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Dissertations / Theses on the topic 'Word Vector Models'

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Published: 4 June 2021
Last updated: 9 February 2022

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1

Prum, Sophea. "On the use of a discriminant approach for handwritten word recognition based on bi-character models." Thesis, La Rochelle, 2013. http://www.theses.fr/2013LAROS418/document.

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Avec l’avènement des dispositifs nomades tels que les smartphones et les tablettes, la reconnaissance automatique de l’écriture manuscrite cursive à partir d’un signal en ligne est devenue durant les dernières décennies un besoin réel de la vie quotidienne à l’ère numérique. Dans le cadre de cette thèse, nous proposons de nouvelles stratégies pour un système de reconnaissance de mots manuscrits en-ligne. Ce système se base sur une méthode collaborative segmentation/reconnaissance et en utilisant des analyses à deux niveaux : caractère et bi-caractères. Plus précisément, notre système repose su
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2

Lipecki, Johan, and Viggo Lundén. "The Effect of Data Quantity on Dialog System Input Classification Models." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237282.

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This paper researches how different amounts of data affect different word vector models for classification of dialog system user input. A hypothesis is tested that there is a data threshold for dense vector models to reach the state-of-the-art performance that have been shown with recent research, and that character-level n-gram word-vector classifiers are especially suited for Swedish classifiers–because of compounding and the character-level n-gram model ability to vectorize out-of-vocabulary words. Also, a second hypothesis is put forward that models trained with single statements are more
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3

Esin, Yunus Emre. "Improvement Of Corpus-based Semantic Word Similarity Using Vector Space Model." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610759/index.pdf.

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This study presents a new approach for finding semantically similar words from corpora using window based context methods. Previous studies mainly concentrate on either finding new combination of distance-weight measurement methods or proposing new context methods. The main difference of this new approach is that this study reprocesses the outputs of the existing methods to update the representation of related word vectors used for measuring semantic distance between words, to improve the results further. Moreover, this novel technique provides a solution to the data sparseness of vectors whic
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4

Sahlgren, Magnus. "The Word-Space Model : Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces." Doctoral thesis, Stockholm : Göteborg : Kista : Department of Linguistics, Stockholm University : National Graduate School of Language Technology, Gothenburg University ; Swedish Institute of Computer Science Useware Laboratory, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-1037.

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5

Westin, Emil. "Authorship classification using the Vector Space Model and kernel methods." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412897.

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Authorship identification is the field of classifying a given text by its author based on the assumption that authors exhibit unique writing styles. This thesis investigates the semantic shortcomings of the vector space model by constructing a semantic kernel created from WordNet which is evaluated on the problem of authorship attribution. A multiclass SVM classifier is constructed using the one-versus-all strategy and evaluated in terms of precision, recall, accuracy and F1 scores. Results show that the use of the semantic scores from WordNet degrades the performance compared to using a linea
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6

Göteman, Malin. "The Complex World of Superstrings : On Semichiral Sigma Models and N=(4,4) Supersymmetry." Doctoral thesis, Uppsala universitet, Teoretisk fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183407.

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Non-linear sigma models with extended supersymmetry have constrained target space geometries, and can serve as effective tools for investigating and constructing new geometries. Analyzing the geometrical and topological properties of sigma models is necessary to understand the underlying structures of string theory. The most general two-dimensional sigma model with manifest N=(2,2) supersymmetry can be parametrized by chiral, twisted chiral and semichiral superfields. In the research presented in this thesis, N=(4,4) (twisted) supersymmetry is constructed for a semichiral sigma model. It is fo
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7

Pettersson, Tove. "Word2vec2syn : Synonymidentifiering med Word2vec." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157638.

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Inom NLP (eng. natural language processing) är synonymidentifiering en av de språkvetenskapliga utmaningarna som många antar. Fodina Language Technology AB är ett företag som skapat ett verktyg, Termograph, ämnad att samla termer inom företag och hålla den interna språkanvändningen konsekvent. En metodkombination bestående av språkteknologiska strategier utgör synonymidentifieringen och Fodina önskar ett större täckningsområde samt mer dynamik i framtagningsprocessen. Därav syftade detta arbete till att ta fram en ny metod, utöver metodkombinationen, för just synonymidentifiering. En färdigträ
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8

Lundberg, Otto. "GDP forecasting and nowcasting : Utilizing a system for averaging models to improve GDP predictions for six countries around the world." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-131718.

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This study was issued by Swedbank because they wanted too improve their GDP growth forecast capabilites.  A program was developed and tested on six countries; USA, Sweden, Germany, UK, Brazil and Norway. In this paper I investigate if I can reduce forecasting error for GDP growth by taking a smart average from a variety of models compared to both the best individual models and a random walk. I combine the forecasts from four model groups: Vector autoregression, principal component analysis, machine learning and random walk. The smart average is given by a system that give more weight to the pr
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9

Wang, Zhenyu. "Modeling crash severity and speed profile at roadway work zones." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002515.

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10

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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11

Criscuolo, Marcelo. "SlimRank: um modelo de seleção de respostas para perguntas de consumidores." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-31012018-140412/.

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A disponibilidade de conteúdo gerado por usuários em sites colaborativos de perguntas e respostas tem impulsionado o avanço de modelos de Question Answering (QA) baseados em reúso. Essa abordagem pode ser implementada por meio da tarefa de seleção de respostas (Answer Selection, AS), que consiste em encontrar a melhor resposta para uma dada pergunta em um conjunto pré-selecionado de respostas candidatas. Nos últimos anos, abordagens baseadas em vetores distribucionais e em redes neurais profundas, em particular em redes neurais convolutivas (CNNs), têm apresentado bons resultados na tarefa de
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12

Balgar, Marek. "Vyhledávání informací v české Wikipedii." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-412831.

