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This paper investigates the nature of directional (asymmetric) similarity measures that aim to quantify distributional feature inclusion. We identify desired properties of such measures for lexical inference, specify a particular measure based on Average Precision that addresses these properties, and demonstrate the empirical benefit of directional measures for two different NLP datasets.<\/jats:p>","DOI":"10.1017\/s1351324910000124","type":"journal-article","created":{"date-parts":[[2010,10,11]],"date-time":"2010-10-11T11:13:10Z","timestamp":1286795590000},"page":"359-389","source":"Crossref","is-referenced-by-count":74,"title":["Directional distributional similarity for lexical inference"],"prefix":"10.1017","volume":"16","author":[{"given":"LILI","family":"KOTLERMAN","sequence":"first","affiliation":[]},{"given":"IDO","family":"DAGAN","sequence":"additional","affiliation":[]},{"given":"IDAN","family":"SZPEKTOR","sequence":"additional","affiliation":[]},{"given":"MAAYAN","family":"ZHITOMIRSKY-GEFFET","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2010,10,11]]},"reference":[{"key":"S1351324910000124_ref40","unstructured":"Patwardhan S. 2003. 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