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Distinguish Polarity in Bag-of-Words Visualization
Proceedings of the AAAI Conference on Artificial Intelligence, 2017Neural network-based BOW models reveal that word-embedding vectors encode strong semantic regularities. However, such models are insensitive to word polarity. We show that, coupled with simple information such as word spellings, word-embedding vectors can preserve both semantic regularity and conceptual polarity without supervision. We
Yusheng Xie +3 more
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Spatial extensions to bag of visual words
Proceedings of the ACM International Conference on Image and Video Retrieval, 2009The Bag of Visual Words (BoV) paradigm has successfully been applied to image content analysis tasks such as image classification and object detection. The basic BoV approach overlooks spatial descriptor distribution within images. Here we describe spatial extensions to BoV and experimentally compare them in the VOC2007 benchmark image category ...
Ville Viitaniemi, Jorma Laaksonen
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Two improved continuous bag-of-word models
2017 International Joint Conference on Neural Networks (IJCNN), 2017Data representation is a fundamental task in machine learning, which affects the performance of the whole machine learning system. In the past few years, with the rapid development of deep learning, the models for word embedding based on neural networks have brought new inspiration to the research of natural language processing.
Qi Wang, Jungang Xu, Hong Chen, Ben He
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BOWL: Bag of Word Clusters Text Representation Using Word Embeddings
2016The text representation is fundamental for text mining and information retrieval. The Bag Of Words (BOW) and its variants (e.g. TF-IDF) are very basic text representation methods. Although the BOW and TF-IDF are simple and perform well in tasks like classification and clustering, its representation efficiency is extremely low.
Weikang Rui, Kai Xing, Yawei Jia
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Generating Bags of Words from the Sums of Their Word Embeddings
2018Many methods have been proposed to generate sentence vector representations, such as recursive neural networks, latent distributed memory models, and the simple sum of word embeddings (SOWE). However, very few methods demonstrate the ability to reverse the process – recovering sentences from sentence embeddings.
Lyndon White +3 more
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ECG biometrics using bag-of-words models
2015 International Symposium on Signals, Circuits and Systems (ISSCS), 2015The paper identifies the effects of key design elements of bag-of-words models on the classification accuracy of ECG time series. Combinations of distinct encoding procedures, pooling methods, and classification strategies are tested in order to find best scenarios under which performances may be optimized.
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Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012
Hang Li, Jun Xu
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Hang Li, Jun Xu
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Bag-Of-Words Classification of Miniature Illustrations
2010In this paper a system for illuminated manuscripts images analysis is presented. In particular the bag-of-keypoints strategy, commonly adopted for object recognition, image classification and scene recognition, is applied to the classification of automatically extracted miniatures.
GRANA, Costantino +3 more
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Bag of Words approaches for Bioinformatics
2015In recent years, several Pattern Recognition problems have been successfully faced by approaches based on the "bag of words" representation. This representation is particularly appropriate when the pattern is characterized (or assumed to be characterized) by the repetition of basic, "constituting" elements called words.
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Image Bag Generator Based on Bag of Visual Words
Journal of Information and Computational Science, 2013openaire +1 more source