Probabilistic parsing strategies (original) (raw)

A note on the expressive power of probabilistic context free grammars

2006

We examine the expressive power of probabilistic context free grammars (PCFGs), with a special focus on the use of probabilities as a mechanism for reducing ambiguity by filtering out unwanted parses. Probabilities in PCFGs induce an ordering relation among the set of trees that yield a given input sentence. PCFG parsers return the trees bearing the maximum probability for a given sentence, discarding all other possible trees. This mechanism is naturally viewed as a way of defining a new class of tree languages. We formalize the tree language thus defined, study its expressive power, and show that the latter is beyond context freeness. While the increased expressive power offered by PCFGs helps to reduce ambiguity, we show that, in general, it cannot be decided whether a PCFG removes all ambiguities.

Hybrid Parsing: Using Probabilistic Models as Predictors for a Symbolic Parser

In this paper we investigate the benefit of stochastic predictor components for the parsing quality which can be obtained with a rule-based dependency grammar. By in- cluding a chunker, a supertagger, a PP at- tacher, and a fast probabilistic parser we were able to improve upon the baseline by 3.2%, bringing the overall labelled accu- racy to 91.1% on the German NEGRA cor- pus. We attribute the successful integra- tion to the ability of the underlying gram- mar model to combine uncertain evidence in a soft manner, thus avoiding the prob- lem of error propagation.

An alternative method of training probabilistic LR parsers

2004

Abstract We discuss existing approaches to train LR parsers, which have been used for statistical resolution of structural ambiguity. These approaches are nonoptimal, in the sense that a collection of probability distributions cannot be obtained. In particular, some probability distributions expressible in terms of a context-free grammar cannot be expressed in terms of the LR parser constructed from that grammar, under the restrictions of the existing approaches to training of LR parsers.

An efficient probabilistic context-free parsing algorithm that computes prefix probabilities

1995

We describe an extension of Earley's parser for stochastic context-free grammars that computes the following quantities given a stochastic context-free grammar and an input string: a) probabilities of successive prefixes being generated by the grammar; b) probabilities of substrings being generated by the nonterminals, including the entire string being generated by the grammar; c) most likely (Viterbi) parse of the string; d) posterior expected number of applications of each grammar production, as required for reestimating rule probabilities. Probabilities (a) and (b) are computed incrementally in a single left-to-right pass over the input. Our algorithm compares favorably to standard bottom-up parsing methods for SCFGs in that it works efficiently on sparse grammars by making use of Earley's top-down control structure. It can process any context-free rule format without conversion to some normal form, and combines computations for (a) through (d) in a single algorithm. Finally, the algorithm has simple extensions for processing partially bracketed inputs, and for finding partial parses and their likelihoods on ungrammatical inputs.

A probabilistic top-down parser for minimalist grammars

2010

This paper describes a probabilistic top-down parser for minimalist grammars. Top-down parsers have the great advantage of having a certain predictive power during the parsing, which takes place in a left-to-right reading of the sentence. Such parsers have already been well-implemented and studied in the case of Context-Free Grammars (see for example [Roa01]), which are already top-down, but these are difficult to adapt to Minimalist Grammars, which generate sentences bottom-up. I propose here a way of rewriting Minimalist Grammars as Linear Context-Free Rewriting Systems, allowing us to easily create a top-down parser. This rewriting allows also to put a probabilistic field on these grammars, which can be used to accelerate the parser. I propose also a method of refining the probabilistic field by using algorithms used in data compression.

Probabilistic GLR parsing: A new formalization and its impact on parsing performance

Journal of Natural …, 1998

This paper presents a new formalization of probabilistic GLR (PGLR) language modeling for statistical parsing. Our model inherits its essential features from Briscoe and Carroll's generalized probabilistic LR model , which takes context of parse derivation into account by assigning a probability to each LR parsing action according to its left and right context. Briscoe and Carroll's model, however, has a drawback in that it is not formalized in any probabilistically well-founded way, which may degrade its parsing performance. Our formulation overcomes this drawback with a few significant refinements, while maintaining all the advantages of Briscoe and Carroll's modeling. In this paper, we discuss the formal and qualitative aspects of our PGLR model, illustrating the qualitative differences between Briscoe and Carroll's model and our model, and their expected impact on parsing performance.

Advances in Probabilistic and Other Parsing Technologies

Springer eBooks, 2000

We address the issue of how to associate frequency information with lexicalized grammar formalisms, using Lexicalized Tree Adjoining Grammar as a representative framework. We consider systematically a number of alternative probabilistic frameworks, evaluating their adequacy from both a theoretical and empirical perspective using data from existing large treebanks. We also propose three orthogonal approaches for backing off probability estimates to cope with the large number of parameters involved.

Parsing with Principles and Probabilities

1994

This paper is an attempt to bring together two approaches to language analysis. The possible use of probabilistic information in principle-based grammars and parsers is considered, including discussion on some theoretical and computational problems that arise. Finally a partial implementation of these ideas is presented, along with some preliminary results from testing on a small set of sentences.