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THAR- Targeted Hate Speech Against Religion: A high-quality Hindi-English code-mixed Dataset with the Application of Deep Learning Models for Automatic Detection ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-18 Deepawali Sharma, Aakash Singh, Vivek Kumar Singh
During the last decade, social media has gained significant popularity as a medium for individuals to express their views on various topics. However, some individuals also exploit the social media platforms to spread hatred through their comments and posts, some of which target individuals, communities or religions. Given the deep emotional connections people have to their religious beliefs, this form
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Neurocomputer System of Semantic Analysis of the Text in the Kazakh Language ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-13 Akerke Akanova, Aisulu Ismailova, Zhanar Oralbekova, Zhanat Kenzhebayeva, Galiya Anarbekova
The purpose of the study is to solve an extreme mathematical problem – semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the LDA model and vector representation of words were used. This study presents the development of a neurocomputer system
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Multilingual Neural Machine Translation for Indic to Indic Languages ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-12 Sudhansu Bala Das, Divyajyoti Panda, Tapas Kumar Mishra, Bidyut Kr. Patra, Asif Ekbal
The method of translation from one language to another without human intervention is known as Machine Translation (MT). Multilingual neural machine translation (MNMT) is a technique for MT that builds a single model for multiple languages. It is preferred over other approaches since it decreases training time and improves translation in low-resource contexts, i.e. for languages that have insufficient
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Medical Question Summarization with Entity-driven Contrastive Learning ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-11 Wenpeng Lu, Sibo Wei, Xueping Peng, Yi-Fei Wang, Usman Naseem, Shoujin Wang
By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although deep learning has been applied to successfully address
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Gender Classification System Based on the Behavioral Biometric Modality: Application of Handwritten Text ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Shaveta Dargan, Munish Kumar
Forensic Science is a branch of science that deals with the discovery, examination, and analysis of strong elements or evidence involved in the criminal justice system. It involves the use of scientific methods to investigate crimes. The Gender Classification System is closely linked to forensic studies, specifically investigating individuals through their handwriting, known as Behavioral Biometrics
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Consensus-Based Machine Translation for Code-Mixed Texts ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Sainik Kumar Mahata, Dipankar Das, Sivaji Bandyopadhyay
Multilingualism in India is widespread due to its long history of foreign acquaintances. This leads to the presence of an audience familiar with conversing using more than one language. Additionally, due to the social media boom, the usage of multiple languages to communicate has become extensive. Hence, the need for a translation system that can serve the novice and monolingual user is the need of
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DeepMedFeature: An Accurate Feature Extraction and Drug-Drug Interaction Model for Clinical Text in Medical Informatics ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 M. Shoaib Malik, Sara Jawad, Syed Atif Moqurrab, Gautam Srivastava
Drug-drug interactions (DDIs) are an important biological phenomenon which can result in medical errors from medical practitioners. Drug interactions can change the molecular structure of interacting agents which may prove to be fatal in the worst case. Finding drug interactions early in diagnosis can be pivotal in side-effect prevention. The growth of big data provides a rich source of information
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Multization: Multi-Modal Summarization Enhanced by Multi-Contextually Relevant and Irrelevant Attention Alignment ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Huan Rong, Zhongfeng Chen, Zhenyu Lu, Fan Xu, Victor S. Sheng
This paper focuses on the task of Multi-Modal Summarization with Multi-Modal Output for China JD.COM e-commerce product description containing both source text and source images. In the context learning of multi-modal (text and image) input, there exists a semantic gap between text and image, especially in the cross-modal semantics of text and image. As a result, capturing shared cross-modal semantics
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Unsupervised Multimodal Machine Translation for Low-Resource Distant Language Pairs ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Turghun Tayir, Lin Li
Unsupervised machine translation (UMT) has recently attracted more attention from researchers, enabling models to translate when languages lack parallel corpora. However, the current works mainly consider close language pairs (e.g., English-German and English-French), and the effectiveness of visual content for distant language pairs has yet to be investigated. This paper proposes a unsupervised multimodal
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PAMR: Persian Abstract Meaning Representation Corpus ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Nasim Tohidi, Chitra Dadkhah, Reza Nouralizadeh Ganji, Ehsan Ghaffari Sadr, Hoda Elmi
