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2022 Doctoral Symposium on Natural Language Processing from the PLN.net Network 2022 (RED2018-102418-T), PLNnet-DS-2022

作者： Maestre, María Miró Department of Software and Computing Systems University of Alicante Alicante03690 Spain

Communicative intentions are one of the linguistic elements that usually determine the content of any message we want to express in our social interactions. With the purpose of contributing to the improvement of natural language processing systems, this thesis aims to create a communicative intention annotation scheme based on the taxonomy presented in the Speech Act Theory. In this way, language processing tools could consider communicative intentions as a starting point to help classify any message and its content depending first on the intention it reflects. To do so, the scheme will be created with the help of an already annotated corpus of Spanish tweets and subsequently evaluated by external annotators so that we can confirm the appropriateness and reliability of the tagged intentions before applying the scheme to an NLP system. Thus, it will be possible to check up to which point communicative intentions can improve the identification of the purpose of a message in an already created NLP system so that we can gain more linguistic information from any text automatically. © 2022 Copyright for this paper by its authors.

关键词： Linguistics

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HanaNLG: A Flexible Hybrid Approach for Natural Language Generation 20th

HanaNLG: A Flexible Hybrid Approach for Natural Language Ge...

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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Barros, Cristina Lloret, Elena Department of Software and Computing Systems University of Alicante Apdo. de Correos 99 AlicanteE-03080 Spain

ISBN: (纸本)9783031243394

Nowadays, with advances in digital technologies, interaction between computers and humans is essential. In this regard, the area of Natural Language Generation (NLG) can provide techniques capable of facilitating and improving this type of interaction. However, the existing approaches to this field are usually developed ad-hoc for specific tasks, purposes and domains, which hinders the advancement of flexible and adaptable multi-domain NLG systems. Under these premises, the objective of this paper is to present HanaNLG, a hybrid generic NLG approach, focused on the surface realisation stage. HanaNLG combines statistic and knowledge-based techniques and is able to generate text independently of the domain. In particular, this is done by exploiting language models in conjunction with semantic knowledge, providing flexibility to the whole generation process, thus, minimising the high cost associated with the development of common elements involved in NLG, such as grammars. Therefore, taking into account this joint perspective, our approach contributes to advancing the NLG field by providing greater flexibility when it comes to (i) producing text for different domains, and (ii) increasing the variety of vocabulary to appear in the generated text. In order to assess the effectiveness of HanaNLG, it was tested in two domains: (i) NLG for assistive technologies and, (ii) NLG for creating opinionated sentences. The positive results obtained (almost the 99% of the generated sentences for both domains are original and well constructed) show that our approach is capable of generating text for different domains. More importantly, the combination of language models with semantic knowledge enhances the quality of the generated text, thereby improving the results obtained compared to other methods that only rely on statistical methods. © 2023, Springer Nature Switzerland AG.

关键词： Knowledge based systems

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Fuzzy Logic Expert System for Analyzing Student Performance 7

Fuzzy Logic Expert System for Analyzing Student Performance

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Aruna, P. Lokhesh, R.J. Shajith Rooban, G.G. Shakthivel, K. Tarun, S.R. Coimbatore Institute of Technology Department of Computing Coimbatore India Coimbatore Institute of Technology MSc Software Systems Coimbatore India

ISBN: (纸本)9798350341485

Assessing the effectiveness of academics at various tiers has grown progressively difficult when employing traditional approaches that predominantly depend on numerical ratings to gauge their teaching and research accomplishments. With the indexing of academic performance in international databases using impact indices at various scales, the demand for advanced computer models has arisen. In this study, we suggest employing expert systems that utilize fuzzy logic to tackle the evaluation of educators, even when confronted with imprecise data and uncertainties. As a novel contribution, this research develops a fuzzy logic model and implements an algorithm using Python and Anaconda, which allows for fuzzy logic rule inference and comprehensive analysis. The implementation of the system is done using Python and leveraging Anaconda's data science environment, providing a seamless and efficient development process. The results of this pilot study involve testing and validating the proposed model through a user-friendly graphical interface, providing comprehensive insights based on minimum criteria along with additional explanations. This approach opens up new possibilities for more accurate assessment of academic performance, particularly when dealing with ambiguous data and uncertain conditions. © 2023 IEEE.

