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Dissertations / Theses on the topic 'Neural language models'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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1

Lei, Tao Ph D. Massachusetts Institute of Technology. "Interpretable neural models for natural language processing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108990.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 109-119).<br>The success of neural network models often comes at a cost of interpretability. This thesis addresses the problem by providing justifications behind the model's structure and predictions. In the first part of this thesis, we present a class of sequence operations for text processing. The proposed component generalizes from convolution operations and gated aggregations. As justi

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2

Kunz, Jenny. "Neural Language Models with Explicit Coreference Decision." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371827.

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Coreference is an important and frequent concept in any form of discourse, and Coreference Resolution (CR) a widely used task in Natural Language Understanding (NLU). In this thesis, we implement and explore two recent models that include the concept of coreference in Recurrent Neural Network (RNN)-based Language Models (LM). Entity and reference decisions are modeled explicitly in these models using attention mechanisms. Both models learn to save the previously observed entities in a set and to decide if the next token created by the LM is a mention of one of the entities in the set, an entit

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Labeau, Matthieu. "Neural language models : Dealing with large vocabularies." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS313/document.

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Le travail présenté dans cette thèse explore les méthodes pratiques utilisées pour faciliter l'entraînement et améliorer les performances des modèles de langues munis de très grands vocabulaires. La principale limite à l'utilisation des modèles de langue neuronaux est leur coût computationnel: il dépend de la taille du vocabulaire avec laquelle il grandit linéairement. La façon la plus aisée de réduire le temps de calcul de ces modèles reste de limiter la taille du vocabulaire, ce qui est loin d'être satisfaisant pour de nombreuses tâches. La plupart des méthodes existantes pour l'entraînement

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4

Bayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/367784.

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Spoken language systems (SLS) communicate with users in natural language through speech. There are two main problems related to processing the spoken input in SLS. The first one is automatic speech recognition (ASR) which recognizes what the user says. The second one is spoken language understanding (SLU) which understands what the user means. We focus on the language model (LM) component of SLS. LMs constrain the search space that is used in the search for the best hypothesis. Therefore, they play a crucial role in the performance of SLS. It has long been discussed that an improvement in the

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Bayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1578/1/bayer_thesis.pdf.

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Spoken language systems (SLS) communicate with users in natural language through speech. There are two main problems related to processing the spoken input in SLS. The first one is automatic speech recognition (ASR) which recognizes what the user says. The second one is spoken language understanding (SLU) which understands what the user means. We focus on the language model (LM) component of SLS. LMs constrain the search space that is used in the search for the best hypothesis. Therefore, they play a crucial role in the performance of SLS. It has long been discussed that an improvement in the

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6

Li, Zhongliang. "Slim Embedding Layers for Recurrent Neural Language Models." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1531950458646138.

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7

Gangireddy, Siva Reddy. "Recurrent neural network language models for automatic speech recognition." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28990.

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The goal of this thesis is to advance the use of recurrent neural network language models (RNNLMs) for large vocabulary continuous speech recognition (LVCSR). RNNLMs are currently state-of-the-art and shown to consistently reduce the word error rates (WERs) of LVCSR tasks when compared to other language models. In this thesis we propose various advances to RNNLMs. The advances are: improved learning procedures for RNNLMs, enhancing the context, and adaptation of RNNLMs. We learned better parameters by a novel pre-training approach and enhanced the context using prosody and syntactic features.

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8

Scarcella, Alessandro. "Recurrent neural network language models in the context of under-resourced South African languages." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29431.

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Over the past five years neural network models have been successful across a range of computational linguistic tasks. However, these triumphs have been concentrated in languages with significant resources such as large datasets. Thus, many languages, which are commonly referred to as under-resourced languages, have received little attention and have yet to benefit from recent advances. This investigation aims to evaluate the implications of recent advances in neural network language modelling techniques for under-resourced South African languages. Rudimentary, single layered recurrent neural n

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Le, Hai Son. "Continuous space models with neural networks in natural language processing." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00776704.