One of the most used and well-known semantic representation models is Abstract Meaning Representation (AMR). This representation has had numerous applications in natural language processing tasks in recent years. Currently, for English and Chinese languages, large annotated corpora are available. In addition, in some low-resource languages, related corpora have been generated with less size; although
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Arabic Sentiment Analysis for ChatGPT Using Machine Learning Classification Algorithms: A Hyperparameter Optimization Technique ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Ahmad Nasayreh, Rabia Emhamed Al Mamlook, Ghassan Samara, Hasan Gharaibeh, Mohammad Aljaidi, Dalia Alzu'bi, Essam Al-Daoud, Laith Abualigah
In the realm of ChatGPT's language capabilities, exploring Arabic Sentiment Analysis emerges as a crucial research focus. This study centers on ChatGPT, a popular machine learning model engaging in dialogues with users, garnering attention for its exceptional performance and widespread impact, particularly in the Arab world. The objective is to assess people's opinions about ChatGPT, categorizing them
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An Expert System for Indian Sign Language Recognition Using Spatial Attention–based Feature and Temporal Feature ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Soumen Das, Saroj Kr. Biswas, Biswajit Purkayastha
Sign Language (SL) is the only means of communication for the hearing-impaired people. Normal people have difficulty understanding SL, resulting in a communication barrier between hearing impaired people and hearing community. However, the Sign Language Recognition System (SLRS) has helped to bridge the communication gap. Many SLRs are proposed for recognizing SL; however, a limited number of works
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Modeling a Novel Approach for Emotion Recognition Using Learning and Natural Language Processing ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Lakshmi Lalitha V., Dinesh Kumar Anguraj
Various facts, including politics, entertainment, industry, and research fields, are connected to analyzing the audience's emotions. Sentiment Analysis (SA) is a Natural Language Processing (NLP) concept that uses statistical and lexical forms as well as learning techniques to forecast how different types of content in social media will express the audience's neutral, positive, and negative emotions
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Improved BIO-Based Chinese Automatic Abstract-Generation Model ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Qing Li, Weibin Wan, Yuming Zhao, Xiaoyan Jiang
With its unique information-filtering function, text summarization technology has become a significant aspect of search engines and question-and-answer systems. However, existing models that include the copy mechanism often lack the ability to extract important fragments, resulting in generated content that suffers from thematic deviation and insufficient generalization. Specifically, Chinese automatic
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Multi-view Image Fusion Using Ensemble Deep Learning Algorithm For MRI And CT Images ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Thenmoezhi N., Perumal B., Lakshmi A.
Medical image fusions are crucial elements in image-based health care diagnostics or therapies and generic applications of computer visions. However, the majority of existing methods suffer from noise distortion that affects the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively
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Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Jie Liu, Yaguang Li, Shizhu He, Shun Wu, Kang Liu, Shenping Liu, Jiong Wang, Qing Zhang
Social media produces large amounts of content every day. How to predict the potential influences of the contents from a social reply feedback perspective is a key issue that has not been explored. Thus, we propose a novel task named reply keyword prediction in social media, which aims to predict the keywords in the potential replies in as many aspects as possible. One prerequisite challenge is that
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Improved Regression Analysis with Ensemble Pipeline Approach for Applications across Multiple Domains ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Debajyoty Banik, Rahul Paul, Rajkumar Singh Rathore, Rutvij H. Jhaveri
In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three datasets, Kaggle House Price, Boston House Price, and California Housing, using various performance metrics. The results consistently show that our
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SCT: Summary Caption Technique for Retrieving Relevant Images in Alignment with Multimodal Abstractive Summary ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Shaik Rafi, Ranjita Das
This work proposes an efficient Summary Caption Technique that considers the multimodal summary and image captions as input to retrieve the correspondence images from the captions that are highly influential to the multimodal summary. Matching a multimodal summary with an appropriate image is a challenging task in computer vision and natural language processing. Merging in these fields is tedious,
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Disambiguation of Isolated Manipuri Tonal Contrast Word Pairs Using Acoustic Features ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Thiyam Susma Devi, Pradip K. Das
Manipuri is a low-resource, Tibeto-Burman tonal language spoken mainly in Manipur, a northeastern state of India. Tone identification is crucial to speech comprehension for tonal languages, where tone defines the word’s meaning. Automatic Speech Recognition for those languages can perform better by including tonal information from a powerful tone detection system. While significant research has been
