Improving Part-of-Speech Tagging for NLP Pipelines
This work addresses the issue of error propagation in NLP pipelines for users relying on accurate tagging, though it appears incremental as it builds on existing taggers.
The paper tackled the problem of part-of-speech tagging errors negatively impacting NLP pipelines by developing sentence-level linguistic rules to adjust tags from state-of-the-art taggers, resulting in significant improvements in tagging accuracy and overall NLP system quality.
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-of-speech 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.