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The purpose of language models is in general to capture and to model regularities of language, thereby capturing morphological, syntactical and distributional properties of word sequences in a given language. They play an important role in many successful applications of Natural Language Processing, such as Automatic Speech Recognition, Machine Translation and Information Extraction. The most successful approaches to date are based on n-gram assumption and the adjustment of statistics from the training data by applying smoothing and back-off techniques, notably Kneser-Ney technique, introduced

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10

Miao, Yishu. "Deep generative models for natural language processing." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258.

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Deep generative models are essential to Natural Language Processing (NLP) due to their outstanding ability to use unlabelled data, to incorporate abundant linguistic features, and to learn interpretable dependencies among data. As the structure becomes deeper and more complex, having an effective and efficient inference method becomes increasingly important. In this thesis, neural variational inference is applied to carry out inference for deep generative models. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here

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11

Sun, Qing. "Greedy Inference Algorithms for Structured and Neural Models." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/81860.

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A number of problems in Computer Vision, Natural Language Processing, and Machine Learning produce structured outputs in high-dimensional space, which makes searching for the global optimal solution extremely expensive. Thus, greedy algorithms, making trade-offs between precision and efficiency, are widely used. %Unfortunately, they in general lack theoretical guarantees. In this thesis, we prove that greedy algorithms are effective and efficient to search for multiple top-scoring hypotheses from structured (neural) models: 1) Entropy estimation. We aim to find deterministic samples that are

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12

Hedström, Simon. "General Purpose Vector Representation for Swedish Documents : An application of Neural Language Models." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160109.

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This thesis is a proof-of-concept for embedding Swedish documents using continuous vectors. These vectors can be used as input in any subsequent task and serves as an alternative to discrete bag of words vectors. The differences goes beyond fewer dimensions as the continuous vectors also hold contextual information. This means that documents with no shared vocabulary can be directly identified as contextually similar, which is impossible for the bag of words vectors. The continuous vectors are the result of neural language models and algorithms that pool the model output into document-level re

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13

Parthiban, Dwarak Govind. "On the Softmax Bottleneck of Word-Level Recurrent Language Models." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41412.

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For different input contexts (sequence of previous words), to predict the next word, a neural word-level language model outputs a probability distribution over all the words in the vocabulary using a softmax function. When the log of probability outputs for all such contexts are stacked together, the resulting matrix is a log probability matrix which can be denoted as Q_theta, where theta denotes the model parameters. When language modeling is formulated as a matrix factorization problem, the matrix to be factorized Q_theta is expected to be high-rank as natural language is highly context-depe

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14

Kamper, Herman. "Unsupervised neural and Bayesian models for zero-resource speech processing." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25432.

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Zero-resource speech processing is a growing research area which aims to develop methods that can discover linguistic structure and representations directly from unlabelled speech audio. Such unsupervised methods would allow speech technology to be developed in settings where transcriptions, pronunciation dictionaries, and text for language modelling are not available. Similar methods are required for cognitive models of language acquisition in human infants, and for developing robotic applications that are able to automatically learn language in a novel linguistic environment. There are two c

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Kryściński, Wojciech. "Training Neural Models for Abstractive Text Summarization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236973.

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Abstractive text summarization aims to condense long textual documents into a short, human-readable form while preserving the most important information from the source document. A common approach to training summarization models is by using maximum likelihood estimation with the teacher forcing strategy. Despite its popularity, this method has been shown to yield models with suboptimal performance at inference time. This work examines how using alternative, task-specific training signals affects the performance of summarization models. Two novel training signals are proposed and evaluated as

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16

Wen, Tsung-Hsien. "Recurrent neural network language generation for dialogue systems." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275648.

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Language is the principal medium for ideas, while dialogue is the most natural and effective way for humans to interact with and access information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses without the natural variation of human language. However, the frequent repetition of identical output forms can quickly make dialogue become tedious for most real-world users.

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17

Pasquiou, Alexandre. "Deciphering the neural bases of language comprehension using latent linguistic representations." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG041.

