Essays about: "neural information retrieval"
Showing result 1 - 5 of 21 essays containing the words neural information retrieval.
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1. Generating an Interpretable Ranking Model: Exploring the Power of Local Model-Agnostic Interpretability for Ranking Analysis
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. However, the complexity of learning-to-rank (LTR) models poses challenges in understanding the underlying reasons contributing to the ranking outcomes. READ MORE
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2. The Impact of the Retrieval Text Set for Text Sentiment Classification With the Retrieval-Augmented Language Model REALM
University essay from KTH/Matematik (Inst.)Abstract : Large Language Models (LLMs) have demonstrated impressive results across various language technology tasks. By training on large corpora of diverse text collections from the internet, these models learn to process text effectively, allowing them to acquire comprehensive world knowledge. READ MORE
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3. Violin Artist Identification by Analyzing Raga-vistaram Audio
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With the inception of music streaming and media content delivery platforms, there has been a tremendous increase in the music available on the internet and the metadata associated with it. In this study, we address the problem of violin artist identification, which tries to classify the performing artist based on the learned features. READ MORE
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4. Re-ranking search results with KB-BERT
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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5. 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
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE