Essays about: "Bidirectional Encoder Representation from Transformers BERT"

Showing result 1 - 5 of 7 essays containing the words Bidirectional Encoder Representation from Transformers BERT.

  1. 1. Classifying personal data on contextual information

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Carl Dath; [2023]
    Keywords : Natural Language Processing; Machine Learning; Word2Vec; GloVe; BERT; Personal Data classification; Språkteknologi; Maskininlärning; Personlig Data Klassificering;

    Abstract : In this thesis, a novel approach to classifying personal data is tested. Previous personal data classification models read the personal data before classifying it. However, this thesis instead investigates an approach to classify personal data by looking at contextual information frequently available in data sets. READ MORE

  3. 2. Help Document Recommendation System

    University essay from Malmö universitet/Fakulteten för teknik och samhälle (TS)

    Author : Keerthi Vijay Kumar; Pinky Mary Stanly; [2023]
    Keywords : Document similarity; Recommender systems; content-based filtering; collaborative filtering; Term Frequency-Inverse Document Frequency TF-IDF ; Bidirectional Encoder Representation from Transformers BERT ; Non-Negative Matrix Factorisation NMF ; cosine similarity; K-means clustering;

    Abstract : Help documents are important in an organization to use the technology applications licensed from a vendor. Customers and internal employees frequently use and interact with the help documents section to use the applications and know about the new features and developments in them. READ MORE

  4. 3. AI Second that Emotion - Using Natural Language Processing to Study the Impact of Non-Stereotyped Video Advertising on Consumers’ Emotions & Online Consumer Engagement

    University essay from Göteborgs universitet/Graduate School

    Author : Amanda Hassel; Linnea Jonsson; Johan Strömfeldt; [2022-08-04]
    Keywords : Stereotype; Video Advertisement; Emotions; Online Consumer Engagement; Natural Language Processing NLP ; Artificial Intelligence AI ; Machine Learning; Bidirectional Encoder Representation from Transformers BERT ; Transformer Models;

    Abstract : This paper aims to provide a deeper understanding of the emotional and online engagement behavioral responses to non-stereotyped gender role depictions in video advertisements. The consumer response to two video ads that portray non-stereotyped gender roles by the well-known brands Gillette and Always was analyzed. READ MORE

  5. 4. Bidirectional Encoder Representations from Transformers (BERT) for Question Answering in the Telecom Domain. : Adapting a BERT-like language model to the telecom domain using the ELECTRA pre-training approach

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Henrik Holm; [2021]
    Keywords : Deep Learning; Natural Language Understanding; Transformers; Language Models; Representation Learning; Domain Adaption; Representationsinlärning; Djupinlärning; Språkteknologi; Transformatorer; Språkmodeller; Domänanpassning;

    Abstract : The Natural Language Processing (NLP) research area has seen notable advancements in recent years, one being the ELECTRA model which improves the sample efficiency of BERT pre-training by introducing a discriminative pre-training approach. Most publicly available language models are trained on general-domain datasets. READ MORE

  6. 5. Coronavirus public sentiment analysis with BERT deep learning

    University essay from Högskolan Dalarna/Informatik

    Author : Jintao Ling; [2020]
    Keywords : coronavirus; deep learning; sentiment analysis; token embedding; social media;

    Abstract : Microblog has become a central platform where people express their thoughts and opinions toward public events in China. With the sudden outbreak of coronavirus, the posts related to coronavirus are usually followed by a burst immediately in microblog volume, which provides a great opportunity to explore public sentiment about the events. READ MORE