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Au cours des dernières décennies, les modèles de langage (MLs) ont atteint des performances équivalentes à celles de l'homme sur plusieurs tâches. Ces modèles peuvent générer des représentations vectorielles qui capturent diverses propriétés linguistiques des mots d'un texte, telles que la sémantique ou la syntaxe. Les neuroscientifiques ont donc mis à profit ces progrès et ont commencé à utiliser ces modèles pour explorer les bases neurales de la compréhension du langage. Plus précisément, les représentations des ML calculées à partir d'une histoire sont utilisées pour modéliser les données c

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18

Rossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic sear

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Brorson, Erik. "Classifying Hate Speech using Fine-tuned Language Models." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352637.

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Given the explosion in the size of social media, the amount of hate speech is also growing. To efficiently combat this issue we need reliable and scalable machine learning models. Current solutions rely on crowdsourced datasets that are limited in size, or using training data from self-identified hateful communities, that lacks specificity. In this thesis we introduce a novel semi-supervised modelling strategy. It is first trained on the freely available data from the hateful communities and then fine-tuned to classify hateful tweets from crowdsourced annotated datasets. We show that our model

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20

Chen, Charles L. "Neural Network Models for Tasks in Open-Domain and Closed-Domain Question Answering." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1578592581367428.

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21

Siniša, Suzić. "Parametarska sinteza ekspresivnog govora." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=110631&source=NDLTD&language=en.

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U disertaciji su opisani postupci sinteze ekspresivnog govorakorišćenjem parametarskih pristupa. Pokazano je da se korišćenjemdubokih neuronskih mreža dobijaju bolji rezultati nego korišćenjemskrivenix Markovljevih modela. Predložene su tri nove metode zasintezu ekspresivnog govora korišćenjem dubokih neuronskih mreža:metoda kodova stila, metoda dodatne obuke mreže i arhitekturazasnovana na deljenim skrivenim slojevima. Pokazano je da se najboljirezultati dobijaju korišćenjem metode kodova stila. Takođe jepredložana i nova metoda za transplantaciju emocija/stilovabazirana na deljenim skrivenim

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22

Fancellu, Federico. "Computational models for multilingual negation scope detection." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33038.

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Negation is a common property of languages, in that there are few languages, if any, that lack means to revert the truth-value of a statement. A challenge to cross-lingual studies of negation lies in the fact that languages encode and use it in different ways. Although this variation has been extensively researched in linguistics, little has been done in automated language processing. In particular, we lack computational models of processing negation that can be generalized across language. We even lack knowledge of what the development of such models would require. These models however exist

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23

Zamora, Martínez Francisco Julián. "Aportaciones al modelado conexionista de lenguaje y su aplicación al reconocimiento de secuencias y traducción automática." Doctoral thesis, Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/18066.

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El procesamiento del lenguaje natural es un área de aplicación de la inteligencia artificial, en particular, del reconocimiento de formas que estudia, entre otras cosas, incorporar información sintáctica (modelo de lenguaje) sobre cómo deben juntarse las palabras de una determinada lengua, para así permitir a los sistemas de reconocimiento/traducción decidir cual es la mejor hipótesis �con sentido común�. Es un área muy amplia, y este trabajo se centra únicamente en la parte relacionada con el modelado de lenguaje y su aplicación a diversas tareas: reconocimiento de secuencias mediante modelos

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VENTURA, FRANCESCO. "Explaining black-box deep neural models' predictions, behaviors, and performances through the unsupervised mining of their inner knowledge." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2912972.

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Wenestam, Arvid. "Labelling factual information in legal cases using fine-tuned BERT models." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447230.

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Labelling factual information on the token level in legal cases requires legal expertise and is time-consuming. This thesis proposes transfer-learning and fine-tuning implementation of pre-trained state-of-the-art BERT models to perform this labelling task. Investigations are done to compare whether models pre-trained on solely legal corpus outperforms a generic corps trained BERT and the model’s behaviour as the number of cases in the training sample varies. This work showed that the models metric scores are stable and on par using 40-60 professionally annotated cases as opposed to using the

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Pontes, Miranda James William. "Federation of heterogeneous models with machine learning-assisted model views." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2025. http://www.theses.fr/2025IMTA0454.

