Deep Neural Network-based Text Segmentation Algorithm: Difference between revisions
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(Created page with "A Deep Neural Network-based Text Segmentation Algorithm is a text segmentation algorithm that employs deep neural networks (to perform text segmentation task)s. * <B>Context:</B> ** It can be trained on large datasets to recognize complex patterns in text for accurate segmentation. ** It can be designed as a Supervised Learning Algorithm, requiring labeled data for training. ** It can be support with techniques such as Transfer Learning and Word...") |
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== References == | == References == | ||
=== 2017 === | |||
* ([[Zhai et al., 2017]]) ⇒ [[Feifei Zhai]], [[Saloni Potdar]], [[Bing Xiang]], and [[Bowen Zhou]]. ([[2017]]). “Neural Models for Sequence Chunking.” In: Proceedings of the AAAI conference on artificial intelligence, vol. 31, no. 1. </s> | |||
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Revision as of 18:45, 22 January 2024
A Deep Neural Network-based Text Segmentation Algorithm is a text segmentation algorithm that employs deep neural networks (to perform text segmentation task)s.
- Context:
- It can be trained on large datasets to recognize complex patterns in text for accurate segmentation.
- It can be designed as a Supervised Learning Algorithm, requiring labeled data for training.
- It can be support with techniques such as Transfer Learning and Word Embeddings.
- ...
- Example(s):
- a Bidirectional Encoder Representations from Transformers (BERT)-based Segmentation Algorithm, which uses a BERT model.
- a Long Short-Term Memory (LSTM)-based Segmentation Algorithm.
- a Convolutional Neural Network-based Text Segmentation Algorithm
- a Recurrent Neural Network-based Text Segmentation Algorithm.
- ...
- Counter-Example(s):
- a Rule-based Text Segmentation Algorithm, which relies on predefined rules rather than learning from data.
- a Frequency-based Text Clustering Algorithm, which groups text based on word frequencies and does not involve deep learning.
- See: Natural Language Processing (NLP), Deep Learning, Text Analytics.
References
2017
- (Zhai et al., 2017) ⇒ Feifei Zhai, Saloni Potdar, Bing Xiang, and Bowen Zhou. (2017). “Neural Models for Sequence Chunking.” In: Proceedings of the AAAI conference on artificial intelligence, vol. 31, no. 1.