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Thèses sur le sujet « NLO Computation »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

LIMATOLA, GIOVANNI. "Infrared Linear Renormalons in Collider Processes." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/402371.

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Al fine di descrivere appropriatamente le osservabili studiate tramite collisori adronici e/o leptonici, risulta cruciale una corretta comprensione delle correzioni non perturbative (che si manifestano come correzioni di potenza), lineari nel rapporto tra una scala di energia non perturbativa e la scala caratteristica del processo. Utilizzando un modello abeliano ricerchiamo tali effetti nella distribuzione in momento trasverso di un bosone Z prodotto con un jet in collisioni adroniche, essendo questa una delle osservabili meglio misurate presso LHC. La presenza delle correzioni non perturbati

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Filali, Karim. "Multi-dynamic Bayesian networks for machine translation and NLP /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6857.

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Westin, Emil. "Fine-grained sentiment analysis of product reviews in Swedish." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424266.

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In this study we gather customer reviews from Prisjakt, a Swedish price comparison site, with the goal to study the relationship between review and rating, known as sentiment analysis. The purpose of the study is to evaluate three different supervised machine learning models on a fine-grained dependent variable representing the review rating. For classification, a binary and multinomial model is used with the one-versus-one strategy implemented in the Support Vector Machine, with a linear kernel, evaluated with F1, accuracy, precision and recall scores. We use Support Vector Regression by appr

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Scheible, Silke. "Computational treatment of superlatives." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4153.

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The use of gradable adjectives and adverbs represents an important means of expressing comparison in English. The grammatical forms of comparatives and superlatives are used to express explicit orderings between objects with respect to the degree to which they possess some gradable property. While comparatives are commonly used to compare two entities (e.g., “The blue whale is larger than an African elephant”), superlatives such as “The blue whale is the largest mammal” are used to express a comparison between a target entity (here, the blue whale) and its comparison set (the set of mammals),

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Rontsch, Raoul Horst. "Higher order QCD corrections to diboson production at hadron colliders." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:5c4c3e7e-5c2a-4878-9fad-d9e5e0535d30.

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Hadronic collider experiments have played a major role in particle physics phenomenology over the last few decades. Data recorded at the Tevatron at Fermilab is still of interest, and its successor, the Large Hadron Collider (LHC) at CERN, has recently announced the discovery of a particle consistent with the Standard Model Higgs boson. Hadronic colliders look set to guide the field for the next fifteen years or more, with the discovery of more particles anticipated. The discovery and detailed study of new particles relies crucially on the availability of high-precision theoretical predictions

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Eisenberg, Joshua Daniel. "Automatic Extraction of Narrative Structure from Long Form Text." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3912.

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Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives. My work has given computers awareness of narrative structure. Specifically, where are the boundar

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Acosta, Andrew D. "Laff-O-Tron: Laugh Prediction in TED Talks." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1667.

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Did you hear where the thesis found its ancestors? They were in the "parent-thesis"! This joke, whether you laughed at it or not, contains a fascinating and mysterious quality: humor. Humor is something so incredibly human that if you squint, the two words can even look the same. As such, humor is not often considered something that computers can understand. But, that doesn't mean we won't try to teach it to them. In this thesis, we propose the system Laff-O-Tron to attempt to predict when the audience of a public speech would laugh by looking only at the text of the speech. To do this, we cre

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Al-Liabi, Majda Majeed. "Computational support for learners of Arabic." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/computational-support-for-learners-of-arabic(abd20b76-3ba2-4e11-8aa5-459ec6d8d7d2).html.

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This thesis documents the use of Natural Language Processing (NLP) in Computer Assisted Language Learning (CALL) and its contribution to the learning experience of students studying Arabic as a foreign language. The goal of this project is to build an Intelligent Computer Assisted Language Learning (ICALL) system that provides computational assistance to learners of Arabic by teaching grammar, producing homework and issuing students with immediate feedback. To produce this system we use the Parasite system, which produces morphological, syntactic and semantic analysis of textual input, and ext

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RE, EMANUELE. "Next - to - leading order qcd corrections to shower Monte Carlo event generators: single vector- boson and single- top hadroproduction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2009. http://hdl.handle.net/10281/7455.

