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Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
Different text mining methods are used based on their suitability for a data set. Text mining is the process of extracting data from unstructured text and finding patterns or relations. Below is a list of text mining methodologies. Centroid-based Clustering: Unsupervised learning method. Clusters are determined based on data points. [1]
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning.
Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering.
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
Rada Mihalcea is the Janice M. Jenkins Collegiate Professor of Computer Science and Engineering at the University of Michigan.She has made significant contributions to natural language processing, multimodal processing, and computational social science.
Text Sentiment analysis, summarization, classification 2006 [83] [84] J. Schler et al. Social Structure of Facebook Networks Large dataset of the social structure of Facebook. None. 100 colleges covered Text Network analysis, clustering 2012 [85] [86] A. Traud et al. Dataset for the Machine Comprehension of Text