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In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans.
Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models, especially in processing long sequences. It is based on the Structured State Space sequence (S4) model. [2] [3] [4]
Panoptic segmentation combines both semantic and instance segmentation. Like semantic segmentation, panoptic segmentation is an approach that identifies, for every pixel, the belonging class. Moreover, like in instance segmentation, panoptic segmentation distinguishes different instances of the same class. [20]
U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]
Find the best similarity between small groups of terms, in a semantic way (i.e. in a context of a knowledge corpus), as for example in multi choice questions MCQ answering model. [6] Expand the feature space of machine learning / text mining systems [7] Analyze word association in text corpus [8]
There is a rich variety of computational models implementing distributional semantics, including latent semantic analysis (LSA), [11] [12] Hyperspace Analogue to Language (HAL), syntax- or dependency-based models, [13] random indexing, semantic folding [14] and various variants of the topic model. [15] Distributional semantic models differ ...
Probabilistic latent semantic indexing (PLSI), an early topic model from Thomas Hofmann in 1999. [5] Latent Dirichlet allocation, a generalization of PLSI developed by David Blei, Andrew Ng, and Michael Jordan in 2002, allowing documents to have a mixture of topics. [6] MALLET, an open-source Java library that implements Pachinko allocation.
ESA is considered by its authors a measure of semantic relatedness (as opposed to semantic similarity). On datasets used to benchmark relatedness of words, ESA outperforms other algorithms, including WordNet semantic similarity measures and skip-gram Neural Network Language Model ( Word2vec ).