Deep learning practices have a great impact in many areas. Big data and significant hardware developments are the main reasons behind deep learning success. Recent advances in deep learning have led to significant improvements in text analysis and classification. Progress in the quality of word representation is an important factor among these improvements. In this study, we aimed to develop word2vec word representation, also called embedding, by automatically optimizing hyperparameters. Minimum word count, vector size, window size, negative sample, and iteration number were used to improve word embedding. We introduce two approaches for setting hyperparameters that are faster than grid search and random search. Word embeddings were created using documents of approximately 300 million words. We measured the quality of word embedding using a deep learning classification model on documents of 10 different classes. It was observed that the optimization of the values of hyperparameters alone increased classification success by 9%. In addition, we demonstrate the benefits of our approaches by comparing the semantic and syntactic relations between word embedding using default and optimized hyperparameters.
Text data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification.
Abstract-Web Service is a standardization effort to interoperate loosely-coupled applications. A Web Service interaction benefits and sometimes requires additive functionalities, called as handlers. They contribute to build rich, modular and efficient Web Services. However, the way of utilizing them is very crucial for the Web Service Architecture and its overall performance. Using distributed approach for the handler execution facilitates significantly to obtain full benefit from them. In this paper we describe an orchestration structure for the handlers to attain richer, more modular and efficient Web Services.
SUMMARY Over the last few decades, distributed systems have architecturally evolved. One recent evolutionary step is SOA. The SOA model is perfectly engendered in Web services, which provide software platforms for building applications as services. Web services utilize supportive capabilities such as security, reliability, and monitoring. These capabilities are typically provisioned as handlers, which incrementally add new features. Even though handlers are very important, the method of utilization is crucial for obtaining potential benefits. Every attempt to support a service with an additional handler increases the chance of an overwhelmingly crowded handler chain. Moreover, a handler may become a bottleneck because of its comparably higher processing time. In this paper, we present the Distributed Handler Architecture to provide an efficient, scalable, and modular architecture. The performance and scalability benchmarks show that the distributed and parallel handler executions are very promising for suitable handler configurations. The paper is concluded with remarks on the fundamentals of a promising computing environment for Web service handlers. Copyright © 2012 John Wiley & Sons, Ltd.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.