F-measure

In the UNL System, the F-measure (or F1-score) is the measure of a grammar's accuracy. It considers both the precision and the recall of the grammar to compute the score, according to the formula

F-measure = 2 x ( (precision x recall) / (precision + recall) )

In the above:

• precision is the number of correct results divided by the number of all returned results
• recall is the number of correct results divided by the number of results that should have been returned

A result is considered "RETURNED" in the following cases:

• In UNLization, when the output is a graph (i.e., all the nodes are interlinked) made only of UW's (i.e., without natural language words)
• In NLization, when the output is a list of natural language words (i.e., without any UW).

A result is considered "CORRECT" in the following cases:

• In UNLization, when
  • The discrepancy of relations between the actual and the expected output is less than 0.3; AND
  • The discrepancy of UW's between the actual and the expected output is less than 0.3; AND
  • The overall discrepancy is less than 0.5;

WHERE
Discrepancy of relations is calculated by the formula:

(exceding_relations + missing_relations)/total_relations

Discrepancy of UW's is calculated by the formula:

(exceding_UW + missing_UW)/total_UW

Overall discrepancy is calculated by the formula:

((3*(exceding_relations+missing_relations))+(2*(exceding_UW+missing_UW)+(exceding_attribute+missing_attribute))/((3*total_relations)+(2*total_UW)+(total_attribute))

• • WHERE
  • exceding_relations is the number of relations present in the actual output but absent from the expected output
  • missing_relations is the number of relations absent from the actual output but present in the expected output
  • total_relations is the sum of the total number of relations in the actual output and in the expected output
  • exceding_UW is the number of UW's present in the actual output but absent from the expected output
  • missing_UW is the number of UW's absent from the actual output but present in the expected output
  • total_UW is the sum of the total number of UW's in the actual output and in the expected output
  • exceding_attribute is the number of attributes present in the actual output but absent from the expected output
  • missing_attribute is the number of attributes absent from the actual output but present in the expected output
  • total_attribute is the sum of the total number of attributes in the actual output and in the expected output
• In NLization, when the difference between the actual result and the expected result is less than 30%
  • The difference between the actual and the expected result is calculated by the formula

(exceding_words+missing_words)/(total_words)

• • WHERE

• • • exceding_words is the number of words present in the actual output but absent from the expected output
    • missing_words is the number of words absent from the actual output but present in the expected output
    • total_words is the sum of the total number of words in the actual output and in the expected output