关键词： Fuzzy logic

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Approach to the Development of Ontology-Driven Language Toolkits Based on Metamodeling 17

Approach to the Development of Ontology-Driven Language Tool...

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17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Ermakov, Ivan Lanin, Viacheslav Lyadova, Lyudmila Proskuryakov, Kirill Perm State University Department of Computing Systems Software Perm Russia Hse University Department of Information Technologies in Business Perm Russia

ISBN: (纸本)9798350303568

The information systems are to be conforming to the requirements defined by domain experts. These requirements are formalized as models created with modeling tools. Applying these tools is complicated for domain experts. Domain specific modeling (DSM) with domain specific languages (DSL) reduces the semantic gap. However, the system development complication shifts to the creation of languages and tools for transforming models and code generation. An approach to automating DSL creation and facilitating code generation based on using multifaceted ontology is proposed. The generalized description of the multifaceted ontology is given. Tools of automating generation of new DSL metamodels based on mapping the corresponding domain ontology onto the metamodels of the selected base languages are described. Metamodels of the visual languages, grammars of the target text languages and transformation rules are also included into the ontology. The proposed approach is implemented as a research prototype of the language toolkits. Examples of metamodels and rules described in the ontology, as well as the results of their application are shown. The results of experiments confirmed practical significance of the approach to the ontology-driven language toolkits development. © 2023 IEEE.

关键词： Ontology

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A Systematic Literature Review of Cyber Security Monitoring in Maritime

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IEEE Access 2025年 13卷 85307-85329页

作者： Vaarandi, Risto Tsiopoulos, Leonidas Visky, Gabor Rehman, Muaan Ur Bahsi, Hayretdin Tallinn University of Technology Centre for Digital Forensics and Cyber Security Department of Software Science Tallinn19086 Estonia Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review papers have been published in the maritime field, none of the previous studies has focused on cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. The current article addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted. The first contribution of this article is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, our article presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this article is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. Based on our findings, we outline future research directions for cyber security monitoring in the maritime field. © 2013 IEEE.

关键词： Network intrusion

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From User Stories to Domain Models: Recommending Relationships between Entities

From User Stories to Domain Models: Recommending Relationshi...

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Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023

作者： Bragilovski, Maxim Dalpiaz, Fabiano Sturm, Arnon Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Department of Information and Computing Sciences Utrecht University Netherlands

User stories are a common notation for expressing requirements, especially in agile development projects. While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic view of the domain. As such, they can be complemented with domain models that not only help gain this comprehensive view, but also serve as a basis for model-driven development. We focus on the task of recommending relationships between entities in a domain model, assuming that these entities were previously extracted from a user story collection either manually or through an automated tool. We investigate whether an approach based on supervised machine learning can recommend essential relationships in a domain model more accurately than state-of-the-art rule-based methods. Based on a collection of datasets that we manually labeled and a set of 32 features we engineered, we train a machine learning model by using a random forest classifier. The results indicate that our approach has higher precision and F1-score than the baseline rule-based methods. Our findings provide preliminary evidence of the suitability of using machine learning to support the development of domain models, especially in recommending relationships between related entities. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Supervised learning

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Unmasking Fake News with Emotional Cues: A Comparative Study of Sentiment Reasoning Models 4

Unmasking Fake News with Emotional Cues: A Comparative Study...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Kumara, B.T.G.S. Ravikumar, Nirubikaa Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Australia Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka

