Academic literature on the topic 'Language models'
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Journal articles on the topic "Language models"
Li, Hang. "Language models." Communications of the ACM 65, no. 7 (July 2022): 56–63. http://dx.doi.org/10.1145/3490443.
Full textBegnarovich, Uralov Azamat. "The Inconsistency Of Language Models." American Journal of Social Science and Education Innovations 03, no. 09 (September 30, 2021): 39–44. http://dx.doi.org/10.37547/tajssei/volume03issue09-09.
Full textShimi, G., C. Jerin Mahibha, and Durairaj Thenmozhi. "An Empirical Analysis of Language Detection in Dravidian Languages." Indian Journal Of Science And Technology 17, no. 15 (April 16, 2024): 1515–26. http://dx.doi.org/10.17485/ijst/v17i15.765.
Full textMezzoudj, Freha, and Abdelkader Benyettou. "An empirical study of statistical language models: n-gram language models vs. neural network language models." International Journal of Innovative Computing and Applications 9, no. 4 (2018): 189. http://dx.doi.org/10.1504/ijica.2018.095762.
Full textMezzoudj, Freha, and Abdelkader Benyettou. "An empirical study of statistical language models: n-gram language models vs. neural network language models." International Journal of Innovative Computing and Applications 9, no. 4 (2018): 189. http://dx.doi.org/10.1504/ijica.2018.10016827.
Full textBabb, Robert G. "Language and models." ACM SIGSOFT Software Engineering Notes 13, no. 1 (January 3, 1988): 43–45. http://dx.doi.org/10.1145/43857.43872.
Full textLiu, X., M. J. F. Gales, and P. C. Woodland. "Paraphrastic language models." Computer Speech & Language 28, no. 6 (November 2014): 1298–316. http://dx.doi.org/10.1016/j.csl.2014.04.004.
Full textCerf, Vinton G. "Large Language Models." Communications of the ACM 66, no. 8 (July 25, 2023): 7. http://dx.doi.org/10.1145/3606337.
Full textNederhof, Mark-Jan. "A General Technique to Train Language Models on Language Models." Computational Linguistics 31, no. 2 (June 2005): 173–85. http://dx.doi.org/10.1162/0891201054223986.
Full textVeres, Csaba. "Large Language Models are Not Models of Natural Language: They are Corpus Models." IEEE Access 10 (2022): 61970–79. http://dx.doi.org/10.1109/access.2022.3182505.
Full textDissertations / Theses on the topic "Language models"
Livingstone, Daniel Jack. "Computer models of the evolution of language and languages." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398331.
Full textRyder, Robin Jeremy. "Phylogenetic models of language diversification." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543009.
Full textWaegner, Nicholas Paul. "Stochastic models for language acquisition." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309214.
Full textNiesler, Thomas Richard. "Category-based statistical language models." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627372.
Full textWallach, Hanna Megan. "Structured topic models for language." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612547.
Full textDouzon, Thibault. "Language models for document understanding." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0075.
Full textEvery day, an uncountable amount of documents are received and processed by companies worldwide. In an effort to reduce the cost of processing each document, the largest companies have resorted to document automation technologies. In an ideal world, a document can be automatically processed without any human intervention: its content is read, and information is extracted and forwarded to the relevant service. The state-of-the-art techniques have quickly evolved in the last decades, from rule-based algorithms to statistical models. This thesis focuses on machine learning models for document information extraction. Recent advances in model architecture for natural language processing have shown the importance of the attention mechanism. Transformers have revolutionized the field by generalizing the use of attention and by pushing self-supervised pre-training to the next level. In the first part, we confirm that transformers with appropriate pre-training were able to perform document understanding tasks with high performance. We show that, when used as a token classifier for information extraction, transformers are able to exceptionally efficiently learn the task compared to recurrent networks. Transformers only need a small proportion of the training data to reach close to maximum performance. This highlights the importance of self-supervised pre-training for future fine-tuning. In the following part, we design specialized pre-training tasks, to better prepare the model for specific data distributions such as business documents. By acknowledging the specificities of business documents such as their table structure and their over-representation of numeric figures, we are able to target specific skills useful for the model in its future tasks. We show that those new tasks improve the model's downstream performances, even with small models. Using this pre-training approach, we are able to reach the performances of significantly bigger models without any additional cost during finetuning or inference. Finally, in the last part, we address one drawback of the transformer architecture which is its computational cost when used on long sequences. We show that efficient architectures derived from the classic transformer require fewer resources and perform better on long sequences. However, due to how they approximate the attention computation, efficient models suffer from a small but significant performance drop on short sequences compared to classical architectures. This incentivizes the use of different models depending on the input length and enables concatenating multimodal inputs into a single sequence
Townsend, Duncan Clarke McIntire. "Using a symbolic language parser to Improve Markov language models." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100621.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 31-32).
This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model.
by Duncan Clarke McIntire Townsend.
