With the rapid proliferation of social networking sites (SNS), automatic topic extraction from various text messages posted on SNS are becoming an important source of information for understanding current social trends or needs. Latent Dirichlet Allo...
The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the re...
In this paper, we study the feasibility of performing fuzzy information retrieval by word embedding. We propose a fuzzy information retrieval approach to capture the relationships between words and query language, which combines some techniques of de...
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...
Uyghur is a morphologically rich and typical agglutinating language, and morphological segmentation affects the performance of Uyghur named-entity recognition (NER). Common Uyghur NER systems use the word sequence as input and rely heavily on feature...
Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences...
In this paper, we propose a novel information criteria-based approach to select the dimensionality of the word2vec Skip-gram (SG). From the perspective of the probability theory, SG is considered as an implicit probability distribution estimation und...
This study is intended to evaluate and contrast the performance of varying combinations of embedding algorithms and weighting systems in measuring perception-based text similarity using the Cosine Similarity approach. Within a structured experiment d...
Biological-sequence representation methods are pivotal for advancing machine learning in computational biology, transforming nucleotide and protein sequences into formats that enhance predictive modeling and downstream task performance. This review c...
Increasingly, the web produces massive volumes of texts, alone or associated with images, videos, photographs, together with some metadata, indispensable for their finding and retrieval. Keywords/keyphrases that characterize the semantic content of d...
Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchan...
The growing significance of sensor data in the development of information technology services finds obstacles due to disparate data presentations and non-adherence to FAIR principles. This paper introduces a novel approach for sensor data gathering a...
Unsupervised topic extraction is a vital step in automatically extracting concise contentual information from large text corpora. Existing topic extraction methods lack the capability of linking relations between these topics which would further help...
New Testament studies has over the past years seen an increase in the use of digital methods, but some of the more advanced methods still lack proper integration. This article explores some of the advantages and disadvantages in employing computation...
Sentiment analysis utilizes Natural Language Processing (NLP) techniques to extract opinions from text, which is critical for businesses looking to refine strategies and better understand customer feedback. Understanding people’s sentiments abo...
This paper presents a mathematical analysis of semantic convergence in transformer-based language models, drawing inspiration from the concept of fractal self-similarity. We introduce and prove a novel theorem characterizing the gradient of embedding...
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...
In the evolving field of machine learning, deploying fair and transparent models remains a formidable challenge. This study builds on earlier research, demonstrating that neural architectures exhibit inherent biases by analyzing a broad spectrum of t...
Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models. We present in detail a method that is especially designed with the requirements of domain experts in mind. Like simila...
The use of different techniques and tools is a common practice to cover all stages in the development life-cycle of systems generating a significant number of work products. These artefacts are frequently encoded using diverse formats, and often requ...
Word embedding is an important reference for natural language processing tasks, which can generate distribution presentations of words based on many text data. Recent evidence demonstrates that introducing sememe knowledge is a promising strategy to...
To overcome the data sparseness in word embedding trained in low-resource languages, we propose a punctuation and parallel corpus based word embedding model. In particular, we generate the global word-pair co-occurrence matrix with the punctuation-ba...
Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting...
The purpose of cross-domain sentiment classification (CDSC) is to fully utilize the rich labeled data in the source domain to help the target domain perform sentiment classification even when labeled data are insufficient. Most of the existing method...
We present a new word embedding technique in a (non-linear) metric space based on the shared membership of terms in a corpus of textual documents, where the metric is naturally defined by the Boolean algebra of all subsets of the corpus and a measure...
19 November 2022
Background: The possible benefits of using semantic language models in the early diagnosis of major ischemic stroke (MIS) based on artificial intelligence (AI) are still underestimated. The present study strives to assay the feasibility of the word2v...
Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared w...
As a core task of natural language processing and information retrieval, automatic text summarization is widely applied in many fields. There are two existing methods for text summarization task at present: abstractive and extractive. On this basis w...
Detecting cybersecurity intelligence (CSI) on social media such as Twitter is crucial because it allows security experts to respond cyber threats in advance. In this paper, we devise a new text classification model based on deep learning to classify...
As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced arti...
Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), an...
Text vectorization is the basic work of natural language processing tasks. High-quality vector representation with rich feature information can guarantee the quality of entity recognition and other downstream tasks in the field of traditional Chinese...
A Domain Generation Algorithm (DGA) employs botnets to generate domain names through a communication link between the C&C server and the bots. A DGA can generate pseudo-random AGDs (algorithmically generated domains) regularly, a handy method for...
Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small frac...
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...
Long Short Term Memory (LSTM)-based structures have demonstrated their efficiency for daily living recognition activities in smart homes by capturing the order of sensor activations and their temporal dependencies. Nevertheless, they still fail in de...
A new method for the recognition of meaningful changes in social state based on transformations of the linguistic content in Arabic newspapers is suggested. The detected alterations of the linguistic material in Arabic newspapers play an indicator ro...
This article presents an novel approach inspired by the modern exploration of short texts’ patterning to creations prescribed to the outstanding Islamic jurist, theologian, and mystical thinker Abu Hamid Al Ghazali. We treat the task with the g...
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...
Existing keyword-based search techniques suffer from limitations owing to unknown, mismatched, and obscure vocabulary. These challenges are particularly prevalent in social media, where slang, jargon, and memetics are abundant. We develop a new techn...
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...
Hashtags are considered important in various real-world applications, including tweet mining, query expansion, and sentiment analysis. Hence, recommending hashtags from tagged tweets has been considered significant by the research community. However,...
Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can...
Health problems in older adults lead to situations where communication with peers, family and caregivers becomes challenging for seniors; therefore, it is necessary to use alternative methods to facilitate communication. In this context, Augmentative...
Metaphors are an integral and important part of human communication and greatly impact the way our thinking is formed and how we understand the world. The theory of the conceptual metaphor has shifted the focus of research from words to thinking, and...
Background: Improving prediction models to timely detect lung cancer is paramount. Our aim is to develop and validate prediction models for early detection of lung cancer in primary care, based on free-text consultation notes, that exploit the order...
Fake news is false or misleading information that looks like real news and spreads through traditional and social media. It has a big impact on our social lives, especially in politics. In Pakistan, where Urdu is the main language, finding fake news...
Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the...
In multilingual textual archives, the availability of textual annotation, that is keywords either manually or automatically associated with texts, is something worth exploiting to improve user experience and successful navigation, search and visualiz...
The KBQA (Knowledge-Based Question Answering) system is an essential part of the smart customer service system. KBQA is a type of QA (Question Answering) system based on KB (Knowledge Base). It aims to automatically answer natural language questions...
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