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CodeKGC: Code Language Model for Generative Knowledge Graph Construction ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, Ningyu Zhang
Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively
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Towards Mental Health Analysis in Social Media for Low-resourced Languages ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Muskan Garg
The surge in internet use for expression of personal thoughts and beliefs has made it increasingly feasible for the social Natural Language Processing (NLP) research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of low-resourced social media data bring to fore the importance of real-time responsible Artificial
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SEEUNRS: Semantically Enriched Entity-Based Urdu News Recommendation System ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-09 Safia Kanwal, Muhammad Kamran Malik, Zubair Nawaz, Khawar Mehmood
The advancement in the production, distribution, and consumption of news has fostered easy access to the news with fair challenges. The main challenge is to present the right news to the right audience. The news recommendation system is one of the technological solutions to this problem. Much work has been done on news recommendation systems for the major languages of the world, but trivial work has
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TransVAE-PAM: A Combined Transformer and DAG-based Approach for Enhanced Fake News Detection in Indian Context ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-02 Shivani Tufchi, Tanveer Ahmed, Ashima Yadav, Krishna Kant Agrawal, Ankit Vidyarthi
In this study, we introduce a novel method, “TransVAE-PAM”, for the classification of fake news articles, tailored specifically for the Indian context. The approach capitalizes on state-of-the-art contextual and sentence transformer-based embedding models to generate article embeddings. Furthermore, we also try to address the issue of compact model size. In this respect, we employ a Variational Autoencoder
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Am I hurt?: Evaluating Psychological Pain Detection in Hindi Text using Transformer-based Models ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-05 Ravleen Kaur, M. P. S. Bhatia, Akshi Kumar
The automated evaluation of pain is critical for developing effective pain management approaches that seek to alleviate while preserving patients’ functioning. Transformer-based models can aid in detecting pain from Hindi text data gathered from social media by leveraging their ability to capture complex language patterns and contextual information. By understanding the nuances and context of Hindi
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Opinion Mining on Social Media Text Using Optimized Deep Belief Networks ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-02 S. Vinayaga Vadivu, P. Nagaraj, B. S. Murugan
In the digital world, most people spend their leisure and precious time on social media networks such as Facebook, Twitter. Instagram, and so on. Moreover, users post their views of products, services, political parties on their social sites. This information is viewed by many other users and brands. With the aid of these posts and tweets, the emotions, polarities of users are extracted to obtain the
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A Survey of Knowledge Enhanced Pre-trained Language Models ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-01 Jian Yang, Xinyu Hu, Gang Xiao, Yulong Shen
Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP) after fine-tuning. These models, however, suffer from poor robustness and lack of interpretability. We refer to pre-trained language models with knowledge injection as knowledge-enhanced
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Exploration on Advanced Intelligent Algorithms of Artificial Intelligence for Verb Recognition in Machine Translation ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-28 Qinghua Ai, Qingyan Ai, Jun Wang
This article aimed to address the problems of word order confusion, context dependency, and ambiguity in traditional machine translation (MT) methods for verb recognition. By applying advanced intelligent algorithms of artificial intelligence, verb recognition can be better processed and the quality and accuracy of MT can be improved. Based on Neural machine translation (NMT), basic attention mechanisms
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Leveraging Bidirectionl LSTM with CRFs for Pashto tagging ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-27 Farooq Zaman, Onaiza Maqbool, Jaweria Kanwal
Part-of-speech tagging plays a vital role in text processing and natural language understanding. Very few attempts have been made in the past for tagging Pashto Part-of-Speech. In this work, we present LSTM based approach for Pashto part-of-speech tagging with special focus on ambiguity resolution. Initially we created a corpus of Pashto sentences having words with multiple meanings and their tags
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A Hybrid Scene Text Script Identification Network for regional Indian Languages ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-24 Veronica Naosekpam, Nilkanta Sahu
In this work, we introduce WAFFNet, an attention-centric feature fusion architecture tailored for word-level multi-lingual scene text script identification. Motivated by the limitations of traditional approaches that rely exclusively on feature-based methods or deep learning strategies, our approach amalgamates statistical and deep features to bridge the gap. At the core of WAFFNet, we utilized the
