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Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing .
Sentence boundary disambiguation (SBD), also known as sentence breaking, sentence boundary detection, and sentence segmentation, is the problem in natural language processing of deciding where sentences begin and end.
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...
Microsoft Word - bases for segmentation.docx; Author: Home: Software used: PScript5.dll Version 5.2.2: File change date and time: 03:48, 30 November 2016: Date and time of digitizing: 03:48, 30 November 2016: Conversion program: Acrobat Distiller 10.1.10 (Windows) Encrypted: no: Page size: 612 x 792 pts (letter) Version of PDF format: 1.5
GrowCut is an interactive segmentation algorithm. It uses Cellular Automaton as an image model. Automata evolution models segmentation process. Each cell of the automata has some label (in case of binary segmentation - 'object', 'background' and 'empty'). During automata evolution some cells capture their neighbours, replacing their labels.
Speech segmentation is the process of identifying the boundaries between words, syllables, or phonemes in spoken natural languages. The term applies both to the mental processes used by humans, and to artificial processes of natural language processing .
o o o s. c: o thO 00 . Created Date: 9/20/2007 3:37:18 PM
By applying medoid-based clustering on the embeddings produced by these models for words, phrases, or sentences, researchers can explore the semantic relationships captured by LLMs. This approach can help identify clusters of semantically similar entities, providing insights into the structure and organization of the high-dimensional embedding ...