Deterministic choices in a data-driven parser.

Data-driven parsers rely on recommendations from parse models, which are generated from a set of training data using a machine learning classifier, to perform parse operations. However, in some cases a parse model cannot recommend a parse action to a parser unless it learns from the training data what parse action(s) to take in every possible situation. Therefore, it will be hard for a parser to make an informed decision as to what parse operation to perform when a parse model recommends no/several parse actions to a parser. Here we examine the effect of various deterministic choices on a datadriven parser when it is presented with no/several recommendation from a parse model

Towards the Development of a Hybrid Parser for Natural Languages

In order to understand natural languages, we have to be able to determine the relations between words, in other words we have to be able to \u27parse\u27 the input text. This is a difficult task, especially for Arabic, which has a number of properties that make it particularly difficult to handle. There are two approaches to parsing natural languages: grammar-driven and data-driven. Each of these approaches poses its own set of problems, which we discuss in this paper. The goal of our work is to produce a hybrid parser, which retains the advantages of the data-driven approach but is guided by grammar rules in order to produce more accurate output. This work consists of two stages: the first stage is to develop a baseline data-driven parser, which is guided by a machine learning algorithm for establishing dependency relations between words. The second stage is to integrate grammar rules into the baseline parser. In this paper, we describe the first stage of our work, which is now implemented, and a number of experiments that have been conducted on this parser. We also discuss the result of these experiments and highlight the different factors that are affecting parsing speed and the correctness of the parser results

The application of constraint rules to data-driven parsing.

In this paper, we show an approach to extracting different types of constraint rules from a dependency treebank. Also, we show an approach to integrating these constraint rules into a dependency data-driven parser, where these constraint rules inform parsing decisions in specific situations where a set of parsing rule (which is induced from a classifier) may recommend several recommendations to the parser. Our experiments have shown that parsing accuracy could be improved by using different sets of constraint rules in combination with a set of parsing rules. Our parser is based on the arc-standard algorithm of MaltParser but with a number of extensions, which we will discuss in some detail

The selection of classifiers for a data-driven parser.

There is a large number of classifiers that can be used for generating a parse model; i.e., as an oracle for guiding data-driven parsers when parsing natural languages. In this paper we present a general and simple approach for generating a parse model. Additionally, we present a large number of experiments on various classifiers. We also present the effect of various parse models, which are generated from different classifiers, on a data-driven parser to see the way each model contributes to parsing performance

Parser Hybridisation for Natural Languages

Identifying and establishing structural relations between words in natural language sentences is called Parsing. Ambiguities in natural languages make parsing a difficult task. Parsing is more difficult when dealing with a structurally complex natural language such as Arabic, which contains a number of properties that make it particularly difficult to handle. In this paper, we briefly highlight some of the complex structure of Arabic, and we identify different parsing approaches (grammar-driven and data-driven approaches) and briefly discuss their limitations. Our main goal is to combine different parsing approaches and produce a hybrid parser, which retains the advantages of data-driven approaches but is guided by grammatical rules to produce more accurate results. We describe a novel technique for directly combining different parsing approaches. Results for initial experiments that we have conducted in this work, and our plans for future work is also presented