Temat: POS tagging - Katalog OPAC zbiorów

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Tytuł:: Web-Application for the Presentation of Bilingual Corpora (Focusing on Bulgarian as One of the Two Paired Languages)
Autorzy:: Dimitrova, Ludmila
Dutsova, Ralitsa
Powiązania:: https://bibliotekanauki.pl/articles/677223.pdf
Data publikacji:: 2013
Wydawca:: Polska Akademia Nauk. Instytut Slawistyki PAN
Tematy:: parallel corpus
aligned corpus
concordance
linguistic annotation
lemmatization
POS-tagging
web-interface
web-application
Opis:: Web-Application for the Presentation of Bilingual Corpora (Focusing on Bulgarian as One of the Two Paired Languages)This paper briefly presents a web-application for the presentation of bilingual aligned corpora focusing on Bulgarian as one the two paired languages. The focus is given to the description of the software tools and user interface. The software is developed in IMI-BAS and will be hosted on a server there. Some examples of the usage of the web-application for the presentation of a Bulgarian-Polish aligned corpus are included.
Źródło:: Cognitive Studies; 2013, 13
2392-2397
Pojawia się w:: Cognitive Studies
Dostawca treści:: Biblioteka Nauki

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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

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