학술논문

A Challenging Data Set for Evaluating Part-of-speech Taggers
Document Type
Source
16th International Conference on Agents and Artificial Intelligence, Rome, Italy International Conference on Agents and Artificial Intelligence. 2:79-86
Subject
Natural Language Processing
Sequence Labeling
Part-of-speech Tagging
Language
English
ISSN
2184433X
21843589
Abstract
We introduce a novel, challenging test set for part-of-speech (POS) tagging, consisting of sentences in which only one word is POS-tagged. First derived from Wiktionary, and then manually curated, it is intended as an out-of-sample test set for POS taggers trained over larger data sets. Sentences were selected such that at least one of four standard benchmark taggers would incorrectly tag the word under consideration for a given sentence, thus identifying challenging instances of POS tagging. Somewhat surprisingly, we find that the benchmark taggers often fail on rather straightforward instances of POS tagging, and we analyze these failures in some detail. We also compute the performance of a state-of-the-art DNN-based POS tagger over our set, obtaining an accuracy of around 0.87 for this out-of-sample test, far below its reported performance in the literature. Also for this tagger, we find instances of failure even in rather simple cases.