Automatic F-Structure Annotation from the AP Treebank

We present a method for automatically annotating treebank resources with functional structures. The method defines systematic patterns of correspondence between partial PS configurations and functional structures. These are applied to PS rules extracted from treebanks. The set of techniques which we have developed constitute a methodology for corpus-guided grammar development. Despite the widespread belief that treebank representations are not very useful in grammar development, we show that systematic patterns of c-structure to f-structure correspondence can be simply and successfully stated over such rules. The method is partial in that it requires manual correction of the annotated grammar rules

Conversational Grammar- Feminine Grammar? A Sociopragmatic Corpus Study

One area in language and gender research that has so far received only little attention is the extent to which the sexes make use of what recent corpus research has termed “conversational grammar.” The author’s initial findings have suggested that the majority of features distinctive of conversational grammar may be used predominantly by female speakers. This article reports on a study designed to test the hypothesis that conversational grammar is “feminine grammar” in the sense that women’s conversational language is more adapted to the conversational situation than men’s. Based on data from the conversational subcorpus of the British National Corpus and following the situational framework for the description of conversational features elaborated in the author’s previous research, features distinctive of conversational grammar are grouped into five functional categories and their normed frequencies compared across the sexes. The functional categories distinguish features that can be seen as adaptations to constraints set by the situational factors of (1) Shared Context, (2) Co-Construction, (3) Real-Time Processing, (4) Discourse Management, and (5) Relation Management. The study’s results, described in detail in relation to the biological category of speaker sex and cultural notions of gender, suggest that the feminine grammar hypothesis is valid

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

A SYSTEMIC FUNCTIONAL ANALYSIS ON JAVANESE POLITENESS: TAKING SPEECH LEVEL INTO MOOD STRUCTURE

Speech level is an important aspect in Javanese grammar. It is just like, among others, tenses in English. Thus, the involvement of speech level in any study of Javanese grammar is highly necessary. On the other hand, speech level must also be studied the grammatical point of view. So far, however, there are very limited numbers—if any does really exist—of grammatical study on Javanese speech level. Most major studies on Javanese speech level are of sociolinguistics, lexical taxonomy or grouping, and prescriptive analysis. It is probably due to the idea of speech level as merely a social phenomenon has been taken for granted. Therefore, taking the speech level system into a grammatical analysis seems hardly possible. It is assumed that the seemingly impossible attempt comes only to the formal approach of the grammar study tradition for it has neglected the social aspect. Hence, it is necessary to look for an alternative grammatical approach which is able to cope with the speech level both grammatically and socially. A particular approach of grammar which involves social context is systemic functional grammar (SFG). SFG proposes that language has three kinds of functional component. One of them is the interpersonal function. This function sees language as an interaction between addresser and addressee—language is used for enacting participants‘ roles and relation among them. The interpersonal function is expressed through a particular grammatical structure, namely mood structure. This article is going present a demonstration of systemic functional analysis on Javanese speech level by taking it into the mood structure analysis. In addition, this paper aims for two kinds of potential significance. First, it could be an adequate description of Javanese speech level grammaticalization. Second, it can be a typological supplement for SFG in dealing with languages which apply a speech level system

Treebank-based multilingual unification-grammar development

Broad-coverage, deep unification grammar development is time-consuming and costly. This problem can be exacerbated in multilingual grammar development scenarios. Recently (Cahill et al., 2002) presented a treebank-based methodology to semi-automatically create broadcoverage, deep, unification grammar resources for English. In this paper we present a project which adapts this model to a multilingual grammar development scenario to obtain robust, wide-coverage, probabilistic Lexical-Functional Grammars (LFGs) for English and German via automatic f-structure annotation algorithms based on the Penn-II and TIGER treebanks. We outline our method used to extract a probabilistic LFG from the TIGER treebank and report on the quality of the f-structures produced. We achieve an f-score of 66.23 on the evaluation of 100 random sentences against a manually constructed gold standard

Dependency parsing resources for French: Converting acquired lexical functional grammar F-Structure annotations and parsing F-Structures directly

Recent years have seen considerable success in the generation of automatically obtained wide-coverage deep grammars for natural language processing, given reliable and large CFG-like treebanks. For research within Lexical Functional Grammar framework, these deep grammars are typically based on an extended PCFG parsing scheme from which dependencies are extracted. However, increasing success in statistical dependency parsing suggests that such deep grammar approaches to statistical parsing could be streamlined. We explore this novel approach to deep grammar parsing within the framework of LFG in this paper, for French, showing that best results (an f-score of 69.46) for the established integrated architecture may be obtained for French