Sökning: "Bert Can"

Visar resultat 1 - 5 av 124 uppsatser innehållade orden Bert Can.

1. 1. Approximating Reasoning with Transformer Language Models

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Daniel Enström; Viktor Kjellberg; [2023-10-19]
   Nyckelord :natural language processing; nlp; automated reasoning; logic; inference; machine learning; transformers; language models; BERT; BART;

   Sammanfattning : We conduct experiments with BART, a generative language-model architecture, to investigate its capabilities for approximating reasoning by learning from data. For this we use the SimpleLogic dataset, a dataset of satisfiability problems in propositional logic originally created by Zhang et al. (2022). LÄS MER

3. 2. Data Augmentation: Enhancing Named Entity Recognition Performance on Swedish Medical Texts

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Lucas Rosvall; Niklas Paasonen; [2023-10-05]
   Nyckelord :Machine Learning; Information Extraction; Named Entity Recognition; BERT; Data Augmentation;

   Sammanfattning : Named Entity Recognition (NER) refers to the task of locating relevant information within text sequences. Within the medical domain, it can benefit applications such as de-identifying patient records or extracting valuable data for other downstream tasks. LÄS MER

4. 3. Detection of insurance fraud using NLP and ML

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Rasmus Bäcklund; Hampus Öhman; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : Machine-Learning can sometimes see things we as humans can not. In this thesis we evaluated three different Natural Language Procces-techniques: BERT, word2vec and linguistic analysis (UDPipe), on their performance in detecting insurance fraud based on transcribed audio from phone calls (referred to as audio data) and written text (referred to as text-form data), related to insurance claims. LÄS MER

5. 4. Mitigating Unintended Bias in Toxic Comment Detection using Entropy-based Attention Regularization

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Fabio Camerota; [2023]
   Nyckelord :XLNet; BERT; Toxic Comment Classification; Entropy-based Attention Regularization; XLNet; BERT; Toxisk Kommentar Klassificering; Entropibaserad uppmärksamhetsreglering;

   Sammanfattning : The proliferation of hate speech is a growing challenge for social media platforms, as toxic online comments can have dangerous consequences also in real life. There is a need for tools that can automatically and reliably detect hateful comments, and deep learning models have proven effective in solving this issue. LÄS MER

7. 5. Speech Classification using Acoustic embedding and Large Language Models Applied on Alzheimer’s Disease Prediction Task

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Maryam Kheirkhahzadeh; [2023]
   Nyckelord :Speech classification; Alzheimer’s disease detection; GPT-3; BERT; Text embedding; Dementia; wav2vec2.0; Klassificering av tal; detektion av Alzheimer’s sjukdom; GPT-3; BERT; textinbäddning; demens; wav2vec2.0;

   Sammanfattning : Alzheimer’s sjukdom är en neurodegenerativ sjukdom som leder till demens. Den kan börja tyst i de tidiga stadierna och fortsätta under åren till en allvarlig och obotlig fas. Språkstörningar uppstår ofta som ett av de tidiga symptomen och kan till slut leda till fullständig mutism i de avancerade stadierna av sjukdomen. LÄS MER