Fast Exact Inference with a Factored Model for Natural Language Parsing
نویسندگان
چکیده
We present a novel generative model for natural language tree structures in which semantic (lexical dependency) and syntactic (PCFG) structures are scored with separate models. This factorization provides conceptual simplicity, straightforward opportunities for separately improving the component models, and a level of performance comparable to similar, non-factored models. Most importantly, unlike other modern parsing models, the factored model admits an extremely effective A* parsing algorithm, which enables efficient, exact inference.
منابع مشابه
An improved joint model: POS tagging and dependency parsing
Dependency parsing is a way of syntactic parsing and a natural language that automatically analyzes the dependency structure of sentences, and the input for each sentence creates a dependency graph. Part-Of-Speech (POS) tagging is a prerequisite for dependency parsing. Generally, dependency parsers do the POS tagging task along with dependency parsing in a pipeline mode. Unfortunately, in pipel...
متن کاملFactored A* Search for Models over Sequences and Trees
We investigate the calculation of A* bounds for sequence and tree models which are the explicit intersection of a set of simpler models or can be bounded by such an intersection. We provide a natural viewpoint which unifies various instances of factored A* models for trees and sequences, some previously known and others novel, including multiple sequence alignment, weighted finitestate transduc...
متن کاملIncremental Sigmoid Belief Networks for Grammar Learning
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network’s model structure is a function of the predicted output structure. These incremental sigmoid belief networks (ISBNs) make decoding possible because inference with partial output structures does not require summing over the unboundedly many compatible model structures, due to their d...
متن کاملStructured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
Reasoning on large and complex real–world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work well on specific models or only on parts of general models. Consequently, a system that can intelligently apply these inference algorithms to different parts of a model for fast ...
متن کاملPolynomial Time Joint Structural Inference for Sentence Compression
We propose two polynomial time inference algorithms to compress sentences under bigram and dependency-factored objectives. The first algorithm is exact and requires O(n6) running time. It extends Eisner’s cubic time parsing algorithm by using virtual dependency arcs to link deleted words. Two signatures are added to each span, indicating the number of deleted words and the rightmost kept word w...
متن کامل