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A Natural Language Processing System for Text Classification Corpus Based on Machine Learning ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-19 Yawen Su
A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation
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Handling Imbalance and Limited Data in Thyroid Ultrasound and Diabetic Retinopathy Datasets Using Discrete Levy Flights Grey Wolf Optimizer Based Random Forest for Robust Medical Data Classification ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-16 Shobha Aswal, Neelu Jyothi Ahuja, Ritika Mehra
In the field of disease diagnosis, medical image classification faces an inherent challenge due to various factors involving data imbalance, image quality variability, annotation variability, and limited data availability and data representativeness. Such challenges affect the algorithm's classification ability on the medical images in an adverse way, which leads to biased model outcomes and inaccurate
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Enriching Urdu NER with BERT Embedding, Data Augmentation, and Hybrid Encoder-CNN Architecture ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-15 Anil Ahmed, Degen Huang, Syed Yasser Arafat, Imran Hameed
Named Entity Recognition (NER) is an indispensable component of Natural Language Processing (NLP), which aims to identify and classify entities within text data. While Deep Learning (DL) models have excelled in NER for well-resourced languages like English, Spanish, and Chinese, they face significant hurdles when dealing with low-resource languages like Urdu. These challenges stem from the intricate
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Sentiment Analysis Method of Epidemic-related Microblog Based on Hesitation Theory ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-14 Yang Yu, Dong Qiu, HuanYu Wan
The COVID-19 pandemic in 2020 brought an unprecedented global crisis. After two years of control efforts, life gradually returned to the pre-pandemic state, but localized outbreaks continued to occur. Towards the end of 2022, COVID-19 resurged in China, leading to another disruption of people’s lives and work. Many pieces of information on social media reflected people’s views and emotions towards
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MSEConv: A Unified Warping Framework for Video Frame Interpolation ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-14 Xiangling Ding, Pu Huang, Dengyong Zhang, Wei Liang, Feng Li, Gaobo Yang, Xin Liao, Yue Li
Within the context of video frame interpolation, complex motion modeling is the task of capturing, in a video sequence, where the moving objects are located in the interpolated frame, and how to maintain the temporal consistency of motion. Existing video frame interpolation methods typically assign either a fixed size of the motion kernel or a refined optical flow to model complex motions. However
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A Study for Enhancing Low-resource Thai-Myanmar-English Neural Machine Translation ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-13 Mya Ei San, Sasiporn Usanavasin, Ye Kyaw Thu, Manabu Okumura
Several methodologies have recently been proposed to enhance the performance of low-resource Neural Machine Translation (NMT). However, these techniques have yet to be explored thoroughly in low-resource Thai and Myanmar languages. Therefore, we first applied augmentation techniques such as SwitchOut and Ciphertext Based Data Augmentation (CipherDAug) to improve NMT performance in these languages.
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Boundary-Aware Abstractive Summarization with Entity-Augmented Attention for Enhancing Faithfulness ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-13 Jiuyi Li, Junpeng Liu, Jianjun Ma, Wei Yang, Degen Huang
With the successful application of deep learning, document summarization systems can produce more readable results. However, abstractive summarization still suffers from unfaithful outputs and factual errors, especially in named entities. Current approaches tend to employ external knowledge to improve model performance while neglecting the boundary information and the semantics of the entities. In
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Contrastive Language-Knowledge Graph Pre-training ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-09 Xiaowei Yuan, Kang Liu, Yequan Wang
Recent years have witnessed a surge of academic interest in knowledge-enhanced pre-trained language models (PLMs) that incorporate factual knowledge to enhance knowledge-driven applications. Nevertheless, existing studies primarily focus on shallow, static, and separately pre-trained entity embeddings, with few delving into the potential of deep contextualized knowledge representation for knowledge
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The Computational Method for Supporting Thai VerbNet Construction ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Krittanut Chungnoi, Rachada Kongkachandra, Sarun Gulyanon
VerbNet is a lexical resource for verbs that has many applications in natural language processing tasks, especially ones that require information about both the syntactic behavior and the semantics of verbs. This article presents an attempt to construct the first version of a Thai VerbNet corpus via data enrichment of the existing lexical resource. This corpus contains the annotation at both the syntactic
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Leveraging Dual Gloss Encoders in Chinese Biomedical Entity Linking ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Tzu-Mi Lin, Man-Chen Hung, Lung-Hao Lee
