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...odel of a [[transmitter]]. Markov sources also occur in [[natural language processing]], where they are used to represent hidden meaning in a text. Given the ou [[Category:Statistical natural language processing]] ...

{{short description|Statistical tool}} In [[machine learning]] and [[natural language processing]], the '''pachinko allocation model (PAM)''' is a [[topic model]]. Topic mo ...

...roblems. It is designed to learn a set of parameters (vector or matrix) by processing all the given training examples one-by-one and updating the parameters acco ...and Semantic Dependency Structures''. Proceedings of Conference on Natural Language Learning (CoNLL), Boulder, 67–72.</ref> looks as follows: ...

'''ELMo''' ('''embeddings from language model''') is a [[word embedding]] method for representing a sequence of wor ...eep contextualized word representation is useful for many natural language processing tasks, such as [[coreference resolution]] and [[polysemy]] resolution. ...

See Chapter B of.<ref>Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2023. All rights reserved. ...|title=Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition |date=2009 |others=James ...

...te=March 2013}} within the natural language processing ([[Natural Language Processing|NLP]]) community. ...e case of generating compressed sentences, rather than simply relying on a language model to retain the most commonly used n-grams in the sentence, constraints ...

...|year=2016 |isbn=978-3-319-47954-5 |location=Cham, Switzerland |pages=177 |language=en}}</ref> is typically applied to text, grouping words into clusters that ...|year=2018 |isbn=978-3-319-73705-8 |location=Cham, Switzerland |pages=66 |language=en}}</ref> ...

...ata permits the manipulation of everyday information in a mathematical and statistical way. This data is often used in financial markets as part of a [[trading st ...alysis and applied to digital texts using elements from [[natural language processing]] and [[machine learning]] such as [[latent semantic analysis]], [[support ...

{{short description|Statistical technique for smoothing categorical data}} {{for|the image processing technique|Laplacian smoothing}} ...

...independent]] of each other. MEMMs find applications in [[natural language processing]], specifically in [[part-of-speech tagging]]<ref>{{Cite conference|last = ...ref>{{Cite journal |title = A maximum entropy approach to natural language processing| ...

...roceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) |location=Doha, Qatar |publisher=Association for Computational Ling ...22 |title=On the validity of pre-trained transformers for natural language processing in the software engineering domain |url=https://ieeexplore.ieee.org/documen ...

...techniques that involves [[predicting]] structured objects, rather than [[Statistical classification|discrete]] or [[Regression analysis|real]] values.<ref>Gökha ...a wide variety of domains including [[bioinformatics]], [[natural language processing]] (NLP), [[speech recognition]], and [[computer vision]]. ...

Discrete mathematics is used in language modeling, including formal grammars, language representation, and historical linguistic trends. ...ural class]]es, and the [[allophonic]] variations of each [[phoneme]] in a language are all examples of applied set theory. Set theory and [[concatenation theo ...

...written in terms of the mean parameters, representing them in terms of the natural parameters is better in the context of dynamic topic models. ...amily]], one solution to this problem is to represent them in terms of the natural parameters, that can assume any real value and can be individually changed. ...

...doi.acm.org/10.1145/355598.362773 "Transition network grammars for natural language analysis"], ''Communications of the ACM'', ''ACM Press'', '''13''':10:591-6 ...Adwait Ratnaparkhi, [http://www.aclweb.org/anthology/P/P98/P98-2177.pdf "''Statistical models for unsupervised prepositional phrase attachment''"], ACL-36: Procee ...

To effectively minimise <math>F_\varepsilon</math>, a natural approach is by [[gradient flow]] ([[steepest descent]]). This means to intr ...dinary continuum Allen–Cahn equation and the graph Allen–Cahn equation are natural counterparts, just replacing ordinary calculus by [[Calculus on finite weig ...

...s://doi.org/10.3758/s13428-021-01711-5 |journal=Behavior Research Methods |language=en |volume=54 |issue=5 |pages=2221–2251 |doi=10.3758/s13428-021-01711-5 |is ...biguation.]" Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004. 2004. ...

...tion|Purely statistical model of language}} {{DISPLAYTITLE:Word ''n''-gram language model}} ...https://dl.acm.org/doi/10.5555/944919.944966 |title=A neural probabilistic language model |first1=Yoshua |last1=Bengio |first2=Réjean |last2=Ducharme |first3=P ...

...hbourhood components analysis''' is a [[supervised learning]] method for [[Statistical classification|classifying]] [[multivariate statistics|multivariate]] data ...here the <math>p_i</math> induced by <math>A</math> are all equal to 1). A natural alternative is the KL-divergence, which induces the following objective fun ...

{{Short description|Automatic analysis of syntactic structure of natural language}} ...he important tasks in [[computational linguistics]] and [[natural language processing]], and has been a subject of research since the mid-20th century with the a ...