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51	Aplicação do Word2vec e do Gradiente descendente dstocástico em tradução automática Aguiar, Eliane Martins de 30 May 2016 (has links) Submitted by Eliane Martins de Aguiar (elianemart@gmail.com) on 2016-08-01T21:03:09Z No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2016-08-03T20:29:34Z (GMT) No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5) / Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2016-08-23T20:12:35Z (GMT) No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5) / Made available in DSpace on 2016-08-23T20:12:54Z (GMT). No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5) Previous issue date: 2016-05-30 / O word2vec é um sistema baseado em redes neurais que processa textos e representa pa- lavras como vetores, utilizando uma representação distribuída. Uma propriedade notável são as relações semânticas encontradas nos modelos gerados. Este trabalho tem como objetivo treinar dois modelos utilizando o word2vec, um para o Português e outro para o Inglês, e utilizar o gradiente descendente estocástico para encontrar uma matriz de tradução entre esses dois espaços. Natural language processing Neural networks Word2vec Continuos bag-of-words Stochastic gradient descent Machine translation Processamento de linguagem natural Redes neurais Gradiente descendente estocástico Tradução automática Matemática Redes neurais (Computação)
52	Word2vec modely s přidanou kontextovou informací / Word2vec Models with Added Context Information Šůstek, Martin January 2017 (has links) This thesis is concerned with the explanation of the word2vec models. Even though word2vec was introduced recently (2013), many researchers have already tried to extend, understand or at least use the model because it provides surprisingly rich semantic information. This information is encoded in N-dim vector representation and can be recall by performing some operations over the algebra. As an addition, I suggest a model modifications in order to obtain different word representation. To achieve that, I use public picture datasets. This thesis also includes parts dedicated to word2vec extension based on convolution neural network.
53	Using a Character-Based Language Model for Caption Generation / Användning av teckenbaserad språkmodell för generering av bildtext Keisala, Simon January 2019 (has links) 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-based language models an alternative model based on TensorFlow im2txt has been created. The model changes the token-generation architecture into handling character-sized tokens instead of word-sized tokens. The results suggest that a character-based language model could outperform the current token-based language models, although due to time and computing power constraints this study fails to draw a clear conclusion. A problem with one of the methods, subsampling, is discussed. When using the original method on character-sized tokens this method removes characters (including special characters) instead of full words. To solve this issue, a two-phase approach is suggested, where training data first is separated into word-sized tokens where subsampling is performed. The remaining tokens are then separated into character-sized tokens. Future work where the modified subsampling and fine-tuning of the hyperparameters are performed is suggested to gain a clearer conclusion of the performance of character-based language models. Natural Language Processing NLP Machine Learning ML Neural Network Caption Generation Deep Learning Recurrent Neural Network Long-Short-Term-Memory LSTM word2vec Language Model
54	Exploring Automatic Synonym Generation for Lexical Simplification of Swedish Electronic Health Records Jänich, Anna January 2023 (has links) Electronic health records (EHRs) are used in Sweden's healthcare systems to store patients' medical information. Patients in Sweden have the right to access and read their health records. Unfortunately, the language used in EHRs is very complex and presents a challenge for readers who lack medical knowledge. Simplifying the language used in EHRs could facilitate the transfer of information between medical staff and patients. This project investigates the possibility of generating Swedish medical synonyms automatically. These synonyms are intended to be used in future systems for lexical simplification that can enhance the readability of Swedish EHRs and simplify medical terminology. Current publicly available Swedish corpora that provide synonyms for medical terminology are insufficient in size to be utilized in a system for lexical simplification. To overcome the obstacle of insufficient corpora, machine learning models are trained to generate synonyms and terms that convey medical concepts in a more understandable way. With the purpose of establishing a foundation for analyzing complex medical terms, a simple mechanism for Complex Word Identification (CWI) is implemented. The mechanism relies on matching strings and substrings from a pre-existing corpus containing hand-curated medical terms in Swedish. To find a suitable strategy for generating medical synonyms automatically, seven different machine learning models are queried for synonym suggestions for 50 complex sample terms. To explore the effect of different input data, we trained our models on different datasets with varying sizes. Three of the seven models are based on BERT and four of them are based on Word2Vec. For each model, results for the 50 complex sample terms are generated and raters with medical knowledge are asked to assess whether the automatically generated suggestions could be considered synonyms. The results vary between the different models and seem to be connected to the amount and quality of the data they have been trained on. Furthermore, the raters involved in judging the synonyms exhibit great disagreement, revealing the complexity and subjectivity of the task to find suitable and widely accepted medical synonyms. The method and models applied in this project do not succeed in creating a stable source of suitable synonyms. The chosen BERT approach based on Masked Language Modelling cannot reliably generate suitable synonyms due to the limitation of generating one term per synonym suggestion only. The Word2Vec models demonstrate some weaknesses due to the lack of context consideration. Despite the fact that the current performance of our models in generating automatic synonym suggestions is not entirely satisfactory, we have observed a promising number of accurate suggestions. This gives us reason to believe that with enhanced training and a larger amount of input data consisting of Swedish medical text, the models could be improved and eventually effectively applied. Lexical Simplification EHR Electronic Health Records Swedish EHRs patient records complex medical terminology synonym generation synonym replacement Word2Vec BERT NLP ML Computer and Information Sciences Data- och informationsvetenskap Medical Engineering Medicinteknik
55	An Exploration of the Word2vec Algorithm: Creating a Vector Representation of a Language Vocabulary that Encodes Meaning and Usage Patterns in the Vector Space Structure Le, Thu Anh 05 1900 (has links) This thesis is an exloration and exposition of a highly efficient shallow neural network algorithm called word2vec, which was developed by T. Mikolov et al. in order to create vector representations of a language vocabulary such that information about the meaning and usage of the vocabulary words is encoded in the vector space structure. Chapter 1 introduces natural language processing, vector representations of language vocabularies, and the word2vec algorithm. Chapter 2 reviews the basic mathematical theory of deterministic convex optimization. Chapter 3 provides background on some concepts from computer science that are used in the word2vec algorithm: Huffman trees, neural networks, and binary cross-entropy. Chapter 4 provides a detailed discussion of the word2vec algorithm itself and includes a discussion of continuous bag of words, skip-gram, hierarchical softmax, and negative sampling. Finally, Chapter 5 explores some applications of vector representations: word categorization, analogy completion, and language translation assistance. Word2vec neural networks vector representation of language skip-gram continuous bag of words hierarchical softmax negative sampling language translation Huffman trees binary cross-entropy optimization Mathematics Neural networks (Computer science) Computational linguistics.
56	EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION Fattah Muhammad Tahabi (14160375) 03 February 2023 (has links) <p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p> Biomechanical engineering Neural engineering Health promotion Preventative health care Applications in health Spatial data and applications Evolutionary computation Natural language processing Planning and decision making Data engineering and data science Data mining and knowledge discovery Graph, social and multimedia data Information retrieval and web search Knowledge and information management Context learning Deep learning Neural networks Semi- and unsupervised learning Data structures and algorithms Graph neural network Node classification Graph clustering Temporal graphs dynamic graphs NODE2VEC Graph Attention Mechanism Hunting BiLSTM model EHR data colorectal Cancer Cancers Cancer symptoms symptom Symptom cluster studies Coauthorship networks network analysis Word2vec Hierarchical Clustering method Dunn index semantic analysis text mining Natural Language Processing Tool UMLS identifiers umls Clinical Data Management

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