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Lagerkvist, Love. "Computation as Strange Material : Excursions into Critical Accidents." Thesis, Malmö universitet, Institutionen för konst, kultur och kommunikation (K3), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43639.

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Waking up in a world where everyone carries a miniature supercomputer, interaction designers find themselves in their forerunners dreams. Faced with the reality of planetary-scale we have to confront the task of articulating approaches responsive this accidental ubiquity of computation. This thesis attempts such a formulation by defining computation as a strange material, a plasticity shaped equally by its technical properties and the mode of production by which is its continuously re-produced. The definition is applied through a methodology of excursions — participatory explorations into two

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Lager, Adam. "Improving Solr search with Natural Language Processing : An NLP implementation for information retrieval in Solr." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177790.

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The field of AI is emerging fast and institutions and companies are pushing the limits of impossibility. Natural Language Processing is a branch of AI where the goal is to understand human speech and/or text. This technology is used to improve an inverted index,the full text search engine Solr. Solr is open source and has integrated OpenNLP makingit a suitable choice for these kinds of operations. NLP-enabled Solr showed great results compared to the Solr that’s currently running on the systems, where NLP-Solr was slightly worse in terms of precision, it excelled at recall and returning the co

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Neme, Alexis. "An arabic language resource for computational morphology based on the semitic model." Thesis, Paris Est, 2020. http://www.theses.fr/2020PESC2013.

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La morphologie de la langue arabe est riche, complexe, et hautement flexionnelle. Nous avons développé une nouvelle approche pour la morphologie traditionnelle arabe destinés aux traitements automatiques de l’arabe écrit. Cette approche permet de formaliser plus simplement la morphologie sémitique en utilisant Unitex, une suite logicielle fondée sur des ressources lexicales pour l'analyse de corpus. Pour les verbes (Neme, 2011), j’ai proposé une taxonomie flexionnelle qui accroît la lisibilité du lexique et facilite l’encodage, la correction et la mise-à-jour par les locuteurs et linguistes ar

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Ceylan, Yavuz Selim. "Exploration of Transition Metal-Containing Catalytic Cycles via Computational Methods." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505287/.

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Styrene production by a (FlDAB)PdII(TFA)(η2-C2H4) complex was modeled using density functional theory (DFT). Benzene C-H activation by this complex was studied via five mechanisms: oxidative addition/reductive elimination, sigma-bond metathesis, concerted metalation deprotonation (CMD), CMD activation of ethylene, and benzene substitution of ethylene followed by CMD of the ligated benzene. Calculations provided evidence that conversion of benzene and ethylene to styrene was initiated by the fifth pathway, arylation via CMD of coordinated benzene, followed by ethylene insertion into the Ru-Ph b

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Zidi, Mohamed Sadok. "Calcul à une boucle avec plusieurs pattes externes dans les théories de jauge : la bibliothèque Golem95." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENY031/document.

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Les calculs de précision dans les théories de jauge jouent un rôle très important pour l’étude de la physique du Modèle Standard et au-delà dans les super-collisionneurs de particules comme le LHC, TeVatron et ILC. Par conséquent, il est extrêmement important de fournir des outils du calcul d’amplitudes à une boucle stables, rapides, efficaces et hautement automatisés. Cette thèse a pour but de développer la bibliothèque d’intégrales Golem95. Cette bibliothèque est un programme écrit en Fortran95, qui contient tous les ingrédients nécessaires pour calculer une intégrale scalaire ou tensorielle

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Björkman, Desireé. "Machine Learning Evaluation of Natural Language to Computational Thinking : On the possibilities of coding without syntax." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424269.

