Precision-Recall curves present the precision measure for selected points of recall, e.g., the precision for a case we require a recall of 0.1, the precision for a case we require a recall of 0.2, etc. (the required recall can be achieved by tuning the threshold of our decision). In this way, we can get more informative picture of an algorithm’s performance (i.e., not just one single F1-measure which encodes the trade-off between the precision and the recall of one specific threshold).

Here you can find an example for such curves, for several examined methods - you should provide such a figure for each of the 20% and 100% runs, where each figure is composed of three curves: PMI-LIN, TFIDF-COS, and DICE-COVER.

Note: There's a simple tool in Excel for generating such figures for given pairs of precision-recall (=you just have to provide the precision value for each of the 0.1, 0.2… 1 recall points).