Addressing the challenges of scarce measured data for Class 3–4 bridges and strong subjectivity in manual assessments in bridge technical-condition evaluation, this study innovatively proposes a FastText+CNN evaluation model that integrates sem...
Based on the original deep network architecture, this paper replaces the deep integrated network by integrating shallow FastText, a bidirectional gated recurrent unit (GRU) network and the convolutional neural networks (CNNs). In FastText, word embed...
This article is the third paper in a series aimed at the establishment of the authorship of Russian-language texts. This paper considers methods for determining the authorship of classical Russian literary texts, as well as fanfiction texts. The proc...
Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in par...
In several applications of text classification, training document labels are provided by human evaluators, and therefore, gathering sufficient data for model creation is time consuming and costly. The labeling time and effort may be reduced by active...
Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzi...
The expanding Arabic user base presents a unique opportunity for researchers to tap into vast online Arabic resources. However, the lack of reliable Arabic word embedding models and the limited availability of Arabic corpora poses significant challen...
In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to...
Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate se...
Intent detection is one of the main tasks of a dialogue system. In this paper, we present our intent detection system that is based on fastText word embeddings and a neural network classifier. We find an improvement in fastText sentence vectorization...
Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these...
Diagnosing mental disorders is complex due to the genetic, environmental and psychological contributors and the individual risk factors. Language markers for mental disorders can help to diagnose a person. Research thus far on language markers and th...
Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective underst...
Accurate intent detection-based chatbots are usually trained on larger datasets that are not available for some languages. Seeking the most accurate models, three English benchmark datasets that were human-translated into four morphologically complex...
Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities i...
This work proposes a new literature topic clustering analysis framework, based on which the topics of digital-economy-related studies are condensed. First, we calculated the word vector of keywords using the FastText model, and then the keywords were...
Modern approaches to computing consumer price indices include the use of various data sources, such as web-scraped data or scanner data, which are very large in volume and need special processing techniques. In this paper, we address one of the main...
In this paper, we use a corpus of about 100,000 happy moments written by people of different genders, marital statuses, parenthood statuses, and ages to explore the following questions: Are there differences between men and women, married and unmarri...
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive...
Accurate generative chatbots are usually trained on large datasets of question–answer pairs. Despite such datasets not existing for some languages, it does not reduce the need for companies to have chatbot technology in their websites. However,...
Due to instant availability of data on social media platforms like Twitter, and advances in machine learning and data management technology, real-time crisis informatics has emerged as a prolific research area in the last decade. Although several ben...
TextNetTopics is a novel topic modeling-based topic selection approach that finds highly ranked discriminative topics for training text classification models, where a topic is a set of semantically related words. However, it suffers from several limi...
The importance of cybersecurity has recently been increasing. A malware coder writes malware into normal executable files. A computer is more likely to be infected by malware when users have easy access to various executables. Malware is considered a...
This paper is devoted to solving the problem of determining the age of the author of the text based on models of deep neural networks. The article presents an analysis of methods for determining the age of the author of a text and approaches to deter...
Large language models (LLMs) are increasingly applied to specialized domains like medical education, necessitating tailored approaches to evaluate structured responses such as SBAR (Situation, Background, Assessment, Recommendation). This study devel...
In this article, we address the problem of detecting anomalies in system log files. Computer systems generate huge numbers of events, which are noted in event log files. While most of them report normal actions, an unusual entry may inform about a fa...
In this study, we investigate knowledge transfer between two distinct sentence embedding models: a computationally demanding, highly performant model and a lightweight model derived from word vector averaging. Our objective is to augment the represen...
It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world...
Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology....
In the increasingly complex domain of Korean voice phishing attacks, advanced detection techniques are paramount. Traditional methods have achieved some degree of success. However, they often fail to detect sophisticated voice phishing attacks, highl...
The ‘intention’ classification of a user question is an important element of a task-engine driven chatbot. The essence of a user question’s intention understanding is the text classification. The transfer learning, such as BERT (Bid...
The detection of essays written by AI compared to those authored by students is increasingly becoming a significant issue in educational settings. This research examines various numerical text representation techniques to improve the classification o...
Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. Howe...
Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information f...
Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate...
In March 2020, the World Health Organisation declared that COVID-19 was a new pandemic. This deadly virus spread and affected many countries in the world. During the outbreak, social media platforms such as Twitter contributed valuable and massive am...
In digital forensics, especially Windows forensics, identifying anomalous file paths is crucial when dealing with large-scale data. Traditional static embedding methods, which aggregate token-level representations, discard hierarchical and sequential...
The induction of the semantics of unstructured text corpora is a crucial task for modern natural language processing and artificial intelligence applications. The Named Entity Disambiguation task comprises the extraction of Named Entities and their l...
Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet a...
Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applicat...
Current research widely acknowledges that the subcellular localization of mRNA is crucial for understanding its biological functions. However, current methods for mRNA subcellular localization based on k-mer frequency features may overlook the sequen...
We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Sho...
Cross-lingual embeddings are vector space representations where word translations tend to be co-located. These representations enable learning transfer across languages, thus bridging the gap between data-rich languages such as English and others. In...
Short message services (SMS), microblogging tools, instant message apps, and commercial websites produce numerous short text messages every day. These short text messages are usually guaranteed to reach mass audience with low cost. Spammers take adva...
The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals’ emotions empowers sentiment analysis. However, sentiment analysis becomes e...
Due to the COVID-19 pandemic, the sales of fast-food businesses have dropped sharply. Customer satisfaction has always been one of the key factors for the sustainable development of enterprises. However, in the fast-food restaurant business, gaining...
This study introduces a novel approach to text alignment tailored for ancient languages, with a focus on Hebrew and Aramaic, aimed at enhancing text reuse detection. Unlike previous methods, our approach integrates multiple NLP components into a spec...
The classification of sparse text, common in short or specialized content, is challenging for natural language processing. These challenges stem from high-dimensional data and scarce relevant features because sparse text can result from noisy, short,...
Social media has transformed into a crucial channel for political expression. Twitter, especially, is a vital platform used to exchange political hate in Pakistan. Political hate speech affects the public image of politicians, targets their supporter...
The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset s...
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