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Voice commands are used in today's society to offer services like putting events into a calendar, tell you about the weather and to control the lights at home. This project tries to extend the possibilities of voice commands by improving an earlier proof of concept system that interprets intention given in natural language to program code. This improvement was made by mixing linguistic methods and neural networks to increase accuracy and flexibility of the interpretation of input. A user testing phase was made to conclude if the improvement would attract users to the interface. The results sho

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Shokat, Imran. "Computational Analyses of Scientific Publications Using Raw and Manually Curated Data with Applications to Text Visualization." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78995.

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Text visualization is a field dedicated to the visual representation of textual data by using computer technology. A large number of visualization techniques are available, and now it is becoming harder for researchers and practitioners to choose an optimal technique for a particular task among the existing techniques. To overcome this problem, the ISOVIS Group developed an interactive survey browser for text visualization techniques. ISOVIS researchers gathered papers which describe text visualization techniques or tools and categorized them according to a taxonomy. Several categories were ma

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Meechan-Maddon, Ailsa. "The effect of noise in the training of convolutional neural networks for text summarisation." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384607.

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In this thesis, we work towards bridging the gap between two distinct areas: noisy text handling and text summarisation. The overall goal of the paper is to examine the effects of noise in the training of convolutional neural networks for text summarisation, with a view to understanding how to effectively create a noise-robust text-summarisation system. We look specifically at the problem of abstractive text summarisation of noisy data in the context of summarising error-containing documents from automatic speech recognition (ASR) output. We experiment with adding varying levels of noise (erro

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Adewumi, Oluwatosin. "Word Vector Representations using Shallow Neural Networks." Licentiate thesis, Luleå tekniska universitet, EISLAB, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83578.

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This work highlights some important factors for consideration when developing word vector representations and data-driven conversational systems. The neural network methods for creating word embeddings have gained more prominence than their older, count-based counterparts.However, there are still challenges, such as prolonged training time and the need for more data, especially with deep neural networks. Shallow neural networks with lesser depth appear to have the advantage of less complexity, however, they also face challenges, such as sub-optimal combination of hyper-parameters which produce

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Roos, Daniel. "Evaluation of BERT-like models for small scale ad-hoc information retrieval." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177675.

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Measuring semantic similarity between two sentences is an ongoing research field with big leaps being taken every year. This thesis looks at using modern methods of semantic similarity measurement for an ad-hoc information retrieval (IR) system. The main challenge tackled was answering the question "What happens when you don’t have situation-specific data?". Using encoder-based transformer architectures pioneered by Devlin et al., which excel at fine-tuning to situationally specific domains, this thesis shows just how well the presented methodology can work and makes recommendations for future

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Karamolegkou, Antonia. "Argument Mining: Claim Annotation, Identification, Verification." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448855.

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Researchers writing scientific articles summarize their work in the abstracts mentioning the final outcome of their study. Argumentation mining can be used to extract the claim of the researchers as well as the evidence that could support their claim. The rapid growth of scientific articles demands automated tools that could help in the detection and evaluation of the scientific claims’ veracity. However, there are neither a lot of studies focusing on claim identification and verification neither a lot of annotated corpora available to effectively train deep learning models. For this reason, w

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Dinkar, Tanvi. "Computational models of disfluencies : fillers and discourse markers in spoken language understanding." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT001.

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Les gens s'expriment rarement de la même manière qu'ils écrivent - en effet ils écrivent rarement de manière diffluente. Les disfluences sont des interruptions dans le flux régulier de la parole, telles que les pauses (silencieuses), les répétitions de mots ou les interruptions pour corriger une phrase précédemment dite. Bien qu'il s'agisse d'une caractéristique naturelle de la parole spontanée et malgré la riche littérature linguistique qui traite de leur caractère informatif, elles sont souvent considérées comme du bruit et éliminées lors du post-traitement des transcriptions de sortie des s

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Danielsson, Benjamin. "A Study on Text Classification Methods and Text Features." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159992.

