Paper: Spectral Unsupervised Parsing with Additive Tree Metrics

ACL ID P14-1100
Title Spectral Unsupervised Parsing with Additive Tree Metrics
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

We propose a spectral approach for un- supervised constituent parsing that comes with theoretical guarantees on latent struc- ture recovery. Our approach is grammar- less ? we directly learn the bracketing structure of a given sentence without us- ing a grammar model. The main algorithm is based on lifting the concept of additive tree metrics for structure learning of la- tent trees in the phylogenetic and machine learning communities to the case where the tree structure varies across examples. Although finding the ?minimal? latent tree is NP-hard in general, for the case of pro- jective trees we find that it can be found using bilexical parsing algorithms. Empir- ically, our algorithm performs favorably compared to the constituent context model of Klein and Manning (2002) without the need...