Academic Articles & Research Papers

Instance-Level Weighted Contrast Learning for Text Classification

With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep...

Navigating the Ethical Terrain of AI-Generated Text Tools: A Review

This review examines the ethical, social, and technical challenges posed by AI-generated text tools, focusing on their rapid advancement and widespread adoption. An exhaustive literature search across many databases, strict inclusion/exclus...

Natural Language Processing and Information Retrieval

Unify Framework for Crime Data Summarization using RSS Feed Service

This research presents online crime news analysis using text mining, Natural Language Processing framework (General Framework for Text Mining: GATE), and data warehouse (DW) technologies. The proposed framework aims at extracting key featur...

A systematic literature review on spam content detection and classification

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. ...

A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge

We present a hypothetical argument against finite-state processes in statistical language modeling that is based on semantics rather than syntax. In this theoretical model, we suppose that the semantic properties of texts in a natural langu...

Natural Language Processing

ABSTRACT: Language is way of communicating your words Language helps in understanding the world,we get a better insight of the world. Language helps speakers to be as vague or as precise as they like. NLP Stands for natural language process...

Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these ...

Mention Detection for Coreference Resolution in Polish. Development of the Formal Grammar

Mention Detection for Coreference Resolution in Polish. Development of the Formal Grammar This paper presents the results of an improvement and extension of the Shallow Grammar of Polish, designed for the needs of the Computer-based Method...

Natural Language Processing

Natural language processing (NLP) is the study of mathematical and computational modeling of various aspects of language and the development of a wide range of systems. These include spoken language systems that integrate speech and natural...

SSAM: a span spatial attention model for recognizing named entities

Abstract Mapping a sentence into a two-dimensional (2D) representation can flatten nested semantic structures and build multi-granular span dependencies in named entity recognition. Existing approaches to recognizing named entities often cl...

Natural Language Processing

Natural language processing is widely discussed and researched topic nowadays. As it is one of the oldest area of research in machine learning it is used in major fields such as machine translation speech recognition and text processing. Na...

Shape, Contour and Grouping in Computer Vision

Computer Vision/Computer Graphics CollaborationTechniques

Computer Vision Systems

User‐guided global explanations for deep image recognition: A user study

Abstract We study a user‐guided approach for producing global explanations of deep networks for image recognition. The global explanations are produced with respect to a test data set and give the overall frequency of different “recognition...

Transformer in Computer Vision

Transformer is widely used in Natural Language Processing (NLP), in which numerous papers have been proposed. Recently, the transformer has been borrowed for many computer vision tasks. However, there are few papers to give a comprehensive ...

UPANets: Learning from the Universal Pixel Attention Neworks

With the successful development in computer vision, building a deep convolutional neural network (CNNs) has been mainstream, considering the character of shared parameters in a convolutional layer. Stacking convolutional layers into a deep ...

An empirical study of LLaMA3 quantization: from LLMs to MLLMs

Abstract The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language...

Computer Vision – ACCV 2006