This book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Service-Oriented Computing: Agents, Semantics and engineering, SOCASE 2009, held in Budapest, Hungary, as an a...
详细信息
ISBN:
(数字)9783642107399
ISBN:
(纸本)9783642107382
This book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Service-Oriented Computing: Agents, Semantics and engineering, SOCASE 2009, held in Budapest, Hungary, as an associated event of AAMAS 2009, the main international conference on autonomous agents and multi-agent systems. The 10 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers address a range of topics at the intersection of service-oriented computing, semantic technology, and intelligent multiagent systems, such as: service description and discovery; planning, composition and negotiation; semantic processes and service agents; as well as applications.
The study of sentiment in Natural language Processing (NLP) is among the most successful research areas because of the availability of millions of user opinions online since the turn of the century. The economic, poli...
详细信息
The study of sentiment in Natural language Processing (NLP) is among the most successful research areas because of the availability of millions of user opinions online since the turn of the century. The economic, political, and medical fields are just some of the many that have benefited from studies of sentiment research. While numerous studies have examined more mainstream topics like consumer electronics, movies, and restaurants, relatively few have examined health and medical concerns. Considerable insight into where to direct efforts to improve public health might be gained by a study of how people feel about healthcare as a whole and of individual drug experiences in particular. When it comes to medicine, automatic analysis of online user evaluations paves the way for sifting through massive amounts of user feedback to find information regarding medications' efficacy and side effects that might be used to enhance pharmacovigilance programs. Simple rules-based methods have given way to more complex machine learning approaches like deep learning, which is developing as a technology for many natural language processing jobs. The opensource datasets have been analyzed with models that use word embeddings and term frequency-inverse document frequency (TF-IDF). A feature-enhanced text-inception model for sentiment classification was presented to work in tandem with this approach. The model first employed a cutting-edge text-inception module to glean useful shallow features from the text. K-MaxPooling was subsequently employed to reduce the dimensionality of its shallow and deep includes as well as enhance the generalization of characteristics, and a deep feature extraction module was formed using the bidirectional gated recurrent unit (Bi-GRU) and the capsule neural network to comprehend the text's semantic data. By combining traditional methods with cutting-edge artificial intelligence techniques, this hybrid approach can revolutionize public health initiatives, de
暂无评论