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An Interaction-Design Method Based upon a Modified Algorithm of Newton's Second Law of Motion ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-20 Qiao Feng, Tian Huang
Newton's Second Law of Motion algorithm is crucial to interactive visual effects and interactive behavior in interface design. Designers can only utilize simple algorithm templates in interface design since they lack organized mathematical science, especially programming. Directly using Newton's Second Law of Motion algorithm introduces two interface design issues. First, the created picture has a
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An adaptive Dual Graph Convolution Fusion Network for Aspect-Based Sentiment Analysis ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-17 Chunmei Wang, Yuan Luo, Chunli Meng, Feiniu Yuan
Aspect-based Sentiment Analysis (ABSA), also known as fine-grained sentiment analysis, aims to predict the sentiment polarity of specific aspect words in the sentence. Some studies have explored the semantic correlation between words in sentences through attention-based methods. Other studies have learned syntactic knowledge by using graph convolution networks to introduce dependency relations. These
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Knowledge-Enriched Prompt for Low-Resource Named Entity Recognition ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-17 Wenlong Hou, Weidong Zhao, Xianhui Liu, WenYan Guo
Named Entity Recognition (NER) in low-resource settings aims to identify and categorize entities in a sentence with limited labeled data. Although prompt-based methods have succeeded in low-resource perspectives, challenges persist in effectively harnessing information and optimizing computational efficiency. In this work, we present a novel prompt-based method to enhance low-resource NER without exhaustive
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Online English Resource Integration Algorithm based on high-dimensional Mixed Attribute Data Mining ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-16 Zhiyu Zhou
To improve the scalability of resources and ensure the effective sharing and utilization of online English resources, an online English resource integration algorithm based on high-dimensional mixed-attribute data mining is proposed. First, an integration structure based on high-dimensional mixed-attribute data mining is constructed. According to this structure, the characteristics of online English
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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 : 2024-04-15 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. Toward 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 toward the
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Contrastive Language-knowledge Graph Pre-training ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-15 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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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 : 2024-04-15 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 such as English, Spanish, and Chinese, they face significant hurdles when dealing with low-resource languages such as Urdu. These challenges stem from the intricate
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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 : 2024-04-15 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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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 : 2024-04-15 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 the 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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Unsupervised Multimodal Machine Translation for Low-resource Distant Language Pairs ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-15 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 article proposes an unsupervised
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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 : 2024-04-15 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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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 : 2024-04-15 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 latent Dirichlet allocation model and vector representation of words were used. This study presents the development of
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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 : 2024-04-15 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 a Long Short-term Memory–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
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Cross-Domain Aspect-Based Sentiment Classification with a Pre-Training and Fine-Tuning Strategy for Low-Resource Domains ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-15 Chuanjun Zhao, Meiling Wu, Xinyi Yang, Xuzhuang Sun, Suge Wang, Deyu Li
Aspect-based sentiment classification (ABSC) is a crucial sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspects in a sentence as positive, negative, or neutral. Most existing ABSC methods are based on supervised learning. However, these methods rely heavily on fine-grained labeled training data, which can be scarce in low-resource domains, limiting
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Complexity Analysis of Chinese Text Based on the Construction Grammar Theory and Deep Learning ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-10 Changlin Wu, Changan Wu
Due to the complexity of Chinese and the differences between Chinese and English, the application of Chinese text in the digital field has a certain complexity. Taking Chinese text in Open Relation Extraction (ORE) as the research object, the complexity of Chinese text is analyzed. An extraction system of word vectors based on construction grammar theory and Deep Learning (DL) is constructed to achieve
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Personalized Explainable Recommendations for Self-Attention Collaboration ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-10 Yongfu Zha, Xuanxuan Che, Lina Sun, Yumin Dong
