Assessing the semantic similarity between two texts is a central task in many applications, including summarization, intelligent tutoring systems, and software testing. Similarity of texts is typically explored at the level of word, sentence, paragraph, and document. The similarity can be defined quantitatively (e.g. in the form of a normalized value between 0 and 1) and qualitatively in the fo...

This paper demonstrates that a lot of time, cost, and complexities can be saved avoided would otherwise used to label the text data for classification purposes. The AI world realizes importance labelled its use various NLP applications. Here, we have categorized close 6,000 unlabelled samples into five distinct classes. dataset was further multi-class classification. Data labelling task using t...

Short text similarity measurement methods play an important role in many applications within natural language processing. This paper reviews the research literature on short (STS) method with aim to (i) classify and give a broad overview of existing techniques; (ii) find out its strengths weaknesses terms domain independence, requirement semantic knowledge, corpus training data, ability identif...

We study algorithms for approximating pairwise similarity matrices that arise in natural language processing. Generally, computing a matrix n data points requires Omega(n^2) computations. This quadratic scaling is significant bottleneck, especially when similarities are computed via expensive functions, e.g., transformer models. Approximation methods reduce this complexity, often by using small...