Named entity recognition in texts with the help of part of speech tagging
Тип публікації :
Стаття
Дата випуску :
2018
Автор(и) :
Bevza, M. V.
Мова основного тексту :
Англійська
eKNUTSHIR URL :
Випуск :
4
ISSN :
1812-5409
Початкова сторінка :
74
Кінцева сторінка :
83
Цитування :
[APA 7] Bevza, M. V. (2018). Named entity recognition in texts with the help of part of speech tagging. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics, (4), 74–83. https://doi.org/10.17721/1812-5409.2018/4.11
[ДСТУ] Bevza M. V. Named entity recognition in texts with the help of part of speech tagging. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics. 2018. no. 4. P. 74—83. DOI: 10.17721/1812-5409.2018/4.11 (date of access: 25.07.2026).
We analyze neural network architectures that yield state of the art results on named entity recognition task and propose a number of new architectures for improving results even further. We have analyzed a number of ideas and approaches that researchers have used to achieve state of the art results in a variety of NLP tasks. In this work, we present a few architectures which we consider to be most likely to improve the existing state of the art solutions for named entity recognition task and part of speech tasks. The architectures are inspired by recent developments in multi-task learning.This work tests the hypothesis that NER and POS are related tasks and adding information about POS tags as input to the network can help achieve better NER results. And vice versa, information about NER tags can help solve the task of POS tagging.This work also contains the implementation of the network and results of the experiments together with the conclusions and future work.Key words: neural networks, named entity recognition, regularization, generalizability, part of speech tagging, multi-task learning.Pages of the article in the issue: 74 - 83Language of the article: Ukrainian
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