Discriminative Log-Linear Grammars with Latent Variables
نویسندگان
چکیده
. Grammars with Latent Variables Given a treebank over a set of categories learn an optimally refined grammar for parsing. The parameters can be learned with an Expectation Maximization algorithm. The E-Step involves computing expectations over derivations corresponding to the observed trees. These expectations are normalized in the M-Step to update the rewrite probabilities: Grammar Learning The observed treebank categories are too coarse because the rewrite probabilities depend on context.
منابع مشابه
Probabilistic Frame-Semantic Parsing
This paper contributes a formalization of frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. It finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two featurebased, discriminative probabilistic (log-linear) mo...
متن کاملSparse Multi-Scale Grammars for Discriminative Latent Variable Parsing
We present a discriminative, latent variable approach to syntactic parsing in which rules exist at multiple scales of refinement. The model is formally a latent variable CRF grammar over trees, learned by iteratively splitting grammar productions (not categories). Different regions of the grammar are refined to different degrees, yielding grammars which are three orders of magnitude smaller tha...
متن کاملHigh-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation constraints estimated with labeled data. This approach encourages the generative model to discover latent structure that is relevant to a prediction task. We estimate parameters with a coordinate ascent algorithm, one s...
متن کاملFeature-Rich Log-Linear Lexical Model for Latent Variable PCFG Grammars
Context-free grammars with latent annotations (PCFG-LA) have been found to be effective for parsing many languages; however, currently their lexical model may be subject to over-fitting and requires language engineering to handle out-ofvocabulary (OOV) words. Inspired by previous studies that have incorporated rich features into generative models, we propose to use a feature-rich log-linear lex...
متن کاملSEMAFOR 1.0: A Probabilistic Frame-Semantic Parser
An elaboration on (Das et al., 2010), this report formalizes frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. SEMAFOR 1.0 finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two feature-based, discriminative ...
متن کامل