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L’Ingénierie Dirigée par les Modèles (IDM) promeut les modèles comme un élément clé pour répondre à la complexité croissante du cycle de vie des systèmes logiciel. L’ingénierie de systèmes avec l’IDM implique divers modèles représentant différentes aspects du système. Cette hétérogénéité nécessite des capacités de fédération de modèles pour intégrer des points de vue spécifiques à de multiples domaines. Les solutions de Vues sur les Modèles (Model Views) répondent à ce défi mais manquent encore de support à l’automatisation. Cette thèse explore l’intégration de l’Apprentissage Automatique (AA)

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Callin, Jimmy. "Word Representations and Machine Learning Models for Implicit Sense Classification in Shallow Discourse Parsing." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325876.

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CoNLL 2015 featured a shared task on shallow discourse parsing. In 2016, the efforts continued with an increasing focus on sense classification. In the case of implicit sense classification, there was an interesting mix of traditional and modern machine learning classifiers using word representation models. In this thesis, we explore the performance of a number of these models, and investigate how they perform using a variety of word representation models. We show that there are large performance differences between word representation models for certain machine learning classifiers, while oth

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Das, Manirupa. "Neural Methods Towards Concept Discovery from Text via Knowledge Transfer." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572387318988274.

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Andruccioli, Matteo. "Previsione del Successo di Prodotti di Moda Prima della Commercializzazione: un Nuovo Dataset e Modello di Vision-Language Transformer." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24956/.

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A differenza di quanto avviene nel commercio tradizionale, in quello online il cliente non ha la possibilità di toccare con mano o provare il prodotto. La decisione di acquisto viene maturata in base ai dati messi a disposizione dal venditore attraverso titolo, descrizioni, immagini e alle recensioni di clienti precedenti. É quindi possibile prevedere quanto un prodotto venderà sulla base di queste informazioni. La maggior parte delle soluzioni attualmente presenti in letteratura effettua previsioni basandosi sulle recensioni, oppure analizzando il linguaggio usato nelle descrizioni per capire

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Azeraf, Elie. "Classification avec des modèles probabilistes génératifs et des réseaux de neurones. Applications au traitement des langues naturelles." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. https://theses.hal.science/tel-03880848.

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Un nombre important de modèles probabilistes connaissent une grande perte d'intérêt pour la classification avec apprentissage supervisé depuis un certain nombre d'années, tels que le Naive Bayes ou la chaîne de Markov cachée. Ces modèles, qualifiés de génératifs, sont critiqués car leur classificateur induit doit prendre en compte la loi des observations, qui peut s'avérer très complexe à apprendre quand le nombre de features de ces derniers est élevé. C'est notamment le cas en Traitement des Langues Naturelles, où les récents algorithmes convertissent des mots en vecteurs numériques de grande

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Gorana, Mijatović. "Dekompozicija neuralne aktivnosti: model za empirijsku karakterizaciju inter-spajk intervala." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=107498&source=NDLTD&language=en.

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Disertacija se se bavi analizom mogućnosti brze, efikasnei pouzdane klasterizacije masivnog skupa neuralnihsnimaka na osnovu probabilističkih parametara procenjenihiz obrazaca generisanja akcionih potencijala, tzv."spajkova", na izlazu pojedinih neurona. Neuralnaaktivnost se grubo može podeliti na periode intezivne,umerene i niske aktivnosti. Shodno tome, predložena jegruba dekompozicija neuralne aktivnosti na tri moda kojaodgovaraju navedenim obrascima neuralne aktivnosti, naosnovu dobro poznatog Gilbert-Eliot modela. Modovi sudodatno ra&scaron;članjeni na sopstvena stanja na osnovu

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Korger, Christina. "Clustering of Distributed Word Representations and its Applicability for Enterprise Search." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-208869.

