Autor:	Kober, Thomas, Weeds, Julie, Reffin, Jeremy, Weir, David
Rok vydání:	2017
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition. Comment: to appear at ACL 2017 (short papers)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1704.06692 Zobrazit plný text záznamu View this record from Arxiv