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Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search
Web Search and Data Mining, 2018Zhuyun Dai +3 more
semanticscholar +1 more source
2014
A statistical language model defines a probability distribution over a set of symbol sequences from some finite inventory. An especially simple yet very powerful concept for the formal description of statistical language models is formed by their representation using Markov chains or so-called n-gram models.
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A statistical language model defines a probability distribution over a set of symbol sequences from some finite inventory. An especially simple yet very powerful concept for the formal description of statistical language models is formed by their representation using Markov chains or so-called n-gram models.
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2019
Another idea related to the non-linear construction of n-grams, i.e., using distinct elements or distinct order of their appearance in a text, is the idea of replacing words by their synonyms or by the generalized concepts that correspond to the words according to a certain ontology.
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Another idea related to the non-linear construction of n-grams, i.e., using distinct elements or distinct order of their appearance in a text, is the idea of replacing words by their synonyms or by the generalized concepts that correspond to the words according to a certain ontology.
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Proceedings of the 7th ACM international conference on Web search and data mining, 2014
We propose a Bayesian nonparametric topic model that rep- resents relationships between given labels and the corre- sponding words/phrases, from supervised articles. Unlike existing supervised topic models, our proposal, supervised N-gram topic model (SNT), focuses on both a number of topics and power-law distribution in the word frequencies to extract
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We propose a Bayesian nonparametric topic model that rep- resents relationships between given labels and the corre- sponding words/phrases, from supervised articles. Unlike existing supervised topic models, our proposal, supervised N-gram topic model (SNT), focuses on both a number of topics and power-law distribution in the word frequencies to extract
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Part-of-speech n-gram and word n-gram fused language model
6th European Conference on Speech Communication and Technology, 1999Hirofumi Yamamoto, Yoshinori Sagisaka
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Speech recognition using particle n-grams and content-word n-grams
3rd European Conference on Speech Communication and Technology (Eurospeech 1993), 1993Ryosuke Isotani, Shigeki Sagayama
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Dual MeV gamma-ray and dark matter observatory - GRAMS Project
Astroparticle Physics, 2020Tsuguo Aramaki
exaly
Malware Detection and Classification Based on N-Grams Attribute Similarity
22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 2017Fuyong Zhang, Tiezhu Zhao
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An external plagiarism detection system based on part-of-speech (POS) tag n-grams and word embedding
Expert Systems With Applications, 2022Gönenç Ercan
exaly
What can N-grams learn for malware detection?
International Conference on Malicious and Unwanted Software, 2017Richard Zak +2 more
semanticscholar +1 more source