Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 62 for “"word embeddings"”.
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Geometries of word embeddings
Real-valued word embeddings have transformed natural language processing (NLP) applications, recognized for their ability to capture linguistic regularities. Popular examples are word2vec, GloVe, GPT and BERT. Both word2vec and GloVe are static whose word representations are independent of its …
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Deep Assertion discovery using word embeddings
… approach to discover the significant features (words) and map them to the relation tuples extracted using the open information extraction technique. We have implemented three different feature discovery techniques – using the numerical statistic like TF-IDF (Term Frequency-Inverse Document …
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TimeLink: Visualizing Diachronic Word Embeddings and Topics
… terms is not easy. Work has been done to develop word embeddings, allowing researchers to treat words like any number. This makes it possible to create simple charts based on word embeddings like scatter plots. However, these methods are inefficient due to loss of effectiveness with multiple time …
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Software Requirements Classification Using Word Embeddings and Convolutional Neural Networks
… classification, specifically the use of word embeddings for document representation when training a convolutional neural network (CNN). As past research endeavors mainly utilize information retrieval and traditional machine learning techniques, we entertain the potential of deep learning …
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Modeling Similarities Among Autism Spectrum Patients Using Word Embeddings on Clinical Notes
… they present. We used Doc2Vec to create neural word embedding vectors on the clinical notes presented and K-means clustering to group the patients based on similarities in the notes. The clusters will give us greater insight into the examinations done by clinicians in ABA therapy, the …
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Post-processing Techniques for Word Embedding
Word embedding has been a significant breakthrough in natural language processing (NLP). Although word representation has improved remarkably and resulted in better performance in downstream NLP applications, interpretability of word embeddings remains a challenge. Post-processing techniques have …
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Temporal Topic Embeddings with a Compass
Aligning Word2vec word embeddings using a compass in a system of Compass-aligned Distributional Embeddings (CADE) creates stable and accurate temporal word embeddings. This thesis seeks to expand the CADE framework into the area of dynamic topic modeling (DTM), where temporal word2vec embeddings …
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Natural Language Processing as a Tool in Supporting Clinical Decision-Making
… generation performed substantially better than word embeddings when using a traditional machine learning model like logistic regression. However, using word embeddings with a neural network architecture yielded more comparable results. For the machine learning models themselves, the support …
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Sentiment Analysis on Twitter feeds to establish opinion towards entities in single entity and multi-entity texts
… to investigate how entities and their descriptor words, for example, adjectives, verbs or adverbs can be used to identify the sentiment of the tweet in relation to the entity or entities, where more than one entity exists. <br/><br/>This task has been approached through a hybrid approach which …
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Representations from vision and language
… in text descriptions (a problem known as word or phrase grounding) without strong supervision, and model the interaction between concepts. Specifically, we address the following three challenges faced by existing vision-language models: The first challenge is that of building generalizable …
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Data-Efficient Bilingual Lexicon Induction with Pretrained Language Models
… previous BLI methods rely on mapping static word embeddings, inspired by the paradigm shifts towards pretrained language models (PLMs), we investigate leveraging PLMs for BLI. Firstly, we propose a two-stage contrastive learning framework, combing cross-lingual word embeddings (CLWEs) mapped …
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Computational support for media ecosystems research
… tool is a visualization that displays neural word embeddings data, allowing a user to explore words used in similar contexts within a text corpus. The second tool is an interface that guides users through a supervised machine learning pipeline, enabling novices to train their own binary …
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Controlling Emotional Text to Speech Using Complex Adverbial Phrases
… involves embedding descriptions using word embeddings and encoding speaker IDs with machine learning techniques. Additionally, the model architecture includes a prosody encoder inspired by prior research. Evaluation of the trained models involves subjective assessments by human …
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Optimización del sistema de análisis y caracterización de discusiones en Twitter "Tsundoku"
… para lecturas eficientes. Además, se integraron word embeddings al proceso de clasificación, permitiendo añadir un acercamiento contextual al procesamiento de texto. Estos embeddings fueron obtenidos a partir de la arquitectura Transformers, ocupando el modelo pre-entrenado en español conocido …
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Occupational gender bias in large language models : a multi-level analysis using the OccuBias Dataset
… cosine similarity, Scoring Association Means of Word Embeddings (SAME), and direct bias scores. In addition, K-means clustering is applied to explore how occupations are organized within the models’ embedding spaces, and correlation analysis is used to examine the relationship between …
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Deep linguistic lensing
Language models and semantic word embeddings have become ubiquitous as sources for machine learning features in a wide range of predictive tasks and real-world applications. We argue that language models trained on a corpus of text can learn the linguistic biases implicit in that corpus. We discuss …
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Affective analysis of text in tweets
… hashtags. I use traditional lexical features and word embeddings to extract semantic and lexical information from the input text. I develop models ranging from linear and tree-based models to deep neural networks to perform emotion detection on Tweets. I create an ensemble of these methods to make …
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Deep learning based semantic textual similarity for applications in translation technology
… STS method which relies on contextual word embeddings. We also propose a novel Siamese neural network based on efficient recurrent neural network units. We empirically evaluate various unsupervised and supervised STS methods, including these newly proposed methods in three different …
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EVALUATING DISTRIBUTED WORD REPRESENTATIONS FOR PREDICTING MISSING WORDS IN SENTENCES
… recent years, the distributed representation of words in vector space or word embeddings have become very popular as they have shown significant improvements in many statistical natural language processing (NLP) tasks as compared to traditional language models like Ngram. In this thesis, we …
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Predicting 30-Day Unplanned ICU Readmissions Using Deep Learning and Natural Language Processing Techniques: A MIMIC IV Data Analysis
… (LDA), Latent Semantic Analysis (LSA), and word embeddings.</p> <p>We sequentially implement three distinct Dense Neural Networks (DNNs) combined with the LightGBM gradient-boosting framework. Our model attained a 5-fold cross-validated area under the ROC curve (AU- ROC) of 0.81. Our results …
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