Arabic parsing using grammar transforms

We investigate Arabic Context Free Grammar parsing with dependency annotation comparing lexicalised and unlexicalised parsers. We study how morphosyntactic as well as function tag information percolation in the form of grammar transforms (Johnson, 1998, Kulick et al., 2006) affects the performance of a parser and helps dependency assignment. We focus on the three most frequent functional tags in the Arabic Penn Treebank: subjects, direct objects and predicates . We merge these functional tags with their phrasal categories and (where appropriate) percolate case information to the non-terminal (POS) category to train the parsers. We then automatically enrich the output of these parsers with full dependency information in order to annotate trees with Lexical Functional Grammar (LFG) f-structure equations with produce f-structures, i.e. attribute-value matrices approximating to basic predicate-argument-adjunct structure representations. We present a series of experiments evaluating how well lexicalized, history-based, generative (Bikel) as well as latent variable PCFG (Berkeley) parsers cope with the enriched Arabic data. We measure quality and coverage of both the output trees and the generated LFG f-structures. We show that joint functional and morphological information percolation improves both the recovery of trees as well as dependency results in the form of LFG f-structures

Treebank-based acquisition of LFG parsing resources for French

Motivated by the expense in time and other resources to produce hand-crafted grammars, there has been increased interest in automatically obtained wide-coverage grammars from treebanks for natural language processing. In particular, recent years have seen the growth in interest in automatically obtained deep resources that can represent information absent from simple CFG-type structured treebanks and which are considered to produce more language-neutral linguistic representations, such as dependency syntactic trees. As is often the case in early pioneering work on natural language processing, English has provided the focus of first efforts towards acquiring deep-grammar resources, followed by successful treatments of, for example, German, Japanese, Chinese and Spanish. However, no comparable large-scale automatically acquired deep-grammar resources have been obtained for French to date. The goal of this paper is to present the application of treebank-based language acquisition to the case of French. We show that with modest changes to the established parsing architectures, encouraging results can be obtained for French, with a best dependency structure f-score of 86.73%

Online Self-Indexed Grammar Compression

Although several grammar-based self-indexes have been proposed thus far, their applicability is limited to offline settings where whole input texts are prepared, thus requiring to rebuild index structures for given additional inputs, which is often the case in the big data era. In this paper, we present the first online self-indexed grammar compression named OESP-index that can gradually build the index structure by reading input characters one-by-one. Such a property is another advantage which enables saving a working space for construction, because we do not need to store input texts in memory. We experimentally test OESP-index on the ability to build index structures and search query texts, and we show OESP-index's efficiency, especially space-efficiency for building index structures.Comment: To appear in the Proceedings of the 22nd edition of the International Symposium on String Processing and Information Retrieval (SPIRE2015

Automatic annotation of the Penn-treebank with LFG f-structure information

Lexical-Functional Grammar f-structures are abstract syntactic representations approximating basic predicate-argument structure. Treebanks annotated with f-structure information are required as training resources for stochastic versions of unification and constraint-based grammars and for the automatic extraction of such resources. In a number of papers (Frank, 2000; Sadler, van Genabith and Way, 2000) have developed methods for automatically annotating treebank resources with f-structure information. However, to date, these methods have only been applied to treebank fragments of the order of a few hundred trees. In the present paper we present a new method that scales and has been applied to a complete treebank, in our case the WSJ section of Penn-II (Marcus et al, 1994), with more than 1,000,000 words in about 50,000 sentences

Aspects of teachers' reliance on English grammars and textbooks in teaching grammar

This graduation thesis deals with aspects of teachers’ reliance of grammars and textbooks when teaching grammar in the classroom, more specifically, with teachers in the third, fifth and seventh grade of primary school. First, readers will be presented with an outline of the literature related to the topic. The literature will be about grammar in general and its types, about foreign language teaching and its methods (mentioning the role of grammar in most methods), about teaching grammar, approaches to teaching grammar and types of practice and finally about previous research similar to the topic of the thesis. The next part, the case study, will provide the reader with information about the teachers, information about their primary teaching material, giving an analysis of the content of the student’s book and workbook, with an insight into their teaching practice based on lesson observation and into their own opinions about their teaching by analysing the conducted interview. In the last part the author will give a conclusion about the topic and the research and will try to emphasize the importance of the research for the improvement of grammar teaching in Croatian primary schools

