Sökning: "document embeddings"

Visar resultat 1 - 5 av 18 uppsatser innehållade orden document embeddings.

  1. 1. Applying Natural Language Processing to document classification

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

    Författare :David Kragbé; [2022]
    Nyckelord :Natural Language Processing; Document Classification; Embeddings; Classifiers; Naturlig Språkbehandling; Dokumentklassificering; Inbäddningar; Klassificerare;

    Sammanfattning : In today's digital world, we produce and use more electronic documents than ever before. And this trend is far from slowing down. Particularly, more and more companies and businesses now need to treat a considerable amount of documents to deal with their clients' requests. LÄS MER

  3. 2. Techniques for Multilingual Document Retrieval for Open-Domain Question Answering : Using hard negatives filtering, binary retrieval and data augmentation

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

    Författare :Carlos Lago Solas; [2022]
    Nyckelord :OpenQA; Multilingual Transformers; Document retrieval; Data augmentation.; OpenQA; Flerspråkiga Transformatorer; Dokumenthämtning; Dataförstärkning.;

    Sammanfattning : Open Domain Question Answering (OpenQA) systems find an answer to a question from a large collection of unstructured documents. In this information era, we have an immense amount of data at our disposal. However, filtering all the content and trying to find the answers to our questions can be too time-consuming and ffdiicult. LÄS MER

  4. 3. Evaluating semantic similarity using sentence embeddings

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

    Författare :Jacob Malmberg; [2021]
    Nyckelord :;

    Sammanfattning : Semantic similarity search is the task of searching for documents or sentences which contain semantically similar content to a user-submitted search term. This task is often carried out, for instance when searching for information on the internet. LÄS MER

  5. 4. Semantic Topic Modeling and Trend Analysis

    Master-uppsats, Linköpings universitet/Statistik och maskininlärning

    Författare :Jasleen Kaur Mann; [2021]
    Nyckelord :NLP; unsupervised topic modelling; trend analysis; LDA; BERT; Sentence-BERT; TF-IDF; transformer based language models; document clustering;

    Sammanfattning : This thesis focuses on finding an end-to-end unsupervised solution to solve a two-step problem of extracting semantically meaningful topics and trend analysis of these topics from a large temporal text corpus. To achieve this, the focus is on using the latest develop- ments in Natural Language Processing (NLP) related to pre-trained language models like Google’s Bidirectional Encoder Representations for Transformers (BERT) and other BERT based models. LÄS MER

  7. 5. Searching and Recommending Texts Related to Climate Change

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Karolin Gjöthlén; [2021]
    Nyckelord :;

    Sammanfattning : This project considers the design of a machine learning system to search efficiently a database of texts related to climate change. The efficient search and navigation of such a database make it easier to find actionable information, detect trends, or derives other useful information. LÄS MER