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 28 for “"word2vec"”.
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Exploring the application of Word2Vec to basket transaction data in the grocery retail industry
In this thesis, we explore the application of Word2vec to basket transaction data provided by a large grocery retailer in South Africa. Word2vec is an algorithm based on representation learning. The objective of the exploration is to establish whether the application of Word2vec to basket …
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Performance evaluation of text augmentation methods with BERT on imbalanced datasets
… this study examines the effectiveness of Word2Vec and WordNet augmentation methods with BERT fine-tuning on datasets of various sizes (e.g., 500, 1,000, and 5,000 training documents) and imbalance ratios (e.g., 4:1 and 9:1). It compares them with other methods for imbalanced data, …
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Benchmarking authorship attribution techniques using over a thousand books by fifty Victorian era novelists
… explore information retrieval techniques such as Word2Vec, paragraph2vec, and other useful feature selection and extraction techniques for a given text with different classifiers. We have performed experiments on novels that are extracted from GDELT database by using different features such as bag …
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TONGS: TLDR; Opinion Network Guide System
… for conducting sentiment analysis. TONGS uses Word2Vec within the TensorFlow library to convert words into vector space representations. The TONGS library contains four different methods built upon previous research in sentiment analysis and Word2Vec. We further experiment and analyze these …
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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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EVALUATING DISTRIBUTED WORD REPRESENTATIONS FOR PREDICTING MISSING WORDS IN SENTENCES
… methods like Latent Semantic Analysis, word2vec, and GloVe to learn the distributed representation of words. Their performance was compared based on the accuracy achieved when tasked with selecting the right missing word in the sentence, given five possible options. For this NLP task we …
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An unsupervised approach to COVID-19 fake tweet detection
… Word embedding techniques such as TF-IDF, Word2Vec, and BERT were employed because machine learning models cannot process unprocessed text data directly, and word embedding resolves this issue. Results: The results on the test data show that K-means algorithm was the best performing …
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Exploring embedding vectors for emotion detection
… emotions. While the word embedding vectors like Word2Vec have been successfully employed in many NLP approaches, the word mover’s distance (WMD) is a method introduced recently to calculate the distance between two documents based on the embedded words. This thesis is investigating the ability to …
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Comparative research on code vulnerability detection: Open-source vs. proprietary large language models and LSTM neural network
… in detecting security risks and compares them to Word2Vec+LSTM neural networks. The findings reveal that CodeGen2 emerges as the most reliable model for vulnerability detection, achieving near-perfect precision and balanced recall, leading to superior F1 and AUC scores. LLaMA2-7b delivers …
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Geometries of word embeddings
… linguistic regularities. Popular examples are word2vec, GloVe, GPT and BERT. Both word2vec and GloVe are static whose word representations are independent of its context, while GPT and BERT are contextualized whose representations will change corresponding to the semantics conveyed by its …
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AWE: Attention Word Embedding
… popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it. A limitation of CBOW is that it equally weights the context words when making a prediction, which is inefficient, …
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An Artificial Intelligence Based Approach to Automate Document Processing in Business Area
… Vector Machine (SVM) as the classifier and Word2vec embeddings as document features to classify business documents. The proposed model reaches a 0.872 Macro F1-score using scanned business documents from the RVL-CDIP dataset. The proposed model outperforms the other commonly used rule-based …
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A Large Collection Learning Optimizer Framework
… of the art feature selection techniques like Word2Vec. Machine learning techniques, using word features that capture semantic and context relationships, can be of benefit regarding classification accuracy. Improving text classification results on Twitter data would pave the way to categorize …
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Software Requirements Classification Using Word Embeddings and Convolutional Neural Networks
… Scikit-Learn and word embedding models such as word2vec and fastText, we build a Python system that trains and validates configurations of Naïve Bayes and CNN requirements classifiers. Applying our system to a suite of experiments on two well-studied requirements datasets, we recreate or …
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A Computational Study in the Detection of English–Spanish Code-Switches
… data. The second experiment is using a novel Word2Vec average nearest neighbor (WANN) classifier based on word embeddings to detect code-switching. The third experiment uses Doc2Vec, where the model uses the mean vector of each document to learn and distinguish between code-switched and …
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Building a similarity engine
… are built upon. In 2013, Mikolov created word2vec, a toolkit that enabled the training and use of pre-trained embeddings. In 2014, Pennington introduced GloVe, a competitive set of pre-trained embeddings. Starting off, a single word or group of words can be converted into a vector. This …
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Helping Developers Migrate their Code across Programming Languages
… leverages a Siamese network combined with Word2Vec embeddings, while the second employs transformers. For code automation, the concept of Translation Types is introduced to categorize code translations. An empirical study was conducted to analyze the differences between human-translated and …
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Classification of customer complaints using machine learning algorithms
… vectorisation techniques namely: CV, TFIDF, and Word2Vec word-embedding. The algorithms are meant to classify each customer complaint into one of the thirteen possible Products. Due to imbalanced distributions of the target (Product complaint topics), a balanced accuracy metric was used to …
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Deep Assertion discovery using word embeddings
… Frequency), using word embedding approaches like Word2Vec in Deep Learning frameworks. We have shown that the assertions discovered using our proposed framework can be effectively used for the topic-based classification of the biomedical dataset containing abstracts for three categories such as …
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Sentiment Analysis on Twitter feeds to establish opinion towards entities in single entity and multi-entity texts
… scored using the word-embedding method known as Word2Vec. <br/><br/>This research considers the usage of word embeddings and a sentiment lexicon hybrid approach, in order to address this task. The findings from this body of work demonstrate that, by integrating a word embeddings approach for …
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