Authors:
Mattias Wahde
;
Minerva Suvanto
and
Marco L. Della Vedova
Affiliation:
Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
Keyword(s):
Part-of-Speech Tagging, Natural Language Processing, Sequence Labeling.
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.