Sökning: "Naturlig språkbehandling"

Visar resultat 16 - 20 av 60 uppsatser innehållade orden Naturlig språkbehandling.

1. 16. A visual approach to web information extraction : Extracting information from e-commerce web pages using object detection

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

   Författare :Alexander Brokking; [2023]
   Nyckelord :Web information extraction; computer vision; object detection; deep learning; Informationsextraktion från webben; datorseende; objektigenkänning; djupinlärning;

   Sammanfattning : Internets enorma omfattning har resulterat i ett överflöd av information som är oorganiserad och spridd över olika hemsidor. Det har varit motivationen för automatisk informationsextraktion av hemsidor sedan internets begynnelse. LÄS MER

3. 17. Exploring toxic lexicon similarity methods with the DRG framework on the toxic style transfer task

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

   Författare :Martin Iglesias; [2023]
   Nyckelord :Detoxifcation; Text style transfer; Deep learning; Transformers; Linguistics; Natural Language Processing; Hate speech; Text style-conditional generation; Large language model; Avgiftning; Överföring av textstil; Djupinlärning; Transformatorer; Lingvistik; Naturlig språkbehandling; Hatprat; Textstil - villkorlig generering; Stor språkmodell;

   Sammanfattning : The topic of this thesis is the detoxification of language in social networks with a particular focus on style transfer techniques that combine deep learning and linguistic resources. In today’s digital landscape, social networks are rife with communication that can often be toxic, either intentionally or unintentionally. LÄS MER

4. 18. Re-ranking search results with KB-BERT

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

   Författare :Bjarki Viðar Kristjánsson; [2022]
   Nyckelord :Natural language processing; Information retrieval; BERT; KB-BERT; Search evaluation; Naturlig språkbehandling; Informationssökning; BERT; KB-BERT; Sökutvärdering;

   Sammanfattning : This master thesis aims to determine if a Swedish BERT model can improve a BM25 search by re-ranking the top search results. We compared a standard BM25 search algorithm with a more complex algorithm composed of a BM25 search followed by re-ranking the top 10 results by a BERT model. LÄS MER

5. 19. Duplicate detection of multimodal and domain-specific trouble reports when having few samples : An evaluation of models using natural language processing, machine learning, and Siamese networks pre-trained on automatically labeled data

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

   Författare :Viktor Karlstrand; [2022]
   Nyckelord :Duplicate detection; Bug reports; Trouble reports; Natural language processing; Information retrieval; Machine learning; Siamese neural network; Transformers; Automated data labeling; Shapley values; Dubblettdetektering; Felrapporter; Buggrapporter; Naturlig språkbehandling; Informationssökning; Maskininlärning; Siamesiska neurala nätverk; Transformatorer; Automatiserad datamärkning; Shapley-värden;

   Sammanfattning : Trouble and bug reports are essential in software maintenance and for identifying faults—a challenging and time-consuming task. In cases when the fault and reports are similar or identical to previous and already resolved ones, the effort can be reduced significantly making the prospect of automatically detecting duplicates very compelling. LÄS MER

7. 20. 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