A Texture Integrated Deep Neural Network for Semantic Segmentation of Urban Meshes

Yang, Yetao; Tang, Rongkui; Xia, Mengjiao; Zhang, Chen

doi:10.1109/jstars.2023.3276977

Cited by 1 publication

(1 citation statement)

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“…α(s) denotes the prior probability of state s, which can be modeled using a Dirichlet process, and β(s, w t ) denotes the similarity between word t and state s, which can be computed using a neural network [32]- [34]. Specifically, a Bidirectional Long Short-Term Memory network (BiLSTM) [35], [36] can be employed to compute the context vector, followed by a feedforward neural network [37] to calculate the similarity:…”

Section: Proposed Modelmentioning

confidence: 99%

Text Segmentation via Hierarchical Document Attention Model

Wang,

Min

2023

IEEE Access

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With the rapid development of natural language processing technology, text segmentation has become an important task in text processing. However, existing text segmentation methods often perform poorly when faced with long texts and complex structures, requiring a more efficient and accurate approach. In this paper, we propose a new text segmentation method based on the Hierarchical Document Attention (HDA), which automatically identifies and segments different paragraphs in the text by analyzing and weighting the hierarchical structure of the text sequence data. Compared with existing methods, the model has higher accuracy and efficiency, and better supports tasks such as text analysis and information extraction. The main contribution of this paper is the proposal of a text segmentation method based on the HDA, which effectively models text sequences through multi-level attention mechanisms. Experimental verification on public datasets shows that this model exhibits good performance in text segmentation tasks.

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