Entity linking is the task of assigning a unique identity to named entities mentioned in a text, a sort of word sense disambiguation that focuses on automatically determining a pre-defined sense for a target entity to be disambiguated. This study proposes the DGE (Dual Gloss Encoders) model for Chinese entity linking in the biomedical domain. We separately model a dual encoder architecture, comprising
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Multi-granularity Knowledge Sharing in Low-resource Neural Machine Translation ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Chenggang Mi, Shaoliang Xie, Yi Fan
As the rapid development of deep learning methods, neural machine translation (NMT) has attracted more and more attention in recent years. However, lack of bilingual resources decreases the performance of the low-resource NMT model seriously. To overcome this problem, several studies put their efforts on knowledge transfer from high-resource language pairs to low-resource language pairs. However, these
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Transliteration Characteristics in Romanized Assamese Language Social Media Text and Machine Transliteration ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Hemanta Baruah, Sanasam Ranbir Singh, Priyankoo Sarmah
This article aims to understand different transliteration behaviors of Romanized Assamese text on social media. Assamese, a language that belongs to the Indo-Aryan language family, is also among the 22 scheduled languages in India. With the increasing popularity of social media in India and also the common use of the English Qwerty keyboard, Indian users on social media express themselves in their
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Explanation Guided Knowledge Distillation for Pre-trained Language Model Compression ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Zhao Yang, Yuanzhe Zhang, Dianbo Sui, Yiming Ju, Jun Zhao, Kang Liu
Knowledge distillation is widely used in pre-trained language model compression, which can transfer knowledge from a cumbersome model to a lightweight one. Though knowledge distillation based model compression has achieved promising performance, we observe that explanations between the teacher model and the student model are not consistent. We argue that the student model should study not only the
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DSISA: A New Neural Machine Translation Combining Dependency Weight and Neighbors ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Lingfang Li, Aijun Zhang, Ming-Xing Luo
Most of the previous neural machine translations (NMT) rely on parallel corpus. Integrating explicitly prior syntactic structure information can improve the neural machine translation. In this article, we propose a Syntax Induced Self-Attention (SISA) which explores the influence of dependence relation between words through the attention mechanism and fine-tunes the attention allocation of the sentence
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Improving the Detection of Multilingual South African Abusive Language via Skip-gram Using Joint Multilevel Domain Adaptation ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Oluwafemi Oriola, Eduan Kotzé
The distinctiveness and sparsity of low-resource multilingual South African abusive language necessitate the development of a novel solution to automatically detect different classes of abusive language instances using machine learning. Skip-gram has been used to address sparsity in machine learning classification problems but is inadequate in detecting South African abusive language due to the considerable
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Ibn-Ginni: An Improved Morphological Analyzer for Arabic ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Waleed Nazih, Amany Fashwan, Amr El-Gendy, Yasser Hifny
Arabic is a morphologically rich language, which means that the Arabic language has a complicated system of word formation and structure. The affixes in the Arabic language (i.e., prefixes and suffixes) can be added to root words to generate different meanings and grammatical functions. These affixes can indicate aspects such as tense, gender, number, case, person, and more. In addition, the meaning
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Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Asmaa Hashem Sweidan, Nashwa El-Bendary, Esraa Elhariri
This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely
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Hypergraph Neural Network for Emotion Recognition in Conversations ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Cheng Zheng, Haojie Xu, Xiao Sun
Modeling conversational context is an essential step for emotion recognition in conversations. Existing works still suffer from insufficient utilization of local context information and remote context information. This article designs a hypergraph neural network, namely HNN-ERC, to better utilize local and remote contextual information. HNN-ERC combines the recurrent neural network with the conventional
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Dual-Branch Multitask Fusion Network for Offline Chinese Writer Identification ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Haixia Wang, Yingyu Mao, Qingran Miao, Qun Xiao, Yilong Zhang
Chinese characters are complex and contain discriminative information, meaning that their writers have the potential to be recognized using less text. In this study, offline Chinese writer identification based on a single character was investigated. To extract comprehensive features to model Chinese characters, explicit and implicit information as well as global and local features are of interest.
