301,180 research outputs found
Special Issue in Artificial Intelligence
Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8].Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8]
A Breadth of NLP Applications
The Center for Natural Language Processing (CNLP) was founded in September 1999 in the School of Information Studies, the “Original Information School”, at Syracuse University. CNLP’s mission is to advance the development of human-like, language understanding software capabilities for government, commercial, and consumer applications. The Center conducts both basic and applied research, building on its recognized capabilities in Natural Language Processing. The Center’s seventeen employees are a mix of doctoral students in information science or computer engineering, software engineers, linguistic analysts, and research engineers
Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing
Large language models have revolutionized the field of artificial
intelligence and have been used in various applications. Among these models,
ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI,
it stands out as a powerful tool that has been widely adopted. ChatGPT has been
successfully applied in numerous areas, including chatbots, content generation,
language translation, personalized recommendations, and even medical diagnosis
and treatment. Its success in these applications can be attributed to its
ability to generate human-like responses, understand natural language, and
adapt to different contexts. Its versatility and accuracy make it a powerful
tool for natural language processing (NLP). However, there are also limitations
to ChatGPT, such as its tendency to produce biased responses and its potential
to perpetuate harmful language patterns. This article provides a comprehensive
overview of ChatGPT, its applications, advantages, and limitations.
Additionally, the paper emphasizes the importance of ethical considerations
when using this robust tool in real-world scenarios. Finally, This paper
contributes to ongoing discussions surrounding artificial intelligence and its
impact on vision and NLP domains by providing insights into prompt engineering
techniques
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Seen the villains: detecting social engineering attacks using case-based reasoning and deep learning
Social engineering attacks are frequent, well-known and easy-toapply attacks in the cyber domain. Historical evidence of such attacks has shown that the vast majority of malicious attempts against both physical and virtual IT systems were based or been initiated using social engineering methods. By identifying the importance of tackling efficiently cybersecurity threats and using the recent developments in machine learning, case-based reasoning and cybersecurity we propose and demonstrate a two-stage approach that detects social engineering attacks and is based on natural language processing, case-based reasoning and deep learning. Our approach can be applied in offline texts or real time environments and can identify whether a human, chatbot or offline conversation is a potential social engineering attack or not. Initially, the conversation text is parsed and checked for grammatical errors using natural language processing techniques and case-based reasoning and then deep learning is used to identify and isolate possible attacks. Our proposed method is being evaluated using both real and semi-synthetic conversation points with high accuracy results. Comparison benchmarks are also presented for comparisons in both datasets
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
Accurately typing entity mentions from text segments is a fundamental task
for various natural language processing applications. Many previous approaches
rely on massive human-annotated data to perform entity typing. Nevertheless,
collecting such data in highly specialized science and engineering domains
(e.g., software engineering and security) can be time-consuming and costly,
without mentioning the domain gaps between training and inference data if the
model needs to be applied to confidential datasets. In this paper, we study the
task of seed-guided fine-grained entity typing in science and engineering
domains, which takes the name and a few seed entities for each entity type as
the only supervision and aims to classify new entity mentions into both seen
and unseen types (i.e., those without seed entities). To solve this problem, we
propose SEType which first enriches the weak supervision by finding more
entities for each seen type from an unlabeled corpus using the contextualized
representations of pre-trained language models. It then matches the enriched
entities to unlabeled text to get pseudo-labeled samples and trains a textual
entailment model that can make inferences for both seen and unseen types.
Extensive experiments on two datasets covering four domains demonstrate the
effectiveness of SEType in comparison with various baselines.Comment: 9 pages; Accepted to AAAI 2024 (Code:
https://github.com/yuzhimanhua/SEType
A review of artificial intelligence technologies in mineral identification : classification and visualization
Artificial intelligence is a branch of computer science that attempts to understand the
essence of intelligence and produce a new intelligent machine capable of responding in a manner
similar to human intelligence. Research in this area includes robotics, language recognition, image
identification, natural language processing, and expert systems. In recent years, the availability of
large datasets, the development of effective algorithms, and access to powerful computers have led
to unprecedented success in artificial intelligence. This powerful tool has been used in numerous
scientific and engineering fields including mineral identification. This paper summarizes the methods
and techniques of artificial intelligence applied to intelligent mineral identification based on research,
classifying the methods and techniques as artificial neural networks, machine learning, and deep
learning. On this basis, visualization analysis is conducted for mineral identification of artificial
intelligence from field development paths, research hot spots, and keywords detection, respectively.
In the end, based on trend analysis and keyword analysis, we propose possible future research
directions for intelligent mineral identification.The National Natural Science Foundation of China.https://www.mdpi.com/journal/jsanElectrical, Electronic and Computer Engineerin
Mobile Phone Text Processing and Question-Answering
Mobile phone text messaging between mobile users and information services is a growing area of
Information Systems. Users may require the service to provide an answer to queries, or may, in wikistyle, want to contribute to the service by texting in some information within the service’s domain of discourse. Given the volume of such messaging it is essential to do the processing through an automated service. Further, in the case of repeated use of the service, the quality of such a response has the potential to benefit from a dynamic user profile that the service can build up from previous texts of the same user.
This project will investigate the potential for creating such intelligent mobile phone services and aims to produce a computational model to enable their efficient implementation. To make the project feasible, the scope of the automated service is considered to lie within a limited domain of, for example, information about entertainment within a specific town centre. The project will assume the existence of a model of objects within the domain of discourse, hence allowing the analysis of texts within the context of a user model and a domain model. Hence, the project will involve the subject areas of natural language processing, language engineering, machine learning, knowledge extraction, and ontological engineering
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