Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …

Tuning pre-trained language models (PLMs) with task-specific prompts has been a
promising approach for text classification. Particularly, previous studies suggest that prompt …

Over the last two decades, determining the similarity between words as well as between
their meanings, that is, word senses, has been proven to be of vital importance in the field of …

Feature selection (FS) is an important preprocessing technique for improving the quality of
feature sets in many practical applications. Particle swarm optimization (PSO) has been …

The goal of this paper is to provide an overview of the methods that allow text
representations with a focus on embeddings for text of different lengths, specifically on works …

The advancements of search engines for traditional text documents have enabled the
effective retrieval of massive textual information in a resource-efficient manner. However …

With the development of the social economy, small and medium-sized enterprises (SMEs)
play a vital role in promoting economic development. Multiple local governments in China …

Image content analysis plays a dynamic role in various computer vision applications. These
contents can be either visual (ie color, shape, texture) or the textual (ie text appearing within …

Abstract Language exhibits structure at a wide range of scales, from subwords to words,
sentences, paragraphs, and documents. We propose building models that isolate scale …

Text classification results can be hindered when just the bag-of-words model is used for
representing features, because it ignores word order and senses, which can vary with the …