Results 21 to 30 of about 629,507 (282)
Esteganografía lingüística en lengua española basada en modelo N-gram y ley de Zipf
Arbor: Ciencia, Pensamiento y Cultura, 2014 La esteganografía lingüistica es una ciencia que se aprovecha de la lingüistica computacional para diseñar sistemas útiles en la protección y la privacidad de las comunicaciones digitales y en el marcado digital de textos.
Alfonso Muñoz Muñoz +1 more
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Recursive n-gram hashing is pairwise independent, at best [PDF]
, 2010 Many applications use sequences of n consecutive symbols (n-grams). Hashing these n-grams can be a performance bottleneck. For more speed, recursive hash families compute hash values by updating previous values.
Carter +12 more
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Jurnal Japanedu: Pendidikan dan Pengajaran Bahasa Jepang, 2018 The situations where both native and non-native speakers participate in a conversation are called contact situations. In these situations, both native and non-native speakers make verbal behaviour adjustments to achieve a smooth conversation. Adjustments
Dwiky Yoseph Christopher
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Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features [PDF]
, 2017 The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well.
Gupta, Prakhar +2 more
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Language Modeling with Power Low Rank Ensembles [PDF]
, 2014 We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context.
Dyer, Chris +3 more
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Biophysics and Physicobiology, 2022 Elucidating the principles of sequence–structure relationships of proteins is a long-standing issue in biology. The nature of a short segment of a protein is determined by both the subsequence of the segment itself and its environment.
Ryohei Kondo +2 more
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Implicit n-grams Induced by Recurrence
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022 Although self-attention based models such as Transformers have achieved remarkable successes on natural language processing (NLP) tasks, recent studies reveal that they have limitations on modeling sequential transformations (Hahn, 2020), which may prompt re-examinations of recurrent neural networks (RNNs) that demonstrated impressive results on ...
Sun, Xiaobing, Lu, Wei
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N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets
, 2023 Accurate transcription of proper names and technical terms is particularly important in speech-to-text applications for business conversations. These words, which are essential to understanding the conversation, are often rare and therefore likely to be under-represented in text and audio training data, creating a significant challenge in this domain ...
Li, Wang Yau +7 more
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Auto-Sizing Neural Networks: With Applications to n-gram Language Models [PDF]
, 2015 Neural networks have been shown to improve performance across a range of natural-language tasks. However, designing and training them can be complicated. Frequently, researchers resort to repeated experimentation to pick optimal settings.
Chiang, David, Murray, Kenton
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Features of Distributional Method for Indonesian Word Clustering
JEPIN (Jurnal Edukasi dan Penelitian Informatika), 2019 We described the results of a study to determine the best features for algorithm EWSB (Extended Word Similarity Based). EWSB is a word clustering algorithm that can be used for all languages with a common feature.
Herry Sujaini
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