TY  - INPR
A1  - Kübler, Sandra
A1  - Wagner, Andreas
T1  - Evaluating POS tagging under sub-optimal conditions : or: does meticulousness pay?
N2  - In this paper, we investigate the role of sub-optimality in training data for part-of-speech tagging. In particular, we examine to what extent the size of the training corpus and certain types of errors in it affect the performance of the tagger. We distinguish four types of errors: If a word is assigned a wrong tag, this tag can belong to the ambiguity class of the word (i.e. to the set of possible tags for that word) or not; furthermore, the major syntactic category (e.g. "N" or "V") can be correctly assigned (e.g. if a finite verb is classified as an infinitive) or not (e.g. if a verb is classified as a noun). We empirically explore the decrease of performance that each of these error types causes for different sizes of the training set. Our results show that those types of errors that are easier to eliminate have a particularly negative effect on the performance. Thus, it is worthwhile concentrating on the elimination of these types of errors, especially if the training corpus is large.
KW  - speech tagging
Y1  - 2000
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/9876
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-1110556
UR  - http://cl.indiana.edu/~skuebler/papers/acidca.ps
N1  - Erschienen in: Proceedings of International Conference on Artificial and Computational Intelligence for Decision, Control and Automation in Engineering and Industrial Applications (ACIDCA 2000), March 2000
ER  -