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https://www.cs.utexas.edu/users/ml/papers/garrette.naacl13.pdf
part-of-speech taggers has been based on un-realistic assumptions about the amount and quality of training data. For this paper, we attempt to create true low-resource scenarios by allowing a linguist just two hours to anno-tate data and evaluating on the languages Kin-yarwanda and Malagasy. Given these severely limited amounts of either type supervision
http://luthuli.cs.uiuc.edu/~daf/courses/signals%20ai/papers/hmms/h92-1022.pdf
guage processing where statistical techniques have been more successful than rule-based methods. In this paper, we present a simple rule-based part of speech tagger which automati- cally acquires its rules and tags with accuracy comparable to stochastic taggers. The rule-based tagger has many ad-
http://www.mysmu.edu/faculty/davidlo/papers/saner15-pos.pdf
POS taggers are accurate when applied on software artifacts, and which of the POS taggers perform the best for software artifacts. The evaluation of taggers' accuracy is important considering that off-the-shelf POS taggers are often trained on general English text (e.g., news article) that might contain little (if any) software engineering ...
https://faculty.washington.edu/fxia/courses/LING572/decison_tree99.pdf
Marialdo, 1984; Church, 1988). HMM taggers, also known as n-gram taggers, make the drastic assumption that only the n-1 words have any effect on the probabilities of the next word (a common n is 3, hence the term trigrams). While this assumption is clearly false, surprisingly n-gram taggers can obtain very high rates of
https://www.researchgate.net/publication/302634595_It's_Just_Paint_Street_Taggers'_Use_of_Neutralization_Techniques
Drawing on interviews with 25 active juvenile street taggers in a large metropolitan area of Texas, this study explores their use of Sykes and Matza’s five techniques of neutralizations and the ...
https://cs230.stanford.edu/blog/namedentity/
We explore the problem of Named Entity Recognition (NER) tagging of sentences. The task is to tag each token in a given sentence with an appropriate tag such as Person, Location, etc. John lives in New York B-PER O O B-LOC I-LOC. Our dataset will thus need to load both the sentences and labels. We will store those in 2 different files, a ...
https://people.eecs.berkeley.edu/~klein/cs288/sp10/slides/SP10%20cs288%20lecture%201%20--%20introduction%20(2PP).pdf
Experiments will take minutes to hours, with efficient code Recommendation: start assignments early Communication: ... Learn the issues and techniques of statistical NLP ... taggers, parsers, translation systems) Be able to read current research papers in the field See where the holes in the field still are! 10
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