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Part‐of‐speech tagging
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-44816-0_7openalex-snapshot
Tagging with Small Training Corpora
inferredcites →https://doi.org/10.1007/978-3-540-30228-5_40openalex-snapshot
Unsupervised Training of a Finite-State Sliding-Window Part-of-Speech Tagger
inferredcites →https://doi.org/10.1007/978-94-009-0683-9_1openalex-snapshot
Probability Theory as Logic
inferredcites →https://doi.org/10.1017/s1351324901002662openalex-snapshot
Randomized rule selection in transformation-based learning: a comparative study
inferredcites →https://doi.org/10.1023/a:1007673816718openalex-snapshot
A Machine Learning Approach to POS Tagging
inferredcites →https://doi.org/10.1109/fskd.2007.86openalex-snapshot
A New Method of the Automatically Marked Chinese Part of Speech Based on Gaussian Prior Smoothing Maximum Entropy Model
inferredcites →https://doi.org/10.1109/icmlc.2002.1167446openalex-snapshot
Chinese part of speech tagging based on maximum entropy method
inferredcites →https://doi.org/10.1109/icmlc.2005.1527595openalex-snapshot
Chinese POS tagging based on bilexical co-occurrences
inferredcites →https://doi.org/10.1109/tit.1967.1054010openalex-snapshot
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
inferredcites →https://doi.org/10.1145/321172.321180openalex-snapshot
A Computational Approach to Grammatical Coding of English Words
inferredcites →https://doi.org/10.1145/956863.956921openalex-snapshot
Lattice-based tagging using support vector machines
inferredcites →https://doi.org/10.1145/956919.956921openalex-snapshot
Lattice-based tagging using support vector machines
inferredcites →https://doi.org/10.1201/9781420085938openalex-snapshot
Handbook of Natural Language Processing
inferredcites →https://doi.org/10.1214/aoms/1177697196openalex-snapshot
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
inferredcites →https://doi.org/10.2307/415888openalex-snapshot
Statistical language learning . By Eugene Charniak. Cambridge, MA: MIT Press, 1993. Pp. xiii, 170.
inferredcites →https://doi.org/10.2307/417436openalex-snapshot
Survey of the state of the art in human language technology . Ed. by Giovanni Varile, Antonio Zampolli, Ronald Cole, Joseph Mariani, Hans Uszkoreit, Annie Zaenen, and Victor Zue. (Studies in natural language processing.) Cambridge: Cambridge University Press. Pp. xx, 513. Cloth $49.95.
inferredcites →https://doi.org/10.3115/1075527.1075553openalex-snapshot
A simple rule-based part of speech tagger
inferredcites →https://doi.org/10.3115/1119250.1119261openalex-snapshot
A Chinese efficient analyser integrating word segmentation, part-of-speech tagging, partial parsing and full parsing
inferredcites →https://doi.org/10.3115/981623.981638openalex-snapshot
Acquiring disambiguation rules from text
inferredcites →https://doi.org/10.5555/234285.234289openalex-snapshot
A maximum entropy approach to natural language processing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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How far can Deep Learning Improve Arabic Part of Speech Tagging?
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Aspect-Based Sentiment Analysis Using Deep Learning and Ensemble Modelling: A Systematic Review
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A Comparison of the Several Speech Tagging Models Used in NLP
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Corpus based part-of-speech tagging
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