Pertinence is computed by various different measures, the most recent of which is balAPinc (Kotlerman et al., 2010), where LIN is Lin's similarity (Lin, 1998) and APinc is an asymmetric measure: APinc applies the DIH via the idea that features with high values in F( − → u ) must also have high values in In the above formula, f r is the feature in F( − → u ) with rank r; P (r) is the precision at rank r; and rel (f r ) is a weight computed as follows: where shows the rank of feature f within the entailed vector.
naturally these models are used extensively to measure the semantic similarity of words (Turney and Pantel, 2010).