation ◽  
Distributional Semantic Models

Measuring the semantic similarity and relatedness of words is important for many natural language processing tasks. Although distributional semantic models designed for this task have many different parameters, such as vector similarity measures, weighting schemes and dimensionality reduction techniques, there is no truly comprehensive study simultaneously evaluating these parameters while also analysing the differences in the findings for multiple languages. We would like to address this gap with our systematic study by searching for the best configuration in the creation and comparison of feature vectors in distributional semantic models for English, Spanish and Hungarian separately, and then comparing our findings across these languages. During our extensive analysis we test a large number of possible settings for all parameters, with more than a thousand novel variants in case of some of them. As a result of this we were able to find such configurations that significantly outperform conventional configurations and achieve state-of-the-art results.

Battelle Barricade—Microelectronic Device Authentication and Counterfeit Detection Utilizing Power Analysis

2017 ◽  
Author(s):  
Katie Liszewski ◽  
Thomas Bergman
Keyword(s):  
Power Analysis ◽  
Principal Component ◽  
Production Test ◽  
Test Fixture ◽  
Automated Test Generation ◽  
Feature Vectors ◽  
Reduction Techniques ◽  
Resultant Data ◽  
Test House ◽  
Dimensionality Reduction Techniques

Abstract Battelle has developed a technology to nondestructively classify electronic components as authentic or counterfeit. The technology uses a method that creates feature vectors for each class of devices using a reconfigurable side channel power analysis test fixture. This test fixture monitors the power fluctuations of the device either via connection to a power or a ground pin while test signals are sent to the device. Power waveforms are processed, undergo dimensionality reduction techniques, and the resultant data is plotted in Principal Component Analysis (PCA) space to reveal information related to the authenticity of the device under test. To scale this technology to the full catalog of parts available to a production test house, unique tools have been created that provide automated test generation and scoring of feature vectors.

Dimensionality Reduction Techniques for Text Mining

2017 ◽  
pp. 49-72
Author(s):  
Neethu Akkarapatty ◽  
Anjaly Muralidharan ◽  
Nisha S. Raj ◽  
Vinod P.
Keyword(s):  
Social Media ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Misclassification Rate ◽  
Time Data ◽  
Chi Square ◽  
Reduction Techniques ◽  
Real Time Data ◽  
Dimensionality Reduction Techniques

Sentiment analysis is an emerging field, concerned with the analysis and understanding of human emotions from sentences. Sentiment analysis is the process used to determine the attitude/opinion/emotions expressed by a person about a specific topic based on natural language processing. Proliferation of social media such as blogs, Twitter, Facebook and Linkedin has fuelled interest in sentiment analysis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassification rate is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains.

scholarly journals Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models

2020 ◽  
Vol 8 ◽  
pp. 231-246
Author(s):  
Vesna G. Djokic ◽  
Jean Maillard ◽  
Luana Bulat ◽  
Ekaterina Shutova
Keyword(s):  
Language Processing ◽  
Sentence Comprehension ◽  
Brain Activity ◽  
Activity Patterns ◽  
Word Embeddings ◽  
Semantic Models ◽  
Distributional Semantic Models ◽  
Metaphor Processing ◽  
Meaning Representation ◽  
The Brain

Recent years have seen a growing interest within the natural language processing (NLP) community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to decode brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor processing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing support for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension.

Detecting linguistic idiosyncratic interests in autism using distributional semantic models

2014 ◽  
Author(s):  
Masoud Rouhizadeh ◽  
Emily Prud'hommeaux ◽  
Jan van Santen ◽  
Richard Sproat
Keyword(s):  
Semantic Models ◽  
Distributional Semantic Models

Dimensionality reduction techniques to analyze heating systems in buildings

2015 ◽  
Vol 294 ◽  
pp. 553-564 ◽  
Author(s):  
Manuel Domínguez ◽  
Serafín Alonso ◽  
Antonio Morán ◽  
Miguel A. Prada ◽  
Juan J. Fuertes
Keyword(s):  
Dimensionality Reduction ◽  
Heating Systems ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques
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