Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elder...
The task to extract relations tries to identify relationships between two named entities in a sentence. Because a sentence usually contains several named entities, capturing structural information of a sentence is important to support this task. Curr...
The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man...
Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words’ feature vector...
Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying...
This research proposes a novel technique for fake news classification using natural language processing (NLP) methods. The proposed technique, TwIdw (Term weight–inverse document weight), is used for feature extraction and is based on TfIdf, wi...
Various tasks in natural language processing (NLP) suffer from lack of labelled training data, which deep neural networks are hungry for. In this paper, we relied upon features learned to generate relation triples from the open information extraction...
One of the most impressive applications of the combined use of natural language processing (NLP), classical machine learning, and deep learning (DL) approaches is the estimation of demographic traits from the text. Author Profiling (AP) is the analys...
Automatic essay evaluation, an essential application of natural language processing (NLP) technology in education, has been increasingly employed in writing instruction and language proficiency assessment. Because automatic Chinese Essay Evaluation (...
Four versions of science and history texts were tailored to diverse hypothetical reader profiles (high and low reading skills and domain knowledge), generated by four Large Language Models (i.e., Claude, Llama, ChatGPT, and Gemini). The Natural Langu...
Collating vast test reports is a time-consuming and laborious task in crowdsourced testing. Crowdsourced test reports are usually presented in two ways, one as text and the other as images, which have symmetrical content. Researchers have proposed ma...
In this paper, we present a novel pipelined near real-time speaker recognition architecture that enhances the performance of speaker recognition by exploiting the advantages of hybrid feature extraction techniques that contain the features of Gabor F...
The study undertook a comprehensive review and comparative analysis of natural language processing techniques for news article classification, with a particular focus on Java language libraries. The dataset comprised an excess of 200,000 items of new...
Sentiment analysis is a crucial component of text mining and natural language processing (NLP), involving the evaluation and classification of text data based on its emotional tone, typically categorized as positive, negative, or neutral. While signi...
Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refr...
Natural language processing (NLP) models based on deep neural networks (DNNs) are vulnerable to backdoor attacks. Existing backdoor defense methods have limited effectiveness and coverage scenarios. We propose a textual backdoor defense method based...
Linguistic features, particularly the use of first-person singular pronouns (FPSPs), have been identified as potential indicators of suicidal ideation. Machine learning (ML) and natural language processing (NLP) have shown potential in suicide detect...
This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based...
Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this st...
Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment ana...
Short text classification is an important problem of natural language processing (NLP), and graph neural networks (GNNs) have been successfully used to solve different NLP problems. However, few studies employ GNN for short text classification, and m...
Text classification is a major task of NLP (Natural Language Processing) and has been the focus of attention for years. News classification as a branch of text classification is characterized by complex structure, large amounts of information and lon...
Visual question answering (VQA) is a task that generates or predicts an answer to a question in human language about visual images. VQA is an active field combining two AI branches: NLP and computer vision. VQA in the medical field is still at an ear...
Relation classification is an important fundamental task in information extraction, and convolutional neural networks have been commonly applied to relation classification with good results. In recent years, due to the proposed pre-training model BER...
In this paper, we present a new deep learning model to classify hematoxylin–eosin-stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas). Our model uses a parallel structure...
Convolutional Neural Networks (CNNs) have demonstrated promising performance in many NLP tasks owing to their excellent local feature-extraction capability. Many previous works have made word-level 2D CNNs deeper to capture global representations of...
Currently, machine learning techniques are widely used in structural seismic response studies. The developed network models for various types of seismic response provide new ways to analyse seismic hazards. However, it is not easy to balance the appl...
While extensive research has documented the cognitive changes associated with Parkinson’s disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial inte...
The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The perf...
Named entity recognition is a basic task in natural language processing, and there is a large number of nested structures in named entities. Nested named entities become the basis for solving many tasks in NLP. A nested named entity recognition model...
This research investigates consumer reviews of eco-friendly products on Amazon to uncover valuable sustainability insights that can inform design optimization. Using natural language processing (NLP) techniques, including sentiment analysis, key term...
To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-malware software, as well as firewalls, require frequent updates and proactive implementation. However, processing the vast amounts of dataset examples can be o...
Arabic abstractive summarization presents a complex multi-objective optimization challenge, balancing readability, informativeness, and conciseness. While extractive approaches dominate NLP, abstractive methods—particularly for Arabic—rem...
With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithm...
In the expanding landscape of biomedical literature, numerous latent associations outlined in scholarly papers await discovery and integration into biomedical databases. Biomedical Natural Language Processing (NLP) research focuses on automating know...
Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively cap...
Speech emotion recognition is a substantial component of natural language processing (NLP). It has strict requirements for the effectiveness of feature extraction and that of the acoustic model. With that in mind, a Heterogeneous Parallel Convolution...
Entity-based information extraction is one of the main applications of Natural Language Processing (NLP). Recently, deep transfer-learning utilizing contextualized word embedding from pre-trained language models has shown remarkable results for many...
In recent years, joint entity–relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and...
Paraphrase detection is a Natural-Language Processing (NLP) task that aims at automatically identifying whether two sentences convey the same meaning (even with different words). For the Portuguese language, most of the works model this task as a mac...
Version Control and Source Code Management Systems, such as GitHub, contain a large amount of unstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrie...
Aspect-based sentiment analysis (ABSA) is a method used to identify the aspects discussed in a given text and determine the sentiment expressed towards each aspect. This can help provide a more fine-grained understanding of the opinions expressed in...
Construction-oriented quantity take-off (QTO) refers to the process of determining the quantities for construction items or work packages in accordance with their descriptions. However, the current construction-oriented QTO practice relies on estimat...
Text representation is one of the key tasks in the field of natural language processing (NLP). Traditional feature extraction and weighting methods often use the bag-of-words (BoW) model, which may lead to a lack of semantic information as well as th...
Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a form of fraud that attracts people and businesses to access malicious uniform resource locators (URLs) and submit their sensitive information such as password...
This study proposes an artificial intelligence (AI)-powered multimodal system designed to enhance the appreciation of traditional poetry, using Japanese haiku as the primary application domain. At the core of the system is an intelligent data analysi...
Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to ca...
Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a...
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...
Relation extraction, a crucial task in natural language processing (NLP) for constructing knowledge graphs, entails extracting relational semantics between pairs of entities within a sentence. Given the intricacy of language, a single sentence often...
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