Parsing with PCFGs and automatic f-structure annotation

The development of large coverage, rich unification- (constraint-) based grammar resources is very time consuming, expensive and requires lots of linguistic expertise. In this paper we report initial results on a new methodology that attempts to partially automate the development of substantial parts of large coverage, rich unification- (constraint-) based grammar resources. The method is based on a treebank resource (in our case Penn-II) and an automatic f-structure annotation algorithm that annotates treebank trees with proto-f-structure information. Based on these, we present two parsing architectures: in our pipeline architecture we first extract a PCFG from the treebank following the method of (Charniak,1996), use the PCFG to parse new text, automatically annotate the resulting trees with our f-structure annotation algorithm and generate proto-f-structures. By contrast, in the integrated architecture we first automatically annotate the treebank trees with f-structure information and then extract an annotated PCFG (A-PCFG) from the treebank. We then use the A-PCFG to parse new text to generate proto-f-structures. Currently our best parsers achieve more than 81% f-score on the 2400 trees in section 23 of the Penn-II treebank and more than 60% f-score on gold-standard proto-f-structures for 105 randomly selected trees from section 23

An Efficient Implementation of the Head-Corner Parser

This paper describes an efficient and robust implementation of a bi-directional, head-driven parser for constraint-based grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a review of the motivation for head-driven parsing strategies, and head-corner parsing in particular, a non-deterministic version of the head-corner parser is presented. A memoization technique is applied to obtain a fast parser. A goal-weakening technique is introduced which greatly improves average case efficiency, both in terms of speed and space requirements. I argue in favor of such a memoization strategy with goal-weakening in comparison with ordinary chart-parsers because such a strategy can be applied selectively and therefore enormously reduces the space requirements of the parser, while no practical loss in time-efficiency is observed. On the contrary, experiments are described in which head-corner and left-corner parsers implemented with selective memoization and goal weakening outperform `standard' chart parsers. The experiments include the grammar of the OVIS system and the Alvey NL Tools grammar. Head-corner parsing is a mix of bottom-up and top-down processing. Certain approaches towards robust parsing require purely bottom-up processing. Therefore, it seems that head-corner parsing is unsuitable for such robust parsing techniques. However, it is shown how underspecification (which arises very naturally in a logic programming environment) can be used in the head-corner parser to allow such robust parsing techniques. A particular robust parsing model is described which is implemented in OVIS.Comment: 31 pages, uses cl.st

Extracting Models from Source Code in Software Modernization

International audienceModel-driven software modernization is a discipline in which model-driven development (MDD) techniques are used in the modernization of legacy systems. When existing software artifacts are evolved, they must be transformed into models to apply MDD techniques such as model transformations. Since most modernization scenarios (e.g., application migration) involve dealing with code in general-purpose programming languages (GPL), the extraction of models from GPL code is an essential task in a model-based modernization process. This activity could be performed by tools to bridge grammarware and MDD technical spaces, which is normally carried out by dedicated parsers. Grammar-to-Model Transformation Language (Gra2MoL) is a domain-specific language (DSL) tailored to the extraction of models from GPL code. This DSL is actually a text-to-model transformation language which can be applied to any code conforming to a grammar. Gra2MoL aims to reduce the effort needed to implement grammarware-MDD bridges, since building dedicated parsers is a complex and time-consuming task. Like ATL and RubyTL languages, Gra2MoL incorporates the binding concept needed to write mappings between grammar elements and metamodel elements in a simple declarative style. The language also provides a powerful query language which eases the retrieval of scattered information in syntax trees. Moreover, it incorporates extensibility and grammar reuse mechanisms. This paper describes Gra2MoL in detail and includes a case study based on the application of the language in the extraction of models from Delphi code