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When it comes to the task of classification the data used for training is the most crucial part. It follows that how this data is processed and presented for the classifier plays an equally important role. This thesis attempts to investigate the performance of multiple classifiers depending on the features that are used, the type of classes to classify and the optimization of said classifiers. The classifiers of interest are support-vector machines (SMO) and multilayer perceptron (MLP), the features tested are word vector spaces and text complexity measures, along with principal component anal

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Schneider, Michael J. "A Study on the Efficacy of Sentiment Analysis in Author Attribution." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2538.

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The field of authorship attribution seeks to characterize an author’s writing style well enough to determine whether he or she has written a text of interest. One subfield of authorship attribution, stylometry, seeks to find the necessary literary attributes to quantify an author’s writing style. The research presented here sought to determine the efficacy of sentiment analysis as a new stylometric feature, by comparing its performance in attributing authorship against the performance of traditional stylometric features. Experimentation, with a corpus of sci-fi texts, found sentiment analysis

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Bridal, Olle. "Named-entity recognition with BERT for anonymization of medical records." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176547.

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Sharing data is an important part of the progress of science in many fields. In the largely deep learning dominated field of natural language processing, textual resources are in high demand. In certain domains, such as that of medical records, the sharing of data is limited by ethical and legal restrictions and therefore requires anonymization. The process of manual anonymization is tedious and expensive, thus automated anonymization is of great value. Since medical records consist of unstructured text, pieces of sensitive information have to be identified in order to be masked for anonymizat

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Wang, Run Fen. "Semantic Text Matching Using Convolutional Neural Networks." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362134.

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Semantic text matching is a fundamental task for many applications in NaturalLanguage Processing (NLP). Traditional methods using term frequencyinversedocument frequency (TF-IDF) to match exact words in documentshave one strong drawback which is TF-IDF is unable to capture semanticrelations between closely-related words which will lead to a disappointingmatching result. Neural networks have recently been used for various applicationsin NLP, and achieved state-of-the-art performances on many tasks.Recurrent Neural Networks (RNN) have been tested on text classificationand text matching, but it d

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Woldemariam, Yonas Demeke. "Natural language processing in cross-media analysis." Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-147640.

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A cross-media analysis framework is an integrated multi-modal platform where a media resource containing different types of data such as text, images, audio and video is analyzed with metadata extractors, working jointly to contextualize the media resource. It generally provides cross-media analysis and automatic annotation, metadata publication and storage, searches and recommendation services. For on-line content providers, such services allow them to semantically enhance a media resource with the extracted metadata representing the hidden meanings and make it more efficiently searchable. Wi

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Navér, Norah. "The past, present or future? : A comparative NLP study of Naive Bayes, LSTM and BERT for classifying Swedish sentences based on their tense." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446793.

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Natural language processing is a field in computer science that is becoming increasingly important. One important part of NLP is the ability to sort text to the past, present or future, depending on when the event came or will come about. The objective of this thesis was to use text classification to classify Swedish sentences based on their tense, either past, present or future. Furthermore, the objective was also to compare how lemmatisation would affect the performance of the models. The problem was tackled by implementing three machine learning models on both lemmatised and not lemmatised

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Fang, Yimai. "Proposition-based summarization with a coherence-driven incremental model." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/287468.

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Summarization models which operate on meaning representations of documents have been neglected in the past, although they are a very promising and interesting class of methods for summarization and text understanding. In this thesis, I present one such summarizer, which uses the proposition as its meaning representation. My summarizer is an implementation of Kintsch and van Dijk's model of comprehension, which uses a tree of propositions to represent the working memory. The input document is processed incrementally in iterations. In each iteration, new propositions are connected to the tree un

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Erik, Cambria. "Application of common sense computing for the development of a novel knowledge-based opinion mining engine." Thesis, University of Stirling, 2011. http://hdl.handle.net/1893/6497.

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The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online op

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Johansson, Oskar. "Parafrasidentifiering med maskinklassificerad data : utvärdering av olika metoder." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167039.

