Paper: Separating Disambiguation from Composition in Distributional Semantics

ACL ID W13-3513
Title Separating Disambiguation from Composition in Distributional Semantics
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2013
Authors

Most compositional-distributional models of meaning are based on ambiguous vec- tor representations, where all the senses of a word are fused into the same vec- tor. This paper provides evidence that the addition of a vector disambiguation step prior to the actual composition would be beneficial to the whole process, produc- ing better composite representations. Fur- thermore, we relate this issue with the current evaluation practice, showing that disambiguation-based tasks cannot reli- ably assess the quality of composition. Us- ing a word sense disambiguation scheme based on the generic procedure of Sch?tze (1998), we first provide a proof of con- cept for the necessity of separating dis- ambiguation from composition. Then we demonstrate the benefits of an ?unambigu- ous? system on a com...