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Machine learning of distributed word representations with neural embeddings is a state-of-the-art approach to modelling semantic relationships hidden in natural language. The thesis “Clustering of Distributed Word Representations and its Applicability for Enterprise Search” covers different aspects of how such a model can be applied to knowledge management in enterprises. A review of distributed word representations and related language modelling techniques, combined with an overview of applicable clustering algorithms, constitutes the basis for practical studies. The latter have two goals: fi

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Rolnic, Sergiu Gabriel. "Anonimizzazione di documenti mediante Named Entity Recognition e Neural Language Model." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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I transformers hanno rivoluzionato il mondo dell'interpretazione linguistica da parte delle macchine. La possibilità di addestrare un neural language model su vocabolari ed enciclopedie intere, per poi utilizzare le conoscenze acquisite e trasmetterle a task specifici, ha permesso di raggiungere lo stato dell'arte in quasi tutti i domini applicativi del Natural Language Processing. In questo contesto è stato sviluppato un applicativo per l'anonimizzazione di file, in grado di identificare entità specifiche rappresentative di dati personali.

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Menad, Safaa. "Enrichissement et alignement sémantique d'οntοlοgies biοmédicales par mοdèles de langue". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR104.

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La première partie de cette thèse traite de la conception de modèles neuronaux siamois entraînés pour la similarité sémantique entre textes biomédicaux et de leur application à des tâches de TAL sur des documents biomédicaux. L’entraînement de ces modèles a été réalisé en plongeant les titres et résumés du corpus PubMed avec le thésaurus MeSH dans un même espace de représentation. Dans la seconde partie nous utilisons ces modèles pour aligner et enrichir les terminologies de l’UMLS (Unified Medical Language System) et automatiser l’intégration de nouvelles relations entre concepts similaires p

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BIANCHI, FEDERICO. "Corpus-based Comparison of Distributional Models of Language and Knowledge Graphs." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/263553.

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L'intelligenza artificiale cerca di spiegare come gli agenti intelligenti si comportano. Il linguaggio è uno dei media di comunicazioni più importanti e studiare delle teorie che permettano di definire il significato di espressioni naturali è molto importante. I linguisti hanno usato con successo linguaggi artificiali basati su logiche, ma una theory che ha avuto un impatto significativo in intelligenza artificiale è la semantica distribuzionale. La semantica distribuzionale afferma che il significato di espressioni in linguaggio naturale può essere derivato dal contesto in cui tali espressio

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Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

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Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character

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Oota, Subba Reddy. "Modèles neurocomputationnels de la compréhension du langage : caractérisation des similarités et des différences entre le traitement cérébral du langage et les modèles de langage." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0080.

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Cette thèse explore la synergie entre l'intelligence artificielle (IA) et la neuroscience cognitive pour faire progresser les capacités de traitement du langage. Elle s'appuie sur l'idée que les avancées en IA, telles que les réseaux neuronaux convolutionnels et des mécanismes comme le « replay d'expérience », s'inspirent souvent des découvertes neuroscientifiques. Cette interconnexion est bénéfique dans le domaine du langage, où une compréhension plus profonde des capacités cognitives humaines uniques, telles que le traitement de structures linguistiques complexes, peut ouvrir la voie à des s

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Garagnani, Max. "Understanding language and attention : brain-based model and neurophysiological experiments." Thesis, University of Cambridge, 2009. https://www.repository.cam.ac.uk/handle/1810/243852.

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This work concerns the investigation of the neuronal mechanisms at the basis of language acquisition and processing, and the complex interactions of language and attention processes in the human brain. In particular, this research was motivated by two sets of existing neurophysiological data which cannot be reconciled on the basis of current psycholinguistic accounts: on the one hand, the N400, a robust index of lexico-semantic processing which emerges at around 400ms after stimulus onset in attention demanding tasks and is larger for senseless materials (meaningless pseudowords) than for matc

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Al-Kadhimi, Staffan, and Paul Löwenström. "Identification of machine-generated reviews : 1D CNN applied on the GPT-2 neural language model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280335.

