From direct tagging to Tagging with sentences compression

TL;DR

This paper compares direct tagging and sentence compression-based tagging, proposing that sentence compression can improve data extraction precision in irregular data scenarios without sacrificing recall.

Contribution

It introduces a novel approach of using sentence compression to enhance tagging accuracy in complex, irregular data environments.

Findings

Sentence compression improves tagging precision.

The method maintains recall levels.

Enhanced data extraction accuracy in irregular contexts.

Abstract

In essence, the two tagging methods (direct tagging and tagging with sentences compression) are to tag the information we need by using regular expression which basing on the inherent language patterns of the natural language. Though it has many advantages in extracting regular data, Direct tagging is not applicable to some situations. if the data we need extract is not regular and its surrounding words are regular is relatively regular, then we can use information compression to cut the information we do not need before we tagging the data we need. In this way we can increase the precision of the data while not undermine the recall of the data.