Improving Part-of-Speech Tagging for NLP Pipelines

This paper outlines the results of sentence level linguistics based rules for improving part-of-speech tagging. It is well known that the performance of complex NLP systems is negatively affected if one of the preliminary stages is less than perfect. Errors in the initial stages in the pipeline have a snowballing effect on the pipeline’s end performance. We have created a set of linguistics based rules at the sentence level which adjust part-ofspeech tags from state-of-the-art taggers. Comparison with state-of-the-art taggers on widely used benchmarks demonstrate significant improvements in tagging accuracy and consequently in the quality and accuracy of NLP systems. Index Terms — Computational Linguistics, Natural Language Understanding, RAGE AI, Part-of-Speech Tagging, Evaluation Improving Part-of-Speech Tagging Jatav et. al. Copyright © 2017, RAGE Frameworks Inc. (www.rageframeworks.com). All rights reserved.