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    Wyświetlanie 1-2 z 2
    Tytuł:
    Towards mass customisation: automatic processing of orders for residential ship’s containers - A case study example
    Autorzy:
    Dudek, Adam
    Patalas-Maliszewska, Justyna
    Frączak, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/27311441.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    mass customization
    natural language processing
    automatic processing of orders
    residential shipping container
    masowa personalizacja
    przetwarzanie języka naturalnego
    kontener mieszkalny
    obsługa zamówień automatyczna
    Opis:
    Along with changes in customer expectations, the process of ordering a house, especially one built with the most modern technology from prefabricated HQ 40-foot shipping containers, should take place in an atmosphere of free-flowing, customer-friendly conversation. Therefore, it is important that the company producing such a solution has a tool supporting such offers and orders when producing personalized solutions. This article provides an original approach to the automatic processing of orders based on an example of orders for residential shipping containers, natural language processing and so-called premises developed. Our solution overcomes the usage of records of the conversations between the customer and the retailer, in order to precisely predict the variant required for the house ordered, also when providing optimal house recommendations and when supporting manufacturers throughout product design and production. The newly proposed approach examines such recorded conversations in the sale of residential shipping containers and the rationale developed, and then offers the automatic placement of an order. Moreover, the practical significance of the solution, thus proposed, was emphasized thanks to verification by a real residential ship container manufacturing company in Poland.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 3; art. no. e145562
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning based Tamil Parts of Speech (POS) tagger
    Autorzy:
    Anbukkarasi, S.
    Varadhaganapathy, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/2086879.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    POS tagging
    part of speech
    deep learning
    natural language processing
    BiLSTM
    Bi-directional long short term memory
    tagowanie POS
    części mowy
    uczenie głębokie
    przetwarzanie języka naturalnego
    Opis:
    This paper addresses the problem of part of speech (POS) tagging for the Tamil language, which is low resourced and agglutinative. POS tagging is the process of assigning syntactic categories for the words in a sentence. This is the preliminary step for many of the Natural Language Processing (NLP) tasks. For this work, various sequential deep learning models such as recurrent neural network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bi-directional Long Short-Term Memory (Bi-LSTM) were used at the word level. For evaluating the model, the performance metrics such as precision, recall, F1-score and accuracy were used. Further, a tag set of 32 tags and 225 000 tagged Tamil words was utilized for training. To find the appropriate hidden state, the hidden states were varied as 4, 16, 32 and 64, and the models were trained. The experiments indicated that the increase in hidden state improves the performance of the model. Among all the combinations, Bi-LSTM with 64 hidden states displayed the best accuracy (94%). For Tamil POS tagging, this is the initial attempt to be carried out using a deep learning model.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 6; e138820, 1--6
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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