Text Auto-complete feature suggests a stream of words which complete a user's text as the user types each character. Such a feature is used in search engines, email programs, source code editors, database query tools etc. Earlier people have used traditional language models for this problem. But for better performance, a Neural network-based language model is needed. Here, an encoder-decoder based sequence-to-sequence language model has been used for performing text generation and the empirical results show that the model effectively suggests the incomplete sentences.


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    Titel :

    E-mail autocomplete function using RNN Encoder-decoder sequence-to-sequence model


    Beteiligte:
    Modi, Rohan (Autor:in) / Naik, Kush (Autor:in) / Vyas, Tarjni (Autor:in) / Desai, Shivani (Autor:in) / Degadwala, Sheshang (Autor:in)


    Erscheinungsdatum :

    02.12.2021


    Format / Umfang :

    908576 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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