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With recent advances in machine learning, computers are able to create more convincing text, creating a concern for an increase in fake information on the internet. At the same time, researchers are creating tools for detecting computer-generated text. Researchers have been able to exploit flaws in neural language models and use them against themselves; for example, GLTR provides human users with a visual representation of texts that assists in classification as human-written or machine-generated. By training a convolutional neural network (CNN) on GLTR output data from analysis of machine-gen

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Cavallucci, Martina. "Speech Recognition per l'italiano: Sviluppo e Sperimentazione di Soluzioni Neurali con Language Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Le e-mail e i servizi di messaggistica hanno cambiato significativamente la comunicazione umana, ma la parola è ancora il metodo più importante di comunicazione tra esseri umani. Pertanto, il riconoscimento vocale automatico (ASR) è di particolare rilevanza perché fornisce una trascrizione della lingua parlata che può essere valutata da sistemi automatizzati. Con altoparlanti intelligenti come Google Home, Alexa o Siri, l' ASR è già un parte integrante di molte famiglie ed è usato per suonare musica, rispondere alle domande o controllare altri dispositivi intelligenti come un sistema di do

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Roos, Magnus. "Speech Comprehension : Theoretical approaches and neural correlates." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11240.

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This review has examined the spatial and temporal neural activation of speech comprehension. Six theories on speech comprehension were selected and reviewed. The most fundamental structures for speech comprehension are the superior temporal gyrus, the fusiform gyrus, the temporal pole, the temporoparietal junction, and the inferior frontal gyrus. Considering temporal aspects of processes, the N400 ERP effect indicates semantic violations, and the P600 indicates re-evaluation of a word due to ambiguity or syntax error. The dual-route processing model provides the most accurate account of neural

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Souza, Cristiano Roberto de. "Modelos para previsão do risco de crédito." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259123.

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Orientador: Gilmar Barreto<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-15T23:37:59Z (GMT). No. of bitstreams: 1 Souza_CristianoRobertode_M.pdf: 1062354 bytes, checksum: 8217be7daba7d7fd194700fdacfc5b03 (MD5) Previous issue date: 2010<br>Resumo: Os modelos computacionais para previsão do risco financeiro têm ganhado grande importância desde 1970. Com a atual crise financeira os governos tem discutido formas de regular o setor financeiro e a mais conhecida e adotada é a de Basiléia I e II

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Helwe, Chadi. "Evaluating and Improving the Reasoning Abilities of Language Models." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT021.

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Cette thèse examine les capacités de raisonnement des Petits Modèles de Langage (SLMs) et Grands Modèles de Langage (LLMs) et expose leurs limites. Elle présente LogiTorch, une bibliothèque Python facilitant l’entraînement de modèles sur diverses tâches de raisonnement. La thèse inclut également TINA, une technique d’augmentation de données qui renforce la robustesse des SLMs face à la négation dans les tâches d’implication textuelle. De plus, la thèse explore les capacités des LLMs avec MAFALDA, un nouveau benchmark pour la classification des sophismes, intégrant une métrique d’évaluation qui

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Gennari, Riccardo. "End-to-end Deep Metric Learning con Vision-Language Model per il Fashion Image Captioning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25772/.

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L'image captioning è un task di machine learning che consiste nella generazione di una didascalia, o caption, che descriva le caratteristiche di un'immagine data in input. Questo può essere applicato, ad esempio, per descrivere in dettaglio i prodotti in vendita su un sito di e-commerce, migliorando l'accessibilità del sito web e permettendo un acquisto più consapevole ai clienti con difficoltà visive. La generazione di descrizioni accurate per gli articoli di moda online è importante non solo per migliorare le esperienze di acquisto dei clienti, ma anche per aumentare le vendite online. Oltre

