been singing" and other constructions using these special "verbs", but not free sequences of verbs in general. With distinct tags, an HMM can often predict the correct finer-grained tag, rather than being equally content with any "verb" in any slot. Similarly, with neural network approaches the weights for short-range collocations may conflate very different cases, making it harder to achieve comparable results (the information has to be discovered and encoded at other levels). Some have argued that this benefit is moot because a program can merely check the spelling: "this 'verb' is a 'do' because of the spelling". However, this fails for erroneous spellings even though they can often be tagged accurately by HMMs. See also Semantic net Sliding window based part-of-speech tagging Trigram tagger Word sense disambiguation References Charniak, Eugene. 1997. "Statistical Techniques for Natural Language Parsing". AI Magazine 18(4):33–44. Hans van Halteren, Jakub Zavrel, Walter Daelemans. 2001. Improving Accuracy in NLP Through Combination of Machine Learning Systems. Computational Linguistics. 27(2): 199–229. PDF DeRose, Steven J. 1990. "Stochastic Methods for Resolution of Grammatical Category Ambiguity in Inflected and Uninflected Languages." Ph.D. Dissertation. Providence, RI: Brown University Department of Cognitive and Linguistic Sciences. Electronic Edition available at D.Q. Nguyen, D.Q. Nguyen, D.D. Pham and S.B. Pham (2016). "A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-Of-Speech Tagging." AI Communications, vol. 29, no. 3, pages 409–422. [.pdf] Corpus linguistics Tasks of natural language processing Markov models Word-sense disambiguation