In recommender systems, providing reasonable explanations can enhance users’ comprehension of recommended results. Template-based explainable recommendation heavily relies on pre-defined templates, constraining the expressiveness of generated sentences and resulting in low-quality explanations. Recently, a novel approach was introduced, utilizing embedding representations of items and comments to address
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Automatic Extractive Text Summarization using Multiple Linguistic Features ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-08 Pooja Gupta, Swati Nigam, Rajiv Singh
Automatic text summarization (ATS) provides a summary of distinct categories of information using natural language processing (NLP). Low-resource languages like Hindi have restricted applications of these techniques. This study proposes a method for automatically generating summaries of Hindi documents using extractive technique. The approach retrieves pertinent sentences from the source documents
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Performance of Binarization Algorithms on Tamizhi Inscription Images: An Analysis ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-08 Monisha Munivel, V S Felix Enigo
Binarization of Tamizhi (Tamil-Brahmi) inscription images are highly challenging as it is captured from very old stone inscriptions that exists around 3rd century BCE in India. The difficulty is due to the degradation of these inscriptions by environmental factors and human negligence over ages. Though many works have been carried out in the binarization of inscription images, very few research was
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SUSTEM: An Improved Rule-Based Sundanese Stemmer ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-05 Irwan Setiawan, Hung-Yu Kao
Current Sundanese stemmers either ignore reduplication words or define rules to handle only affixes. There is a significant amount of reduplication words in the Sundanese language. Because of that, it is impossible to achieve superior stemming precision in the Sundanese language without addressing reduplication words. This paper presents an improved stemmer for the Sundanese language, which handles
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MIMIC: Misogyny Identification in Multimodal Internet Content in Hindi-English Code-Mixed Language ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-04 Aakash Singh, Deepawali Sharma, Vivek Kumar Singh
Over the years, social media has emerged as one of the most popular platforms where people express their views and share thoughts about various aspects. The social media content now includes a variety of components such as text, images, videos etc. One type of interest is memes, which often combine text and images. It is relevant to mention here that, social media being an unregulated platform, sometimes
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Graph4IUR: Incomplete Utterance Rewriting with Semantic Graph ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-04 Zipeng Gao, Jinke Wang, Tong Xu, Zhefeng Wang, Yu Yang, Jia Su, Enhong Chen
Utterance rewriting aims to identify and supply the omitted information in human conversation, which further enables the downstream task to understand conversations more comprehensively. Recently, sequence edit methods, which leverage the overlap between two sentences, have been widely applied to narrow the search space confronted by the previous linear generation methods. However, these methods ignore
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Student's Emotion Recognition using Multimodality and Deep Learning ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-04-01 M. Kalaiyarasi, B. V. V. Siva Prasad, Janjhyam Venkata Naga Ramesh, Ravindra Kumar Kushwaha, Ruchi Patel, Balajee J
The goal of emotion detection is to find and recognise emotions in text, speech, gestures, facial expressions, and more. This paper proposes an effective multimodal emotion recognition system based on facial expressions, sentence-level text, and voice. Using public datasets, we examine face expression image classification and feature extraction. The Tri-modal fusion is used to integrate the findings
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Part-of-Speech Tagging for low resource languages: Activation function for deep learning network to work with Minimal Training Data ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-30 Diganta Baishya, Rupam Baruah
Numerous natural language processing (NLP) applications exist today, especially for the most commonly spoken languages like English, Chinese, and Spanish. Popular traditional methods like Naive Bayes classifiers, Hidden Markov models, Conditional Random field-based classifiers, and other stochastic methods have contributed to this improvement over the last three decades. Recently, deep learning has
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MRMI-TTS: Multi-reference audios and Mutual Information Driven Zero-shot Voice cloning ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-30 Yiting Chen, Wanting Li, Buzhou Tang
Voice cloning in text-to-speech (TTS) is the process of replicating the voice of a target speaker with limited data. Among various voice cloning techniques, this paper focuses on zero-shot voice cloning. Although existing TTS models can generate high-quality speech for seen speakers, cloning the voice of an unseen speaker remains a challenging task. The key aspect of zero-shot voice cloning is to obtain
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A Novel Pretrained General-Purpose Vision Language Model for the Vietnamese Language ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-30 Vu Dinh Anh, Pham Quang Nhat Minh, Giang Son Tran
Lying in the cross-section of computer vision and natural language processing, vision language models are capable of processing images and text at once. These models are helpful in various tasks: text generation from image and vice versa, image-text retrieval, or visual navigation. Besides building a model trained on a dataset for a task, people also study general-purpose models to utilize many datasets
