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Showing result 21 - 25 of 66 essays matching the above criteria.

1. 21. Descriptive Labeling of Document Clusters

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

   Author : Adam Österberg; [2022]
   Keywords : Natural Language Processing; Wikipedia; Topic Modeling; Labeling; Språkteknologi; Wikipedia; Temamodellering; Märkning;

   Abstract : Labeling is the process of giving a set of data a descriptive name. This thesis dealt with documents with no additional information and aimed at clustering them using topic modeling and labeling them using Wikipedia as a second source. Labeling documents is a new field with many potential solutions. READ MORE

3. 22. Automatic Podcast Chapter Segmentation : A Framework for Implementing and Evaluating Chapter Boundary Models for Transcribed Audio Documents

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

   Author : Adam Feldstein Jacobs; [2022]
   Keywords : Machine Learning; Natural Language Processing; Speech Technology; Deep Learning; Podcast Segmentation; Maskininlärning; Språkteknologi; Djupinlärning; Podcast Segmentation;

   Abstract : Podcasts are an exponentially growing audio medium where useful and relevant content should be served, which requires new methods of information sorting. This thesis is the first to look into the state-of-art problem of segmenting podcasts into chapters (structurally and topically coherent sections). READ MORE

4. 23. Pricing collateralized loan obligation tranches using machine learning : Machine learning applied to financial data

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

   Author : Marcus Enström; [2022]
   Keywords : Collateralized loan obligation; Machine learning; Artificial neural networks; Financial data; Ensemble methods; Collateralized loan obligation; Maskininlärning; Artificiella neurala nätverk; Finansiell data; Ensemblemetoder;

   Abstract : Machine learning and neural networks have recently become very popular in a large category of domains, partly thanks to their ability to solve complex problems by finding patterns in data, but also due to an increase in computing power and data availability. Successful applications of machine learning include for example image classification, natural language processing, and product recommendation. READ MORE

5. 24. Coreference Resolution for Swedish

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

   Author : Lisa Vällfors; [2022]
   Keywords : Natural language processing; Information extraction; Machine learning; Random forests; Coreference resolution; Språkteknologi; informationsextraktion; maskininlärning; beslutsträdsinlärning; koreferenslösning;

   Abstract : This report explores possible avenues for developing coreference resolution methods for Swedish. Coreference resolution is an important topic within natural language processing, as it is used as a preprocessing step in various information extraction tasks. READ MORE

6. 25. Medical image captioning based on Deep Architectures

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

   Author : Georgios Moschovis; [2022]
   Keywords : Artificial Neural Networks; Deep Learning; Speech and language technology; Natural Language Processing NLP ; Deep networks; Generative deep networks; Convolutional neural networks CNN ; Text generation; Information retrieval; Diagnostic captioning; Image captioning; concept prediction; classification; image encoders; transformers; Encoder-Decoder architecture; abstractive summarization; Neurala nätverk; Djup inlärning; Tal-och språkteknologi; naturlig språkbehandling; djup neurala nätverk; generativa djupa nätverk; konvolutionella neurala nätverk; Textgenerering; Informationssökning; Diagnostisk textning; Bildtextning; konceptförutsägelse; klassificering; bildkodare; transformatorer; kodaravkodararkitektur; abstrakt sammanfattning;

   Abstract : Diagnostic Captioning is described as “the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination” [59] and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals increase their productivity. In this context, tools that would help medical doctors produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain, which makes it particularly interesting for people involved in industry and researchers all along. READ MORE