SavedAdd your journal

Journals / İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2021 / Cilt: 3 - Sayı: 1

Open Source Natural Language Processing Libraries

Açık Kaynak Doğal Dil İşleme Kütüphaneleri

Havva YILMAZ
Semih YUMUŞAK
Pages
81–85
Publication Date
2021-04-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Current and popular libraries used in natural language processing and the methods used in these libraries are comperatively explained.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Natural language processing is a collection of methods in which language components are analyzed both syntactically and semantically.
📊
Key Findings & Data:This study presents the set of tools and libraries classified as natural language processing methods.
💡
Conclusion & Impact:Current and popular libraries used in natural language processing and the methods used in these libraries are comperatively explained.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Doğal dil işleme · Doğal dil işleme kütüphaneleri · Makine öğrenmesi

Abstract

Natural language processing is a collection of methods in which language components are analyzed both syntactically and semantically. This study presents the set of tools and libraries classified as natural language processing methods. Current and popular libraries used in natural language processing and the methods used in these libraries are comperatively explained.

Özet

Doğal dil işleme, dil bileşenlerinin hem şekilsel hem de anlamsal olarak analiz edildiği yöntemlere verilen isimdir. Doğal dil işleme yöntemleri sürekli güncellenmekte ve yeni yöntemler geliştirilmektedir. Bu çalışmada, doğal dil işlemede kullanılan güncel ve popüler kütüphaneler ve bu kütüphanelerde kullanılan yöntemler incelenmiştir. Farklı yöntem ve kütüphaneler karşılaştırmalı olarak açıklanmıştır.

🔖

Cite This Article

YILMAZ, H., YUMUŞAK, S. (2021). Open Source Natural Language Processing Libraries. İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 3 - Sayı: 1, 81–85. https://ajindex.com/articles/772047

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Aggarwal, C. C., & Zhai, C. (2012). A survey of text classification algorithms. In Mining text data (pp. 163-222).
  2. Springer, Boston, MA. Akbik, Roland, (2018). Flair A very simple framework for state-of-the-art NLP, 2020. [Çevrimiçi]: https://github.com/flairNLP/flair Barrus, (2018). Pyspellchecker Pure python spell checker based on work by Peter Norvig, 2020. [Çevrimiçi]: https://pypi.org/project/pyspellchecker/ Bora, (2020). Zemberek Parser, 2019. [Çevrimiçi]: https://github.com/kemalcanbora/zemberek\_parser
  3. Buitinck, Louppe, (2013). Scikit-learn 0.23.2 Machine Learning in Python, 2020. [Çevrimiçi]: https://scikit-learn.org/stable/ Bird, (2019). Natural Language Toolkit, 2020. [Çevrimiçi]: http://www.nltk.org
  4. Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32(6), 74-80. Chowdhary, (2020). Natural language processing. In Fundamentals of Artificial Intelligence (pp. 603-649).
  5. Springer, New Delhi. Çetinkaya, (2018). Turkish NLP, 2020. [Çevrimiçi]: https://pypi.org/project/turkishnlp/ David, 2020. How many languages in the world. [Çevrimiçi]: https://www.ethnologue.com/guides/how-many-languages
  6. Dehkharghani, Rahim & Saygin, Yucel & Yanikoglu, Berrin & Oflazer, Kemal. (2015). SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation. 50. 10.1007/s10579-015-9307-6.
  7. Eryiğit, G. (2014, April). ITU Turkish NLP web service. In Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1-4). Gardner, (2017). A natural language processing platform for building state-of-the-art models. 2020. [Çevrimiçi]: https://allennlp.org/
  8. Honnibal, Matthew, (2017). Spacy Industrial-Strength Natural Language Processing in Python, 2020. [Çevrimiçi]: https://spacy.io/ Howard, (2017). Fast.ai, 2020. [Çevrimiçi]: https://www.fast.ai/
  9. Jivani, A. G. (2011). A comparative study of stemming algorithms. Int. J. Comp. Tech. Appl, 2(6), 1930-1938. Koksal, (2018). GitHub - akoksal/Turkish-Lemmatizer: Lemmatization for Turkish Language, 2018. [Çevrimiçi]: https://github.com/akoksal/Turkish-Lemmatizer
  10. Liang, J., Koperski, K., Dhillon, N. S., Tusk, C., & Bhatti, S. (2013). U.S. Patent No. 8,594,996. Washington, DC: U.S. Patent and Trademark Office. Lorai, (2013). TextBlob: Simplified Text Processing, 2020. [Çevrimiçi]: https://textblob.readthedocs.io/en/dev/
  11. Majumder, P., Mitra, M., & Chaudhuri, B. B. (2