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Detta arbete undersöker hur språkmodellen BERT och en MaLSTM-arkitektur fungerar att för att identifiera parafraser ur 'Microsoft Paraphrase Research Corpus' (MPRC) om dessa tränats på automatiskt identifierade parafraser ur 'Paraphrase Database' (PPDB). Metoderna ställs mot varandra för att undersöka vilken som presterar bäst och metoden att träna på maskinklassificerad data för att användas på mänskligt klassificerad data utvärderas i förhållande till annan klassificering av samma dataset. Meningsparen som används för att träna modellerna hämtas från de högst rankade parafraserna ur PPDB och

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Kantzola, Evangelia. "Extractive Text Summarization of Greek News Articles Based on Sentence-Clusters." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420291.

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This thesis introduces an extractive summarization system for Greek news articles based on sentence clustering. The main purpose of the paper is to evaluate the impact of three different types of text representation, Word2Vec embeddings, TF-IDF and LASER embeddings, on the summarization task. By taking these techniques into account, we build three different versions of the initial summarizer. Moreover, we create a new corpus of gold standard summaries to evaluate them against the system summaries. The new collection of reference summaries is merged with a part of the MultiLing Pilot 2011 in or

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Toska, Marsida. "A Rule-Based Normalization System for Greek Noisy User-Generated Text." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424777.

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The ever-growing usage of social media platforms generates daily vast amounts of textual data which could potentially serve as a great source of information. Therefore, mining user-generated data for commercial, academic, or other purposes has already attracted the interest of the research community. However, the informal writing which often characterizes online user-generated texts poses a challenge for automatic text processing with Natural Language Processing (NLP) tools. To mitigate the effect of noise in these texts, lexical normalization has been proposed as a preprocessing method which

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Shockley, Darla Magdalene. "Email Thread Summarization with Conditional Random Fields." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1268159269.

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Monsen, Julius. "Building high-quality datasets for abstractive text summarization : A filtering‐based method applied on Swedish news articles." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176352.

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With an increasing amount of information on the internet, automatic text summarization could potentially make content more readily available for a larger variety of people. Training and evaluating text summarization models require datasets of sufficient size and quality. Today, most such datasets are in English, and for minor languages such as Swedish, it is not easy to obtain corresponding datasets with handwritten summaries. This thesis proposes methods for compiling high-quality datasets suitable for abstractive summarization from a large amount of noisy data through characterization and fi

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Lameris, Harm. "Homograph Disambiguation and Diacritization for Arabic Text-to-Speech Using Neural Networks." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446509.

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Pre-processing Arabic text for Text-to-Speech (TTS) systems poses major challenges, as Arabic omits short vowels in writing. This omission leads to a large number of homographs, and means that Arabic text needs to be diacritized to disambiguate these homographs, in order to be matched up with the intended pronunciation. Diacritizing Arabic has generally been achieved by using rule-based, statistical, or hybrid methods that combine rule-based and statistical methods. Recently, diacritization methods involving deep learning have shown promise in reducing error rates. These deep-learning methods

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Sidås, Albin, and Simon Sandberg. "Conversational Engine for Transportation Systems." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176810.

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Today's communication between operators and professional drivers takes place through direct conversations between the parties. This thesis project explores the possibility to support the operators in classifying the topic of incoming communications and which entities are affected through the use of named entity recognition and topic classifications. By developing a synthetic training dataset, a NER model and a topic classification model was developed and evaluated to achieve F1-scores of 71.4 and 61.8 respectively. These results were explained by a low variance in the synthetic dataset in comp

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Svedberg, Jonatan, and George Shmas. "Effekten av textaugmenteringsstrategier på träffsäkerhet, F1-värde och viktat F1-värde." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296550.

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Att utveckla en sofistikerad chatbotlösning kräver stora mängder textdata för att kunna anpassalösningen till en specifik domän. Att manuellt skapa en komplett uppsättning textdata, specialanpassat för den givna domänen och innehållandes ett stort antal varierande meningar som en människa kan tänkas yttra, är ett enormt tidskrävande arbete. För att kringgå detta tillämpas dataaugmentering för att generera mer data utifrån en mindre uppsättning redan existerande textdata. Softronic AB vill undersöka alternativa strategier för dataaugmentering med målet att eventuellt ersätta den nuvarande lösni

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Cheung, Luciana Montera. "SimAffling um ambiente computacional para suporte e simulação do processo de DNA shuffling." Universidade Federal de São Carlos, 2008. https://repositorio.ufscar.br/handle/ufscar/249.