M. Eng.
Buttery, P. J. "Computational models for first language acquisition." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597195.
Full textNkadimeng, Calvin. "Language identification using Gaussian mixture models." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4170.
Full textENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various algorithms were implemented and tested on raw telephone speech data. These algorithms entailed data preparation, signal processing, and statistical analysis aimed at discriminating between languages. The statistical model of Gaussian Mixture Models (GMMs) were chosen for this research due to their ability to represent an entire language with a single stochastic model that does not require phonetic transcription. Language Identification for African languages using GMMs is feasible, although there are some few challenges like proper classification and accurate study into the relationship of langauges that need to be overcome. Other methods that make use of phonetically transcribed data need to be explored and tested with the new corpus for the research to be more rigorous.
AFRIKAANSE OPSOMMING: Die belang van die Taal identifiseer vir Afrika-tale is sien ’n dramatiese toename te danke aan die ontwikkeling van telekommunikasie-infrastruktuur en as gevolg ’n toename in volumes van data en spraak verkeer in die openbaar netwerke.Deur outomaties verwerking van die ruwe toespraak gegee die noodsaaklike hulp verleen aan mense in nood kan word vinniger-up ”, deur te verwys hul oproepe na ’n persoon ingelichte in daardie taal. Tot hierdie effek van ’n toespraak corpus het ontwikkel en die verskillende algoritmes is gemplementeer en getoets op die ruwe telefoon toespraak gegee.Hierdie algoritmes behels die data voorbereiding, seinverwerking, en statistiese analise wat gerig is op onderskei tussen tale.Die statistiese model van Gauss Mengsel Modelle (GGM) was gekies is vir hierdie navorsing as gevolg van hul vermo te verteenwoordig ’n hele taal met’ n enkele stogastiese model wat nodig nie fonetiese tanscription nie. Taal identifiseer vir die Afrikatale gebruik GGM haalbaar is, alhoewel daar enkele paar uitdagings soos behoorlike klassifikasie en akkurate ondersoek na die verhouding van TALE wat moet oorkom moet word.Ander metodes wat gebruik maak van foneties getranskribeerde data nodig om ondersoek te word en getoets word met die nuwe corpus vir die ondersoek te word strenger.
Schuster, Ingmar. "Probabilistic models of natural language semantics." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-204503.
Full textDiese Dissertation befasst sich mit der Modellierung der Semantik natürlicher Sprache. Eine Übersicht von Neuronalen Netzwerkmodellen wird gegeben und ein eigener Bayesscher Ansatz wird entwickelt und evaluiert. Da die Leistungsfähigkeit von Standardalgorithmen aus der Monte-Carlo-Familie auf dem entwickelten Model unbefriedigend ist, liegt der Hauptfokus der Arbeit auf neuen adaptiven Algorithmen im Rahmen von Sequential Monte Carlo (SMC). Es wird gezeigt, dass der in der Dissertation entwickelte Gradient Importance Sampling (GRIS) Algorithmus sehr leistungsfähig ist im Vergleich zu vielen Algorithmen des adaptiven Markov Chain Monte Carlo (MCMC), wobei komplexe und hochdimensionale Integrationsprobleme herangezogen werden. Ein weiterer Vorteil im Vergleich mit MCMC ist, dass GRIS einen Schätzer der Modelevidenz liefert. Schließlich wird Sample Inflation eingeführt als Ansatz zur Reduktion von Varianz und schnellerem auffinden von Modi in einer Verteilung, wenn Importance Sampling oder SMC verwendet werden. Sample Inflation ist beweisbar konsistent und es wird empirisch gezeigt, dass seine Anwendung die Konvergenz von Integralschätzern verbessert
Books on the topic "Language models"
Stevenson, Rosemary J. Models of language development. Milton Keynes [England]: Open University Press, 1988.
Find full textAnn, Ryan, Wray Alison, and British Association for Applied Linguistics., eds. Evolving models of language. Clevedon, England: British Association for Applied Linguistics in association with Multilingual Matters, 1997.
Find full text1945-, Giora Rachel, ed. Models of figurative language. Hillsdale, N. J: Lawrence Erlbaum Association, 2001.
Find full textAmaratunga, Thimira. Understanding Large Language Models. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7.
Full textMeduna, Alexander, and Ondřej Soukup. Modern Language Models and Computation. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63100-4.
Full textKucharavy, Andrei, Octave Plancherel, Valentin Mulder, Alain Mermoud, and Vincent Lenders, eds. Large Language Models in Cybersecurity. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7.
Full textGoossens, Louis. English modals and functional models: A confrontation. [Wilrijk, Belgium]: Universiteit Antwerpen, Universitaire Instelling Antwerpen, Departement Germaanse, Afd. Linguïstiek, 1996.
Find full textDirven, René, Roslyn Frank, and Martin Pütz, eds. Cognitive Models in Language and Thought. Berlin, Boston: DE GRUYTER, 2003. http://dx.doi.org/10.1515/9783110892901.