ISBN: (纸本)9798350384864

Discerning truth from deception requires innovative tools in an era besieged by misinformation. Fake news is a form of misleading information delivered as reliable news. It is mainly made up of intentionally manipulating public opinion, decision bias, and exploiting general credulity. Various supervised learning-based fake news detection and classifications only focus on the semantic values due to their subjectivity in the current context. Sentiment analysis is also a common interpretation mechanism of information mining. This research explores the role of sentiment analysis in the context of fake news detection. Sentiment analysis, a facet of natural language processing, delves into the nuanced sentiments within the text, unveiling hidden narratives and differentiating between authentic reporting and carefully crafted deception. The study employs various sentiment reasoning models, including VADER, TextBlob, and Roberta, focusing on comparative analysis. The VADER model, complemented by a Bag of Words approach, emerges as the most effective, outperforming, compared to TextBlob and RoBERTa. Especially BERT sentiment reasoner model underperformed with uneven distribution in this fake news domain. The best VADER sentiments are taken for the transitions of sequential neural network architectures such as Simple Neural Network (SNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) featuring Long Short-Term Memory (LSTM). The LSTM model captures intricate patterns, dependencies, and contextual information. Evaluation metrics, encompassing accuracy, precision, recall, and F1 score, provide a comprehensive understanding of models' performance. The RNN LSTM performed well compared to the other sequential models. It primely emphasised the multifaceted landscape of sentiment analysis, presenting a robust strategy for combating the proliferation of fake news in an information-abundant age. Combining Sentiment Reasoning in VADER and RNN LSTM presents a rob

关键词： Sentiment analysis

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference

arXiv

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arXiv 2024年

作者： García-Carrasco, Jorge Maté, Alejandro Trujillo, Juan Department of Software and Computing Systems University of Alicante Spain

Large Language Models (LLMs) have shown impressive performance across a wide range of tasks. However, the size of LLMs is steadily increasing, hindering their application on computationally constrained environments. On the other hand, despite their general capabilities, there are many situations where only one specific task is performed, rendering all other capabilities unnecessary and wasteful. This leads us to the following question: Is it possible to extract the minimal subset from an LLM that is able to perform a specific task in a faster, standalone manner? Recent works on Mechanistic Interpretability (MI) have shown that specific tasks are performed by a localized subset of components, or circuit. However, current techniques used to identify the circuit cannot be used to extract it for its standalone usage. In this work, we propose a novel approach to automatically extract the subset of the LLM that properly performs a targeted task requiring no additional training and a small amount of data samples. We evaluate our approach on different tasks and show that the resulting models are (i) considerably smaller, reducing the number of parameters up to 82.77% and (ii) more interpretable, as they focus on the circuit that is used to carry out the specific task, and can therefore be understood using MI techniques. © 2024, CC BY.

关键词： Inference engines

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ELAINE: rELiAbility and evIdence-aware News vErifier 1

ELAINE: rELiAbility and evIdence-aware News vErifier

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1st Workshop on NLP Applied to Misinformation, NLP-MisInfo 2023

作者： Badenes-Olmedo, Carlos Saquete, Estela Sepúlveda-Torres, Robiert Bonet-Jover, Alba Ontology Engineering Group Universidad Politécnica de Madrid Spain Department of Software and Computing Systems University of Alicante Spain

Disinformation is one of the main problems of today’s society, and specifically the viralization of fake news. This research presents ELAINE, a hybrid proposal to detect the veracity of news items that combines content reliability information with external evidence. The external evidence is extracted from a scientific knowledge base that contains medical information associated with coronavirus, organized in a knowledge graph created from a CORD-19 corpus. The information is accessed using Natural Language Question Answering and a set of evidences are extracted and their relevance measured. By combining both reliability and evidence information, the veracity of the news items can be predicted, improving both accuracy and F1 compared with using only reliability information. These results prove that the approach presented is very promising for the veracity detection task. © 2023 CEUR-WS. All rights reserved.

关键词： Knowledge graph

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