Stanford Tagger

A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like ’noun-plural’. This software is a Java implementation of the log-linear part-of-speech taggers described in these papers (if citing just one paper, cite the 2003 one): Kristina Toutanova and Christopher D. Manning. 2000. Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), pp. 63-70. Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLT-NAACL 2003, pp. 252-259. The tagger was originally written by Kristina Toutanova. Since that time, Dan Klein, Christopher Manning, William Morgan, Anna Rafferty, Michel Galley, and John Bauer have improved its speed, performance, usability, and support for other languages.

References in zbMATH (referenced in 25 articles )

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  1. Vilares Ferro, Manuel; Darriba Bilbao, Víctor M.; Vilares Ferro, Jesús: Absolute convergence and error thresholds in non-active adaptive sampling (2022)
  2. Ayats, Hugo; Cellier, Peggy; Ferré, Sébastien: Extracting relations in texts with concepts of neighbours (2021)
  3. Zhang, Meishan; Li, Zhenghua; Fu, Guohong; Zhang, Min: Dependency-based syntax-aware word representations (2021)
  4. Lv, Shaoqing; Xiang, Ju; Feng, Jingyu; Wang, Honggang; Lu, Guangyue; Li, Min: Community enhancement network embedding based on edge reweighting preprocessing (2020)
  5. Zhang, Jiajun; Zhou, Long; Zhao, Yang; Zong, Chengqing: Synchronous bidirectional inference for neural sequence generation (2020)
  6. Evert, Stefan; Heinrich, Philipp; Henselmann, Klaus; Rabenstein, Ulrich; Scherr, Elisabeth; Schmitt, Martin; Schröder, Lutz: Combining machine learning and semantic features in the classification of corporate disclosures (2019)
  7. Fernández-González, Daniel; Gómez-Rodríguez, Carlos: Faster shift-reduce constituent parsing with a non-binary, bottom-up strategy (2019)
  8. Dobriban, Edgar; Wager, Stefan: High-dimensional asymptotics of prediction: ridge regression and classification (2018)
  9. Kanavos, Andreas; Nodarakis, Nikolaos; Sioutas, Spyros; Tsakalidis, Athanasios; Tsolis, Dimitrios; Tzimas, Giannis: Large scale implementations for Twitter sentiment classification (2017)
  10. Chen, Wenliang; Zhang, Min; Zhang, Yue; Duan, Xiangyu: Exploiting meta features for dependency parsing and part-of-speech tagging (2016)
  11. Lima, Ana Carolina E. S.; de Castro, Leandro Nunes; Corchado, Juan M.: A polarity analysis framework for Twitter messages (2015)
  12. Zhang, Jie; Wang, Xiaoyin; Hao, Dan; Xie, Bing; Zhang, Lu; Mei, Hong: A survey on bug-report analysis (2015) ioport
  13. Du, Gengshen; Ruhe, Guenther: Two machine-learning techniques for mining solutions of the ReleasePlanner(^\textTM) decision support system (2014) ioport
  14. Angelov, Krasimir; Camilleri, John J.; Schneider, Gerardo: A framework for conflict analysis of normative texts written in controlled natural language (2013)
  15. Qu, Wen; Song, Kai-Song; Zhang, Yi-Fei; Feng, Shi; Wang, Da-Ling; Yu, Ge: A novel approach based on multi-view content analysis and semi-supervised enrichment for movie recommendation (2013) ioport
  16. Lutbat, Yadamsuren; Enkhbat, Rentsen; Hwang, Won-Joo: Large margin shortest path routing (2012)
  17. Savoy, Jacques; Zubaryeva, Olena: Simple and efficient classification scheme based on specific vocabulary (2012)
  18. Gleich, David F.; Wang, Ying; Meng, Xiangrui; Ronaghi, Farnaz; Gerritsen, Margot; Saberi, Amin: Some computational tools for digital archive and metadata maintenance (2011)
  19. Sutton, Charles; McCallum, Andrew: An introduction to conditional random fields (2011)
  20. Zouaq, Amal; Gasevic, Dragan; Hatala, Marek: Towards open ontology learning and filtering (2011) ioport

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