DCU 250 Arabic dependency bank: an LFG gold standard resource for the Arabic Penn treebank

This paper describes the construction of a dependency bank gold standard for Arabic, DCU 250 Arabic Dependency Bank (DCU 250), based on the Arabic Penn Treebank Corpus (ATB) (Bies and Maamouri, 2003; Maamouri and Bies, 2004) within the theoretical framework of Lexical Functional Grammar (LFG). For parsing and automatically extracting grammatical and lexical resources from treebanks, it is necessary to evaluate against established gold standard resources. Gold standards for various languages have been developed, but to our knowledge, such a resource has not yet been constructed for Arabic. The construction of the DCU 250 marks the first step towards the creation of an automatic LFG f-structure annotation algorithm for the ATB, and for the extraction of Arabic grammatical and lexical resources

Cloud service localisation

The essence of cloud computing is the provision of software and hardware services to a range of users in dierent locations. The aim of cloud service localisation is to facilitate the internationalisation and localisation of cloud services by allowing their adaption to dierent locales. We address the lingual localisation by providing service-level language translation techniques to adopt services to dierent languages and regulatory localisation by providing standards-based mappings to achieve regulatory compliance with regionally varying laws, standards and regulations. The aim is to support and enforce the explicit modelling of aspects particularly relevant to localisation and runtime support consisting of tools and middleware services to automating the deployment based on models of locales, driven by the two localisation dimensions. We focus here on an ontology-based conceptual information model that integrates locale specication in a coherent way

From treebank resources to LFG F-structures

We present two methods for automatically annotating treebank resources with functional structures. Both methods define systematic patterns of correspondence between partial PS configurations and functional structures. These are applied to PS rules extracted from treebanks, or directly to constraint set encodings of treebank PS trees

Evaluating automatic LFG f-structure annotation for the Penn-II treebank

Lexical-Functional Grammar (LFG: Kaplan and Bresnan, 1982; Bresnan, 2001; Dalrymple, 2001) f-structures represent abstract syntactic information approximating to basic predicate-argument-modifier (dependency) structure or simple logical form (van Genabith and Crouch, 1996; Cahill et al., 2003a) . A number of methods have been developed (van Genabith et al., 1999a,b, 2001; Frank, 2000; Sadler et al., 2000; Frank et al., 2003) for automatically annotating treebank resources with LFG f-structure information. Until recently, however, most of this work on automatic f-structure annotation has been applied only to limited data sets, so while it may have shown lsquoproof of conceptrsquo, it has not yet demonstrated that the techniques developed scale up to much larger data sets. More recent work (Cahill et al., 2002a,b) has presented efforts in evolving and scaling techniques established in these previous papers to the full Penn-II Treebank (Marcus et al., 1994). In this paper, we present a number of quantitative and qualitative evaluation experiments which provide insights into the effectiveness of the techniques developed to automatically derive a set of f-structures for the more than 1,000,000 words and 49,000 sentences of Penn-II. Currently we obtain 94.85% Precision, 95.4% Recall and 95.09% F-Score for preds-only f-structures against a manually encoded gold standard

Dynamically structuring, updating and interrelating representations of visual and linguistic discourse context

AbstractThe fundamental claim of this paper is that salience—both visual and linguistic—is an important overarching semantic category structuring visually situated discourse. Based on this we argue that computer systems attempting to model the evolving context of a visually situated discourse should integrate models of visual and linguistic salience within their natural language processing (NLP) framework. The paper highlights the importance of dynamically updating and interrelating visual and linguistic discourse context representations. To support our approach, we have developed a real-time, natural language virtual reality (NLVR) system (called LIVE, for Linguistic Interaction with Virtual Environments) that implements an NLP framework based on both visual and linguistic salience. Within this framework saliency information underpins two of the core subtasks of NLP: reference resolution and the generation of referring expressions. We describe the theoretical basis and architecture of the LIVE NLP framework and present extensive evaluation results comparing the system's performance with that of human participants in a number of experiments