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Lombardini, Alessandro. "Estrazione di Correlazioni Medicali da Social Post non Etichettati con Language Model Neurali e Data Clustering." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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La progressiva informatizzazione della società a cui il mondo contemporaneo sta assistendo, ha generato un radicale cambiamento nelle abitudini delle persone, le quali oggi giorno trascorrono sempre più tempo online e creano reti di conoscenza prima inimmaginabili. Tale cambiamento ha coinvolto, nel suo avanzare, anche gli individui affetti da malattie di varia natura. In particolare, la scarsa disponibilità di informazioni che caratterizza alcuni contesti medici, unita al bisogno di dialogare con altre persone aventi la medesima problematica, ha determinato negli ultimi anni una forte crescit

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Bojan, Batinić. "Model za predviđanje količine ambalažnog i biorazgradivog otpada primenom neuronskih mreža." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. http://www.cris.uns.ac.rs/record.jsf?recordId=94084&source=NDLTD&language=en.

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U okviru disertacije, kori&scaron;ćenjem ve&scaron;tačkih neuronskih mreža razvijeni su modeli za predviđanje količina ambalažnog i biorazgradivog komunalnog otpada u Republici Srbiji do kraja 2030. godine. Razvoj modela baziran je na zavisnosti između ukupne potro&scaron;nje domaćinstva i generisane količine dva posmatrana toka otpada. Pored toga, na bazi zavisnosti sa bruto domaćim proizvodom (BDP), definisan je i model za projekciju zastupljenosti osnovnih opcija tretmana komunalnog otpada u Republici Srbiji za isti period. Na osnovu dobijenih rezultata, stvorene su polazne osnove za procen

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Galdo, Carlos, and Teddy Chavez. "Prototyputveckling för skalbar motor med förståelse för naturligt språk." Thesis, KTH, Hälsoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223350.

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Förståelse för naturligt språk, språk som har utvecklats av människan ex. talspråk eller teckenspråk, är en del av språkteknik. Det är ett brett ämnesområde där utvecklingen har gått fram i snabb takt senaste 20 åren. En bidragande faktor till denna utveckling är framgångarna med neurala nätverk som är en matematisk modell inspirerad av biologiska hjärnor. Förståelse för naturligt språk används inom många områden där det krävs att applikationer förstår innebörden av textinmatning. Exempel på applikationer som använder förståelse för naturligt språk är Google translate, Googles sökmotor och rät

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Zarrinkoub, Sahand. "Transfer Learning in Deep Structured Semantic Models for Information Retrieval." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286310.

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Recent approaches to IR include neural networks that generate query and document vector representations. The representations are used as the basis for document retrieval and are able to encode semantic features if trained on large datasets, an ability that sets them apart from classical IR approaches such as TF-IDF. However, the datasets necessary to train these networks are not available to the owners of most search services used today, since they are not used by enough users. Thus, methods for enabling the use of neural IR models in data-poor environments are of interest. In this work, a bag

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Prencipe, Michele Pio. "Elaborazione del Linguaggio Naturale con Metodi Probabilistici e Reti Neurali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24312/.

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L'elaborazione del linguaggio naturale (NLP) è il processo per il quale la macchina tenta di imparare le informazioni del parlato o dello scritto tipico dell'essere umano. La procedura è resa particolarmente complessa dalle numerose ambiguità tipiche della lingua o del testo: ironia, metafore, errori ortografici e così via. Grazie all'apprendimento profondo, il Deep Learning, che ha permesso lo sviluppo delle reti neurali, si è raggiunto lo stato dell'arte nell'ambito NLP, tramite l'introduzione di architetture quali Encoder-Decoder, Transformers o meccanismi di attenzione. Le reti neurali

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Hubková, Helena. "Named-entity recognition in Czech historical texts : Using a CNN-BiLSTM neural network model." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385682.

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The thesis presents named-entity recognition in Czech historical newspapers from Modern Access to Historical Sources Project. Our goal was to create a specific corpus and annotation manual for the project and evaluate neural networks methods for named-entity recognition within the task. We created the corpus using scanned Czech historical newspapers. The scanned pages were converted to digitize text by optical character recognition (OCR) method. The data were preprocessed by deleting some OCR errors. We also defined specific named entities types for our task and created an annotation manual wi

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