2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)最新文献_第3页

Exact-match Based Wikipedia-WordNet Integration 基于精确匹配的维基百科- wordnet集成

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778336

Tomasz Boinski, J. Szymański, Tymoteusz Cejrowski

引用次数: 0

BLISS-A CPSS-like Application for Lifelong Learning bliss -一个类似cpss的终身学习应用程序

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778363

J. Todorov, I. Krasteva, Vanya Ivanova, E. Doychev

引用次数: 3

Adversarial Nuclei Segmentation on H&E Stained Histopathology Images H&E染色组织病理学图像上的对抗性细胞核分割

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778369

O. Koyun, T. Yıldırım

引用次数: 9

CoMACAV: Cooperative MAC Protocol for Autonomous Vehicles CoMACAV:自动驾驶汽车的合作MAC协议

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778404

Muhammet Ali Karabulut, A. Shah, H. Ilhan

引用次数: 2

Comparison of CNN Tolerances to Intra Class Variety in Food Recognition CNN对食物识别类内变异的耐受性比较

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778355

M. Taskiran, N. Kahraman

引用次数: 6

The Analysis of Text Categorization Represented With Word Embeddings Using Homogeneous Classifiers 基于同构分类器的词嵌入文本分类分析

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778329

Z. H. Kilimci, S. Akyokuş

{"title":"The Analysis of Text Categorization Represented With Word Embeddings Using Homogeneous Classifiers","authors":"Z. H. Kilimci, S. Akyokuş","doi":"10.1109/INISTA.2019.8778329","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778329","url":null,"abstract":"Text data mining is the process of extracting and analyzing valuable information from text. A text data mining process generally consists of lexical and syntax analysis of input text data, the removal of non-informative linguistic features and the representation of text data in appropriate formats, and eventually analysis and interpretation of the output. Text categorization, text clustering, sentiment analysis, and document summarization are some of the important applications of text mining. In this study, we analyze and compare the performance of text categorization by using different single classifiers, an ensemble of classifiers, a neural probabilistic representation model called word2vec on English texts. The neural probabilistic based model namely, word2vec, enables the representation of terms of a text in a new and smaller space with word embedding vectors instead of using original terms. After the representation of text data in new feature space, the training procedure is carried out with the well-known classification algorithms, namely multivariate Bernoulli naïve Bayes, support vector machines and decision trees and an ensemble algorithm such as bagging, random subspace and random forest. A wide range of comparative experiments are conducted on English texts to analyze the effectiveness of word embeddings on text classification. The evaluation of experimental results demonstrates that an ensemble of algorithms models with word embeddings performs better than other classification algorithms that uses traditional methods on English texts.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123774695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Hybrid Translation System from Turkish Spoken Language to Turkish Sign Language 从土耳其口语到土耳其手语的混合翻译系统

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778347

Dilek Kayahan, Tunga Güngör

引用次数: 15

Effects of Positivization on the Paragraph Vector Model 实证化对段落向量模型的影响

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778304

Aydın Gerek, Mehmet Can Yüney, Erencan Erkaya, M. Ganiz

{"title":"Effects of Positivization on the Paragraph Vector Model","authors":"Aydın Gerek, Mehmet Can Yüney, Erencan Erkaya, M. Ganiz","doi":"10.1109/INISTA.2019.8778304","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778304","url":null,"abstract":"Natural language processing (NLP) is an important field of Artificial Intelligence. One of the fundamental problems in NLP is to create vector (distributed) representations of words so that vectors of words that have similar meaning lie closer in space. One of the most popular algorithms for creating these representations are word embedding models such as word2vec and fastText. Similarly the paragraph vector model (doc2vec) is used to create distributed representations of documents while simultaneously creating distributed representations for the words in these documents. These models create a dense, and low dimensional (usually in the low hundreds) vector representations which may include negative values. In this study we focus on these negative values and introduce a family of regularization methods in which document, word and/or context vectors of the paragraph vector model are forced to have only positive components. We measure its effects on several tasks; text classification, semantic similarity, and analogy tasks. Although positivization greatly increases the sparsity of the word embeddings, and should be expected to result in a loss of information, our results show that there is almost no reduction in the performance of the regularized embeddings in these tasks. We also observe an increase in the classification accuracy in one case. We foresee that these approaches can be beneficial in machine learning systems which require non-negative vectors.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132550883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Fractional Order Memristance Simulator Circuit Design 一种新的分数阶忆阻模拟器电路设计

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778386

Zehra Gulru Cam Taskiran, M. Taskiran

引用次数: 1

Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems 基于agent人工免疫系统的机器学习启发的基于主机的入侵检测系统

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778269

C. Ou

引用次数: 11