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Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-30 Queenie Luo, Yung-Sung Chuang
Scholars in the humanities heavily rely on ancient manuscripts to study history, religion, and socio-political structures of the past. Significant efforts have been devoted to digitizing these precious manuscripts using OCR technology. However, most manuscripts have been blemished over the centuries, making it unrealistic for OCR programs to accurately capture faded characters. This work presents the
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Crossing Linguistic Barriers: Authorship Attribution in Sinhala Texts ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-30 Raheem Sarwar, Maneesha Perera, Pin Shen Teh, Raheel Nawaz, Muhammad Umair Hassan
Authorship attribution involves determining the original author of an anonymous text from a pool of potential authors. Author attribution task has applications in several domains, such as plagiarism detection, digital text forensics, and information retrieval. While these applications extend beyond any single language, existing research has predominantly centered on English, posing challenges for application
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Application of Hybrid Image Processing Based on Artificial Intelligence in Interactive English Teaching ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-28 Dou Xin, Cuiping Shi
Primary school English teaching resources play an important role in primary school English teaching. The information age requires that primary school English teaching should strengthen the use of multimedia resources and gradually realize the diversification of teaching content. Expanded reality innovation is a sort of mixture picture handling innovation, which is one of the significant innovations
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Learn More Manchu Words with A New Visual-Language Framework ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-28 Zhiwei, Wang, Siyang, Lu, Xiang, Wei, Run, Su, Yingjun, Qi, Wei, Lu
Manchu language, a minority language of China, is of significant historical and research value. An increasing number of Manchu documents are digitized into image format for better preservation and study. Recently, many researchers focused on identifying Manchu words in digitized documents. In previous approaches, a variety of Manchu words are recognized based on visual cues. However, we notice that
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Syntax-aware Offensive Content Detection in Low-resourced Code-mixed Languages with Continual Pre-training ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-26 Necva Bölücü, Pelin Canbay
Social media is a widely used platform that includes a vast amount of user-generated content, allowing the extraction of information about users’ thoughts from texts. Individuals freely express their thoughts on these platforms, often without constraints, even if the content is offensive or contains hate speech. The identification and removal of offensive content from social media are imperative to
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A Context-enhanced Adaptive Graph Network for Time-sensitive Question Answering ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-22 Jitong Li, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng
Time-sensitive question answering is to answer questions limited to certain timestamps based on the given long document, which mixes abundant temporal events with an explicit or implicit timestamp. While existing models make great progress in answering time-sensitive questions, their performance degrades dramatically when a long distance separates the correct answer from the timestamp mentioned in
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Topic-Aware Masked Attentive Network for Information Cascade Prediction ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-21 Yu Tai, Hongwei Yang, Hui He, Xinglong Wu, Yuanming Shao, Weizhe Zhang, Arun Kumar Sangaiah
Predicting information cascades holds significant practical implications, including applications in public opinion analysis, rumor control, and product recommendation. Existing approaches have generally overlooked the significance of semantic topics in information cascades or disregarded the dissemination relations. Such models are inadequate in capturing the intricate diffusion process within an information
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NPEL: Neural Paired Entity Linking in Web Tables ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-19 Tianxing Wu, Lin Li, Huan Gao, Guilin Qi, Yuxiang Wang, Yuehua Li
This paper studies entity linking (EL) in Web tables, which aims to link the string mentions in table cells to their referent entities in a knowledge base. Two main problems exist in previous studies: 1) contextual information is not well utilized in mention-entity similarity computation; 2) the assumption on entity coherence that all entities in the same row or column are highly related to each other
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Supervised Contrast Learning Text Classification Model Based on Data Quality Augmentation ACM Trans. Asian Low Resour. Lang. Inf. Process. (IF 2.0) Pub Date : 2024-03-19 Liang Wu, Fangfang Zhang, Chao Cheng, Shinan Song
Token-level data augmentation generates text samples by modifying the words of the sentences. However, data that are not easily classified can negatively affect the model. In particular, not considering the role of keywords when performing random augmentation operations on samples may lead to the generation of low-quality supplementary samples. Therefore, we propose a supervised contrast learning text
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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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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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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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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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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