Name
Abstract | ||
|---|---|---|
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 pipelineu0027s 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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | Pipeline transport,Computer science,Part-of-speech tagging,Natural language processing,Artificial intelligence,Sentence |
DocType | Volume | Citations |
Journal | abs/1708.00241 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vishaal Jatav | 1 | 0 | 0.34 |
| Ravi Teja | 2 | 0 | 0.34 |
| Srini Bharadwaj | 3 | 0 | 0.34 |
| Venkat Srinivasan | 4 | 20 | 5.79 |