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A Machine Learning–Based Readability Model for Gujarati Texts ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Chandrakant K. Bhogayata
This study aims to develop a machine learning–based model to predict the readability of Gujarati texts. The dataset was 50 prose passages from Gujarati literature. Fourteen lexical and syntactic readability text features were extracted from the dataset using a machine learning algorithm of the unigram parts of speech tagger and three Python programming scripts. Two samples of native Gujarati speaking
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An Ensemble Strategy with Gradient Conflict for Multi-Domain Neural Machine Translation ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Zhibo Man, Yujie Zhang, Yu Li, Yuanmeng Chen, Yufeng Chen, Jinan Xu
Multi-domain neural machine translation aims to construct a unified neural machine translation model to translate sentences across various domains. Nevertheless, previous studies have one limitation is the incapacity to acquire both domain-general and domain-specific representations concurrently. To this end, we propose an ensemble strategy with gradient conflict for multi-domain neural machine translation
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A Relation Embedding Assistance Networks for Multi-hop Question Answering ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Songlin Jiao, Zhenfang Zhu, Jiangtao Qi, Fuyong Xu, Hongli Pei, Wenling Wang, Ze Song, Peiyu Liu
Multi-hop Knowledge Graph Question Answering aims at finding an entity to answer natural language questions from knowledge graphs. When humans perform multi-hop reasoning, people tend to focus on specific relations across different hops and confirm the next entity. Therefore, most algorithms choose the wrong specific relation, which makes the system deviate from the correct reasoning path. The specific
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Deep Learning-based POS Tagger and Chunker for Odia Language Using Pre-trained Transformers ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Tusarkanta Dalai, Tapas Kumar Mishra, Pankaj K. Sa
Developing effective natural language processing (NLP) tools for low-resourced languages poses significant challenges. This article centers its attention on the task of Part-of-speech (POS) tagging and chunking, which pertains to the identification and categorization of linguistic units within sentences. POS tagging and Chunking have already produced positive results in English and other European languages
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Few-shot Incremental Event Detection ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-08 Hao Wang, Hanwen Shi, Jianyong Duan
Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively
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Fast Recurrent Neural Network with Bi-LSTM for Handwritten Tamil text segmentation in NLP ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-07 C. Vinotheni, Lakshmana Pandian S.
Tamil text segmentation is a long-standing test in language comprehension that entails separating a record into adjacent pieces based on its semantic design. Each segment is important in its own way. The segments are organised according to the purpose of the content examination as text groups, sentences, phrases, words, characters or any other data unit. That process has been portioned using rapid
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Automatic Construction of Interval-Valued Fuzzy Hindi WordNet using Lexico-Syntactic Patterns and Word Embeddings ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-02-02 Minni Jain, Rajni Jindal, Amita Jain
A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) consists of various classical semantic relations (crisp relations). To handle uncertainty and represent Hindi WordNet more semantically, Type- 1 fuzzy
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Understanding the performance of AI algorithms in Text-Based Emotion Detection for Conversational Agents ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-01-31 Sheetal D. Kusal, Shruti G. Patil, Jyoti Choudrie, Ketan V. Kotecha
Current industry trends demand automation in every aspect, where machines could replace humans. Recent advancements in conversational agents have grabbed a lot of attention from industries, markets, and businesses. Building conversational agents that exhibit human communication characteristics is a need in today's marketplace. Thus, by accumulating emotions, we can build emotionally-aware conversational
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Human Emotion Recognition Based on Machine Learning Algorithms with low Resource Environment ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-01-26 Asha P., Hemamalini V., Poongodaia., Swapna N., Soujanya K. L. S., Vaishali Gaikwad (Mohite)
It is difficult to discover significant audio elements and conduct systematic comparison analyses when trying to automatically detect emotions in speech. In situations when it is desirable to reduce memory and processing constraints, this research deals with emotion recognition. One way to achieve this is by reducing the amount of features. In this study, propose "Active Feature Selection" (AFS) method
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Fuzzified Deep Learning based Forgery Detection of Signatures in the Healthcare Mission Records ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-01-24 Ishu Priya, Nisha Chaurasia, Ashutosh Kumar Singh, Nakul Mehta, Abhishek Singh Kilak, Ahmed Alkhayyat
In an era subjected to digital solutions, handwritten signatures continue playing a crucial role in identity verification and document authentication. These signatures, a form of bio-metric verification, are unique to every individual, serving as a primitive method for confirming identity and ensuring security of an individual. Signatures, apart from being a means of personal authentication, are often
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Multi-Criteria Decision-Making Framework with Fuzzy Queries for Multimedia Data Fusion ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-01-16 Khalid Haseeb, Irshad Ahmad, Mohammad Siraj, Naveed Abbas, Gwanggil Jeon
Multimedia Internet of Things (MIoT) is widely explored in many smart applications for connectivity with wireless communication. Such networks are not like ordinary networks because it has to collect a massive amount of data and are further forwarded to processing systems. As MIoT is very limited in terms of resources for healthcare, smart homes, etc., therefore, energy efficiency with reliable data
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NLP-enabled Recommendation of Hashtags for Covid based Tweets using Hybrid BERT-LSTM Model ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-01-16 Kirti Jain, Rajni Jindal
Hashtags have become a new trend to summarize the feelings, sentiments, emotions, swinging moods, food tastes and much more. It also represents various entities like places, families and friends. It is a way to search and categorize various stuff on social media sites. With the increase in the hashtagging, there is a need to automate it, leading to the term “Hashtag Recommendation”. Also, there are