This paper presents a statistical model which trains from a corpus annotated with Part-Of-Speech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6 % ) . The model can be classified as a Maximum Entropy model and simultaneously uses many contextual " features " to predict the POS tag . Furthermore , this paper demonstrates the use of specialized features to model difficult tagging decisions , discusses the corpus consistency problems discovered during the implementation of these features , and proposes a training strategy that mitigates these problems . Many natural language tasks require the accurate assignment of Part-Of-Speech ( POS ) tags to previously unseen text . Due to the availability of large corpora which have been manually annotated with POS information , many taggers use annotated text to " learn " either probability distributions or rules and use them to automatically assign POS tags to unseen text . The experiments in this paper were conducted on the Wall Street Journal corpus from the Penn Treebank project ( Marcus et al . , 1994 ) , although the model can trai~n from any large corpus annotated with POS tags . Since most realistic natural language applications must process words that were never seen before in training data , all experiments in this paper are conducted on test data that include unknown words . Several recent papers ( Brill , 1994 , Magerman , 1995 ) have reported 96.5 % tagging accuracy on the Wall St. Journal corpus . The experiments in this paper test the hypothesis that better use of context will improve the accuracy . A Maximum Entropy model is well-suited for such experiments since it corn-bines diverse forms of contextual information in a principled manner , and does not impose any distributional assumptions on the training data . Previous uses of this model include language modeling ( Lau et al . , 1993 ) , machine translation ( Berger et al . , 1996 ) , prepositional phrase attachment ( Ratnaparkhi et al . , 1994 ) , and word morphology ( Della Pietra et al . , 1995 ) . This paper briefly describes the maximum entropy and maximum likelihood properties of the model , features used for POS tagging , and the experiments on the Penn Treebank Wall St. Journal corpus . It then discusses the consistency problems discovered during an attempt to use specialized features on the word context . Lastly , the results in this paper are compared to those from previous work on POS tagging . The Maximum Entropy model is an extremely flexible technique for linguistic modelling , since it can use a virtually unrestricted and rich feature set in the framework of a probability model . The implementation in this paper is a state-of-the-art POS tagger , as evidenced by the 96.6 % accuracy on the unseen Test set , shown in Table 11 . The model with specialized features does not perform much better than the baseline model , and further discovery or refinement of word-based features is difficult given the inconsistencies in the training data . A model trained and tested on data from a single annotator performs at .5 % higher accuracy than the baseline model and should produce more consistent input for applications that require tagged text .

Challenge: Because natural language tasks require the part-of-speech tags to previously unseen texts, taggers need to be tested on test data which contain such unseen words.
Approach: They present a Maximum Entropy model that simultaneously uses contextual features trained on annotated part-of-speech tags.
Outcome: They show the proposed model achieves the state-of-the-art on unseen corpus and the model with specialized features does not perform much better than the baseline.