Sökning: "Textklassificering"

Visar resultat 6 - 10 av 44 uppsatser innehållade ordet Textklassificering.

1. 6. ML enhanced interpretation of failed test result

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

   Författare :Hiranmayi Pechetti; [2023]
   Nyckelord :Data Parsing; Machine Learning; Log file Analysis; Text Classification; Supervised Classification; Dataanalys; maskininlärning; loggfilsanalys; textklassificering; Övervakad klassificering;

   Sammanfattning : This master thesis addresses the problem of classifying test failures in Ericsson AB’s BAIT test framework, specifically distinguishing between environment faults and product faults. The project aims to automate the initial defect classification process, reducing manual work and facilitating faster debugging. LÄS MER

3. 7. Text Classification using the Teacher- Student  Chatroom Corpus

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

   Författare :Marcus Österberg; [2023]
   Nyckelord :Chatbot; NLP; Deep learning; BERT; Data quality; Chatbots; språkteknologi; djup inlärning; BERT; Datakvalité;

   Sammanfattning : Advancements in Artificial Intelligence, especially in the field of natural language processing have opened new possibilities for educational chatbots. One of these is a chatbot that can simulate a conversation between the teacher and the student for continuous learner support. LÄS MER

4. 8. Application of transfer learning in text classification for small and medium sized web-based enterprises

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

   Författare :Victor Oldensand; Simon Haglund; [2022]
   Nyckelord :;

   Sammanfattning : In recent years, the open sourcing of pretrained machine learning models through platforms like Hugging Face has reduced the barriers to entry in big data analysis. This thesis studies the use case of such models for web-based organisations, with a focus on text classification. LÄS MER

5. 9. Methods for data and user efficient annotation for multi-label topic classification

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

   Författare :Agnieszka Miszkurka; [2022]
   Nyckelord :Natural Language Processing; Multi-label text classification; Active Learning; Zero-shot learning; Data Augmentation; Data-centric AI; Naturlig språkbehandling; Textklassificering med multipla klasser; Active Learning; Zero-shot learning; Data Augmentation; Datacentrerad AI;

   Sammanfattning : Machine Learning models trained using supervised learning can achieve great results when a sufficient amount of labeled data is used. However, the annotation process is a costly and time-consuming task. There are many methods devised to make the annotation pipeline more user and data efficient. LÄS MER

7. 10. Fine-Tuning Pre-Trained Language Models for CEFR-Level and Keyword Conditioned Text Generation : A comparison between Google’s T5 and OpenAI’s GPT-2

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

   Författare :Quintus Roos; [2022]
   Nyckelord :Transformed-based Pre-trained Language Models; Natural Language Processing; Natural Language Generation; Conditional Text Generation; Text Classification; Fine-tuning; English Language Learning.; Transformbaserade förtränade språkmodeller; naturlig språkbehandling; naturlig språkgenerering; betingad textgenerering; finjustering; instruktionsjustering; engelska inlärning.;

   Sammanfattning : This thesis investigates the possibilities of conditionally generating English sentences based on keywords-framing content and different difficulty levels of vocabulary. It aims to contribute to the field of Conditional Text Generation (CTG), a type of Natural Language Generation (NLG), where the process of creating text is based on a set of conditions. LÄS MER