Deep Neural Network-based Text Segmentation Algorithm

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.

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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, 31(1).
  • ABSTRACT: Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside- Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot filling tasks.

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