Results 11 to 20 of about 14,339,869 (296)
, 2017 In language modeling, n-gram models are probabilistic models of text that use some limited amount of history, or word dependencies, where n refers to the number of words that participate in the dependence ...
Liu, Ling +2 more
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Interspeech 2010, 2010 International audienceThis paper describes an extension of the n-gram language model: the similar n-gram language model. The estimation of the probability P(s) of a string s by the classical model of order n is computed using statistics of occurrences of
Gillot, Christian +3 more
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N-gram similarity and distance [PDF]
, 2005 . In many applications, it is necessary to algorithmically quantify the similarity exhibited by two strings composed of symbols from a finite alphabet. Numerous string similarity measures have been proposed. Particularly well-known measures are based are
Grzegorz Kondrak
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The textcat Package for n -Gram Based Text Categorization in R [PDF]
Journal of Statistical Software, 2013 Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach to ...
Kurt Hornik +5 more
doaj +2 more sources
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 ...
Xiaobing Sun 0002, Wei Lu 0011
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N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets
CoRR, 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 ...
Wang Yau Li +7 more
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, 2020 GRAM POSITIVOS, es una revista digital con periodicidad anual, que se plantea como un espacio de divulgaciónsobre avances de tipo académico, de investigación y desarrollo social, del Departamento de Microbiología de la Universidadde Pamplona, en ...
VILLAMIZAR GALLARDO, RAQUEL AMANDA
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Pseudo-Conventional N-Gram Representation of the Discriminative N-Gram Model for LVCSR [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2010 The discriminative n-gram modeling approach re-ranks the N-best hypotheses generated during decoding and can effectively improve the performance of large-vocabulary continuous speech recognition (LVCSR). This work recasts the discriminative n-gram model as a pseudo-conventional n-gram model.
Zhengyu Zhou, Helen M. Meng
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Computing n-gram statistics in MapReduce [PDF]
Proceedings of the 16th International Conference on Extending Database Technology, 2013 Statistics about n-grams (i.e., sequences of contiguous words or other tokens in text documents or other string data) are an important building block in information retrieval and natural language processing. In this work, we study how n-gram statistics, optionally restricted by a maximum n-gram length and minimum collection frequency, can be computed ...
Berberich, K., Bedathur, S.
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Granulocyte-activating mediators (GRAM) [PDF]
, 1987 In the present study we investigated the capability of human epidermal cells to generate granulocyte-activating mediators (GRAM). It could be shown that human epidermal cells as well as an epidermoid carcinoma cell line (A431) produce an epidermal cell ...
Kapp, A. +8 more
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