The Biomedical Entity Normalization (BEN) task aims to align raw, unstructured medical entities to standard entities, thus promoting data coherence and facilitating better downstream medical applications. Recently, prompt learning methods have shown ...

One recent advancement in the field of machine learning is the translation of text into sign language gloss, which is a form of natural language for the deaf community. The work presented is a new method to translate Marathi text to Marathi sign language ...

The study aims to present an in-depth Sentiment Analysis (SA) grounded by the presence of emotions in the speech signals. Nowadays, all kinds of web-based applications ranging from social media platforms and video-sharing sites to e-commerce applications ...

Translation from the mother tongue, including the Tunisian dialect, to modern standard Arabic is a highly significant field in natural language processing due to its wide range of applications and associated benefits. Recently, researchers have shown ...

With the advent of Deep Learning-based Artificial Neural Network models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly ...

In order to capture and integrate the structural features and temporal features contained in social graph and diffusion cascade more effectively, an information diffusion prediction model based on the Transformer and Relational Graph Convolutional Network ...

Mention detection is an important component of the Coreference Resolution (CR) system, where mentions such as name, nominal, and pronominals are identified. These mentions can be purely coreferential mentions or singleton mentions (non-coreferential ...

Human nature is fundamentally driven by the need for interaction and attention, which are fulfilled through various sensory modalities, including hearing, sight, touch, taste, and smell. These senses enable us to perceive, understand, and engage with the ...

Sentiment analysis is a critical task for natural language processing. Much research has been done for high-resource languages such as English and Chinese. However, Tibetan is an extremely low-resource language with less reference information. According ...

Document-level event extraction endeavors to automatically extract structural events from a given document. Many existing approaches focus on modeling entity interactions and decoding these interactions into events, assigning each entity as an event ...

This paper investigates the development and evaluation of machine translation models from Cantonese to English (and backward), where we propose a novel approach to tackle low-resource language translations. Despite recent improvements in Neural Machine ...

Correcting grammatical errors in various language contexts is a crucial and challenging task in the field of natural language processing, commonly referred to as Multilingual Grammatical Error Correction. This paper elaborates the Adversarial Temporal ...

Understanding the expression of emotion and generating appropriate responses are key steps toward constructing emotional, conversational agents. In this paper, we propose a framework for single-turn emotional conversation generation, and there are three ...

Previous methods incorporating knowledge graphs (KGs) into neural machine translation (NMT) adopt a static knowledge utilization strategy, that introduces many useless knowledge triples and makes the useful triples difficult be utilized by NMT. To address ...

Sentiment analysis is a process of dealing with people's opinions, remarks, and comments to extract valuable insights from them. Sentiment analysis can be used for various purposes like market analysis, campaign monitoring, decision-making, etc. In recent ...

Aspect-based Sentiment Analysis (ABSA) comprises numerous subtasks including aspect term extraction (AE), opinion term extraction (OE), opinion pair extraction (PE), and triplet extraction (TE). Current research in Chinese ABSA primarily concentrates on ...

Abstractive Text Summarization (ATS) is a task to create a novel summary by generating fresh sentences incorporating new words or rephrasing the article. It is a complex task as the model needs to understand the semantic similarity between the sentences ...

Abstractive text summarization techniques for Malayalam language is still in its infancy. The lack of benchmarked datasets for this task is one of the constraints in developing and testing good models. Malayalam has seven nominal case forms, two nominal ...

Studying human emotions and feelings is a crucial element in the field of psychology, having significant implications such as evaluating mental health and improving human-computer interactions. Recently, there has been a rise in interest in examining how ...

Document-level Event Extraction (DEE) aims to identify event types and arguments from one document. However, existing methods fail to consider semantic distinctions between multiple mentions of one entity, and ignore dynamic representation of entities ...