Results 11 to 20 of about 28,490,308 (324)
Early Modern Digital Review, 2022 This is a review of Collocations and N-grams.
Freebury-Jones, Darren
core +4 more sources
Keywords in Context (Using n-grams) with Python
The Programming Historian, 2012 Like in Output Data as HTML File, this lesson takes the frequency pairs collected in Counting Frequencies and outputs them in HTML. This time the focus is on keywords in context (KWIC) which creates n-grams from the original document content – in this ...
William J. Turkel, Adam Crymble
doaj +1 more source
Scientometrics, 2000 N-grams are generalized words consisting of N consecutive symbols, as they are used in a text. This paper determines the rank-frequency distribution for redundant N-grams. For entire texts this is known to be Zipf's law (i.e., an inverse power law).
EGGHE, Leo
openaire +4 more sources
, 2018 A collection of n-grams extracted from the IMP corpus of historical Slovene (cf. https://nl.ijs.si/imp/). Three sets of n-gram lists are provided for lowercased word n-grams of length 1 to 5: - extensive frequency lists of all extracted n-grams ...
Dobrovoljc, Kaja
core +8 more sources
N-Grammer: Augmenting Transformers with latent n-grams [PDF]
arXiv.org, 2022 Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models. However, the training and inference costs of these
Aurko Roy +14 more
semanticscholar +1 more source
The subjective frequency of word n-grams [PDF]
Psihologija, 2013 When asked to think about the subjective frequency of an n-gram (a group of n words), what properties of the n-gram influence the respondent? It has been recently shown that n-grams that occurred more frequently in a large corpus of English were
Shaoul Cyrus +2 more
doaj +1 more source
Relation Extraction with Word Graphs from N-grams
Conference on Empirical Methods in Natural Language Processing, 2021 Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality ...
Han Qin, Yuanhe Tian, Yan Song
semanticscholar +1 more source
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
openaire +3 more sources
Research on N-grams feature selection methods for text classification
IOP Conference Series: Materials Science and Engineering, 2021 Text classification requires previously extraction of features describing the text documents in the collection. Usually these features are based on the occurrence frequency of words, N-grams of words in documents, i.e. the vector space model for document
T. Georgieva-Trifonova, Mahmut Duraku
semanticscholar +1 more source
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
openaire +2 more sources