Paper: Vector space semantics with frequency-driven motifs

ACL ID P14-1060
Title Vector space semantics with frequency-driven motifs
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

Traditional models of distributional se- mantics suffer from computational issues such as data sparsity for individual lex- emes and complexities of modeling se- mantic composition when dealing with structures larger than single lexical items. In this work, we present a frequency- driven paradigm for robust distributional semantics in terms of semantically cohe- sive lineal constituents, or motifs. The framework subsumes issues such as dif- ferential compositional as well as non- compositional behavior of phrasal con- situents, and circumvents some problems of data sparsity by design. We design a segmentation model to optimally par- tition a sentence into lineal constituents, which can be used to define distributional contexts that are less noisy, semantically more interpretable, and lingu...