Full textLeopold, Henrik, ed. Natural Language in Business Process Models. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-04175-9.
Full text1957-, Kornai András, ed. Extended finite state models of language. Cambridge, UK: Cambridge University Press, 1999.
Find full textBook chapters on the topic "Language models"
Hiemstra, Djoerd. "Language Models." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_923-2.
Full textHiemstra, Djoerd. "Language Models." In Encyclopedia of Database Systems, 1591–94. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_923.
Full textTanaka-Ishii, Kumiko. "Language Models." In Mathematics in Mind, 173–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59377-3_17.
Full textHiemstra, Djoerd. "Language Models." In Encyclopedia of Database Systems, 2061–65. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_923.
Full textTurner, Raymond. "Programming Language Specification." In Computable Models, 1–7. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-052-4_22.
Full textMark, Kevin E., Michael I. Miller, and Ulf Grenander. "Constrained Stochastic Language Models." In Image Models (and their Speech Model Cousins), 131–40. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4056-3_7.
Full textSkansi, Sandro. "Neural Language Models." In Undergraduate Topics in Computer Science, 165–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73004-2_9.
Full textSobernig, Stefan. "Variable Language Models." In Variable Domain-specific Software Languages with DjDSL, 73–136. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42152-6_3.
Full textMcTear, Michael, and Marina Ashurkina. "Large Language Models." In Transforming Conversational AI, 61–84. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_4.
Full textWang, Peng, and David B. Sawyer. "Predicative Language Models." In Machine Learning in Translation, 52–68. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003321538-5.
Full textConference papers on the topic "Language models"
Zaytsev, Vadim. "Language Design with Intent." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.16.
Full textTorroba Hennigen, Lucas, and Yoon Kim. "Deriving Language Models from Masked Language Models." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-short.99.
Full textPerez, Ethan, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. "Red Teaming Language Models with Language Models." In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.emnlp-main.225.
Full textPfeiffer, Jérôme. "Systematic Component-Oriented Language Reuse." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00043.
Full textSchiedermeier, Maximilian, Bowen Li, Ryan Languay, Greta Freitag, Qiutan Wu, Jorg Kienzle, Hyacinth Ali, Ian Gauthier, and Gunter Mussbacher. "Multi-Language Support in TouchCORE." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00096.
Full textBrychcin, Tomas, and Miloslav Konopik. "Morphological based language models for inflectional languages." In 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2011. http://dx.doi.org/10.1109/idaacs.2011.6072829.
Full text"Natural-language Scenario Descriptions for Testing Core Language Models of Domain-Specific Languages." In International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004713703560367.
Full textVara Larsen, Matias Ezequiel, Julien DeAntoni, Benoit Combemale, and Frederic Mallet. "A Behavioral Coordination Operator Language (BCOoL)." In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338249.
Full textSeidewitz, Ed, Arnaud Blouin, and Jérôme Pfeiffer. "Modeling Language Engineering Workshop (MLE 2023)." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00063.
Full textGaudin, Emmanuel, Eric Brunel, and Mihal Brumbulli. "Language Agnostic Model Checking for SDL." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00052.
Full textReports on the topic "Language models"
Seymore, Kristie, and Ronald Rosenfeld. Scalable Trigram Backoff Language Models,. Fort Belvoir, VA: Defense Technical Information Center, May 1996. http://dx.doi.org/10.21236/ada310721.
Full textHacioglu, Kadri, and Wayne Ward. On Combining Language Models: Oracle Approach. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada460991.
Full textLavrenko, Victor. Localized Smoothing for Multinomial Language Models. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada477851.
Full textRaimondo, S., T. Chen, A. Zakharov, L. Brin, D. Kur, J. Hui, S L Burgoyne, G. Newton, and C. J. M. Lawley. Datasets to support geological language models. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329265.
Full textHowland, Scott, Jessica Yaros, and Noriaki Kono. MetaText: Compositional Generalization in Deep Language Models. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1987883.
Full textCullen, Cabot, and Zhehui Wang. Exploration of Language Models for ICF Design. Office of Scientific and Technical Information (OSTI), July 2023. http://dx.doi.org/10.2172/1992244.
Full textBuchanan, Ben, Andrew Lohn, Micah Musser, and Katerina Sedova. Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/2021ca003.
Full textBorders, Tammie, and Svitlana Volkova. An Introduction to Word Embeddings and Language Models. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1773690.
Full textKorinek, Anton. Language Models and Cognitive Automation for Economic Research. Cambridge, MA: National Bureau of Economic Research, February 2023. http://dx.doi.org/10.3386/w30957.
Full textLee, Benjamin Yen Kit. Automated neuron explanation for code-trained language models. Ames (Iowa): Iowa State University, May 2024. http://dx.doi.org/10.31274/cc-20240624-267.
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