Comparison of Numeral Strings Interpretation: Rule-Based and Feature-Based N-Gram Methods

摘要

This paper describes a performance comparison for two approaches to numeral string interpretation: manually generated rule-based interpretation of numerals and strings including numerals vs automatically generated feature-based interpretation. The system employs three interpretation processes: word trigram construction with a tokeniser, rule-based processing of number strings, and n-gram based classification. We extracted numeral strings from 378 online newspaper articles, finding that, on average, they comprised about 2.2% of the words in the articles. For feature-based interpretation, we tested on 11 datasets, with random selection of sample data to extract tabular feature-based constraints. The rule-based approach resulted in 86.8% precision and 77.1% recall ratio. The feature-based interpretation resulted in 83.1% precision and 74.5% recall ratio.