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The main task of this Masters Thesis is to understand questions of information retrieval and text classifi cation. The main research is focused on the text data, the semantic dictionaries and especially the knowledges inferred from the Wikipedia. In this thesis is also described implementation of the querying system, which is based on achieved knowledges. Finally properties and possible improvements of the system are talked over.
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13

Chinea, Ríos Mara. "Advanced techniques for domain adaptation in Statistical Machine Translation." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/117611.

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[ES] La Traducción Automática Estadística es un sup-campo de la lingüística computacional que investiga como emplear los ordenadores en el proceso de traducción de un texto de un lenguaje humano a otro. La traducción automática estadística es el enfoque más popular que se emplea para construir estos sistemas de traducción automáticos. La calidad de dichos sistemas depende en gran medida de los ejemplos de traducción que se emplean durante los procesos de entrenamiento y adaptación de los modelos. Los conjuntos de datos empleados son obtenidos a partir de una gran variedad de fuentes y en mucho
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14

Pešek, Milan. "Detekce logopedických vad v řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218106.

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The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In t
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15

CHEN, CHIA-MING, and 陳嘉銘. "Improve word-to-answer capabilities of chat bots through word vector correlation models and web search." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5vbjtk.

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碩士<br>國立雲林科技大學<br>資訊管理系<br>107<br>Most of the chat bots provide customers with instant access to information. Most of the current chat bot systems are based on search keywords, and then look for the best matching response sentences from the database to give back to the customer. Most of the questions in the database are manually generated, which will cost a lot of time and labor. This research will propose a model that groups similar questions together. Through the web crawler, a large number of question corpora are retrieved from the website forum, and Word2vec is used for similarity calculat
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16

Lee, Chun-Ming, and 李俊民. "Vector Information Retrieval Technique with Word Bigram Relation Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/96919424018156173137.

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碩士<br>大同大學<br>資訊經營學系(所)<br>93<br>“Telephone Center” has become enterprises’ major service window and important information source for customer relationship management (CRM). Customer service operators are dealing with similar questions for most of time, and by agents could improve their service quality and efficiency. if automatic agents could apply to replace part of their complicate works, they could deal with more specialized problems and reduce manpower to improve process efficiency. Therefore, for agents’ capability to undertake information, this thesis researches the improvement of gener
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17

"Fixed Verse Generation using Neural Word Embeddings." Master's thesis, 2016. http://hdl.handle.net/2286/R.I.39410.

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abstract: For the past three decades, the design of an effective strategy for generating poetry that matches that of a human’s creative capabilities and complexities has been an elusive goal in artificial intelligence (AI) and natural language generation (NLG) research, and among linguistic creativity researchers in particular. This thesis presents a novel approach to fixed verse poetry generation using neural word embeddings. During the course of generation, a two layered poetry classifier is developed. The first layer uses a lexicon based method to classify poems into types based on form and
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18

Huang, Pei-Yuan, and 黃培遠. "Speaker-independent connected Chinese spoken word recognition based on vector quantization, hidden Markov model and level building." Thesis, 1986. http://ndltd.ncl.edu.tw/handle/06980486031255480460.

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19

Prodanovic, Srdjan. "Semantic disambiguation using Distributional Semantics." Master's thesis, 2012. http://www.nusl.cz/ntk/nusl-304468.

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Ve statistických modelů sémantiky jsou významy slov pouze na základě jejich distribuční vlastnosti.Základní zdroj je zde jeden slovník, který lze použít pro různé úkoly, kde se význam slov reprezentovány jako vektory v vektorového prostoru, a slovní podoby jako vzdálenosti mezi jejich vektorových osobnosti. Pomocí silných podobnosti, může vhodnost podmínek uvedených zejména v souvislosti se vypočítá a používá pro celou řadu úkolů, jeden z nich je slovo smysl Disambiguation. V této práci bylo vyšetřeno několik různých přístupů k modelům z vektorového prostoru a prováděny tak, aby k překročení v
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20

Chang, Qian-Rong, and 張虔榮. "Latent Conceptual Analysis--Using Web data in Chinese to represent conceptual knowledge about word relations in a vector space model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/20311828580113346123.

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碩士<br>國立臺灣師範大學<br>英語學系<br>100<br>In the field of Natural Language Processing, lexical patterns are often applied in many experiments that involve similarity measure among word relations. Despite their growing importance, however, these patterns are rarely examined in terms of what aspect they inherit from the word relation they are claimed to represent. In the thesis, it is proposed that lexical patterns exhibit the same conceptual nature as word relations do. They both display conceptual qualities when they are applied in language use. It is also proposed in this thesis that the conceptua
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21

Silva, André de Vasconcelos Santos. "Sparse distributed representations as word embeddings for language understanding." Master's thesis, 2018. http://hdl.handle.net/10071/18245.

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Word embeddings are vector representations of words that capture semantic and syntactic similarities between them. Similar words tend to have closer vector representations in a N dimensional space considering, for instance, Euclidean distance between the points associated with the word vector representations in a continuous vector space. This property, makes word embeddings valuable in several Natural Language Processing tasks, from word analogy and similarity evaluation to the more complex text categorization, summarization or translation tasks. Typically state of the art word embedding
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