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Made available in DSpace on 2016-06-02T19:02:39Z (GMT). No. of bitstreams: 1 2372.pdf: 3456814 bytes, checksum: 7894f1e8062bb948621e2d222d01e3b0 (MD5) Previous issue date: 2008-11-06<br>Financiadora de Estudos e Projetos<br>The Molecular Evolution of the living organisms is a slow process that occurs over the years producing mutations and recombinations at the genetic material, i.e. at the DNA. The mutations can occur as nucleotide remotion, insertion and/or substitution at the DNA chain. The Directed Molecular Evolution is an in vitro process that tries to improve biological functions of sp

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Akrin, Christoffer, and Simon Tham. "A Natural Language Interface for Querying Linked Data." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78921.

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The thesis introduces a proof of concept idea that could spark great interest from many industries. The idea consists of a remote Natural Language Interface (NLI), for querying Knowledge Bases (KBs). The system applies natural language technology tools provided by the Stanford CoreNLP, and queries KBs with the use of the query language SPARQL. Natural Language Processing (NLP) is used to analyze the semantics of a question written in natural language, and generates relational information about the question. With correctly defined relations, the question can be queried on KBs containing relevan

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Svensson, Henrik, and Kalle Lindqvist. "Rättssäker Textanalys." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20396.

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Digital språkbehandling (natural language processing) är ett forskningsområde inom vilketdet ständigt görs nya framsteg. En betydande del av den textanalys som sker inom dettafält har som mål att uppnå en fullgod tillämpning kring dialogen mellan människa ochdator. I denna studie vill vi dock fokusera på den inverkan digital språkbehandling kan hapå den mänskliga inlärningsprocessen. Vårt praktiska testområde har också en framtidainverkan på en av de mest grundläggande förutsättningarna för ett rättssäkert samhälle,nämligen den polisiära rapportskrivningen.Genom att skapa en teoretisk idébas s

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Holmer, Daniel. "Context matters : Classifying Swedish texts using BERT's deep bidirectional word embeddings." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166304.

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When classifying texts using a linear classifier, the texts are commonly represented as feature vectors. Previous methods to represent features as vectors have been unable to capture the context of individual words in the texts, in theory leading to a poor representation of natural language. Bidirectional Encoder Representations from Transformers (BERT), uses a multi-headed self-attention mechanism to create deep bidirectional feature representations, able to model the whole context of all words in a sequence. A BERT model uses a transfer learning approach, where it is pre-trained on a large a

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Olsson, Fredrik. "Bootstrapping Named Entity Annotation by Means of Active Machine Learning: A Method for Creating Corpora." Doctoral thesis, SICS, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-22935.

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This thesis describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. The reason for working with documents, as opposed to for instance sentences or phrases, is that the BootMark method is concerned with the creation of corpora. The claim made in the thesis is that BootMark requires a human annotator to manually annotate fewer documents in order to produce a named entity recognizer with a given performance, than would be needed if the documents forming the basis for the recognizer were r

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Trembczyk, Max. "Answer Triggering Mechanisms in Neural Reading Comprehension-based Question Answering Systems." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-390840.

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We implement a state-of-the-art question answering system based on Convolutional Neural Networks and Attention Mechanisms and include four different variants of answer triggering that have been discussed in recent literature. The mechanisms are included in different places in the architecture and work with different information and mechanisms. We train, develop and test our models on the popular SQuAD data set for Question Answering based on Reading Comprehension that has in its latest version been equipped with additional non-answerable questions that have to be retrieved by the systems. We t

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Pettersson, Eva. "Spelling Normalisation and Linguistic Analysis of Historical Text for Information Extraction." Doctoral thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-269753.

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Historical text constitutes a rich source of information for historians and other researchers in humanities. Many texts are however not available in an electronic format, and even if they are, there is a lack of NLP tools designed to handle historical text. In my thesis, I aim to provide a generic workflow for automatic linguistic analysis and information extraction from historical text, with spelling normalisation as a core component in the pipeline. In the spelling normalisation step, the historical input text is automatically normalised to a more modern spelling, enabling the use of existin

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Capshaw, Riley. "Relation Classification using Semantically-Enhanced Syntactic Dependency Paths : Combining Semantic and Syntactic Dependencies for Relation Classification using Long Short-Term Memory Networks." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153877.

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Many approaches to solving tasks in the field of Natural Language Processing (NLP) use syntactic dependency trees (SDTs) as a feature to represent the latent nonlinear structure within sentences. Recently, work in parsing sentences to graph-based structures which encode semantic relationships between words—called semantic dependency graphs (SDGs)—has gained interest. This thesis seeks to explore the use of SDGs in place of and alongside SDTs within a relation classification system based on long short-term memory (LSTM) neural networks. Two methods for handling the information in these graphs a

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Öhrström, Fredrik. "Cluster Analysis with Meaning : Detecting Texts that Convey the Same Message." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153873.

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Textual duplicates can be hard to detect as they differ in words but have similar semantic meaning. At Etteplan, a technical documentation company, they have many writers that accidentally re-write existing instructions explaining procedures. These "duplicates" clutter the database. This is not desired because it is duplicate work. The condition of the database will only deteriorate as the company expands. This thesis attempts to map where the problem is worst, and also how to calculate how many duplicates there are. The corpus is small, but written in a controlled natural language called Simp

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Mann, Jasleen Kaur. "Semantic Topic Modeling and Trend Analysis." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173924.

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This thesis focuses on finding an end-to-end unsupervised solution to solve a two-step problem of extracting semantically meaningful topics and trend analysis of these topics from a large temporal text corpus. To achieve this, the focus is on using the latest develop- ments in Natural Language Processing (NLP) related to pre-trained language models like Google’s Bidirectional Encoder Representations for Transformers (BERT) and other BERT based models. These transformer-based pre-trained language models provide word and sentence embeddings based on the context of the words. The results are then

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Grant, Harald. "Extractive Multi-document Summarization of News Articles." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158275.

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Publicly available data grows exponentially through web services and technological advancements. To comprehend large data-streams multi-document summarization (MDS) can be used. In this research, the area of multi-document summarization is investigated. Multiple systems for extractive multi-document summarization are implemented using modern techniques, in the form of the pre-trained BERT language model for word embeddings and sentence classification. This is combined with well proven techniques, in the form of the TextRank ranking algorithm, the Waterfall architecture and anti-redundancy filt

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Lund, Max. "Duplicate Detection and Text Classification on Simplified Technical English." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158714.

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This thesis investigates the most effective way of performing classification of text labels and clustering of duplicate texts in technical documentation written in Simplified Technical English. Pre-trained language models from transformers (BERT) were tested against traditional methods such as tf-idf with cosine similarity (kNN) and SVMs on the classification task. For detecting duplicate texts, vector representations from pre-trained transformer and LSTM models were tested against tf-idf using the density-based clustering algorithms DBSCAN and HDBSCAN. The results show that traditional method

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Garabato, Brady D. "Synthesis and Computational Studies of a New Class of Lanthanide Niobate Cluster : [Ln₄(H₂O)₈(SO₄)₅(NbO₃)₂]+3H₂O; Ln= Dy, Tb." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1294.

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Polyoxoniobates (PONbs) are a small family of highly electron-rich clusters. The development of new solids composed of these clusters have applications in green energy and electronics. However, the high charge environment of PONbs typically requires alkaline synthetic conditions that are unsuitable for introducing other metals and organic molecules, making synthesis of new systems difficult. To date, very few transition metals and organic ligands have been incorporated into these PONb solids, and lanthanide metal inclusion, which generally improves photoconductivity due to longlived f-orbital

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