The Grail theorem prover: Type theory for syntax and semantics

As the name suggests, type-logical grammars are a grammar formalism based on logic and type theory. From the prespective of grammar design, type-logical grammars develop the syntactic and semantic aspects of linguistic phenomena hand-in-hand, letting the desired semantics of an expression inform the syntactic type and vice versa. Prototypical examples of the successful application of type-logical grammars to the syntax-semantics interface include coordination, quantifier scope and extraction.This chapter describes the Grail theorem prover, a series of tools for designing and testing grammars in various modern type-logical grammars which functions as a tool . All tools described in this chapter are freely available

UNIARAB: An Universal Machine Translator System For Arabic Based On Role And Reference Grammar

This paper presents a machine translation system (Hutchins 2003) called UniArab (Salem, Hensman and Nolan 2008). It is a proof-of-concept system supporting the fundamental aspects of Arabic, such as the parts of speech, agreement and tenses. UniArab is based on the linking algorithm of RRG (syntax to semantics and vice versa). UniArab takes MSA Arabic as input in the native orthography, parses the sentence(s) into a logical meta-representation based on the fully expanded RRG logical structures and, using this, generates perfectly grammatical English output with full agreement and morphological resolution. UniArab utilizes an XML-based implementation of elements of the Role and Reference Grammar theory in software. In order to analyse Arabic by computer we first extract the lexical properties of the Arabic words (Al-Sughaiyer and Al-Kharashi 2004). From the parse, it then creates a computer-based representation for the logical structure of the Arabic sentence(s). We use the RRG theory to motivate the computational implementation of the architecture of the lexicon in software. We also implement in software the RRG bidirectional linking system to build the parse and generate functions between the syntax-semantic interfaces. Through seven input phases, including the morphological and syntactic unpacking, UniArab extracts the logical structure of an Arabic sentence. Using the XML-based metadata representing the RRG logical structure, UniArab then accurately generates an equivalent grammatical sentence in the target language through four output phases. We discuss the technologies used to support its development and also the user interface that allows for the addition of lexical items directly to the lexicon in real time. The UniArab system has been tested and evaluated generating equivalent grammatical sentences, in English, via the logical structure of Arabic sentences, based on MSA Arabic input with very significant and accurate results (Izwaini 2006). At present we are working to greatly extend the coverage by the addition of more verbs to the lexicon. We have demonstrated in this research that RRG is a viable linguistic model for building accurate rulebased semantically oriented machine translation software. Role and Reference Grammar (RRG) is a functional theory of grammar that posits a direct mapping between the semantic representation of a sentence and its syntactic representation. The theory allows a sentence in a specific language to be described in terms of its logical structure and grammatical procedures. RRG creates a linking relationship between syntax and semantics, and can account for how semantic representations are mapped into syntactic representations. We claim that RRG is very suitable for machine translation of Arabic, notwithstanding well-documented difficulties found within Arabic MT (Izwaini, S. 2006), and that RRG can be implemented in software as the rule-based kernel of an Interlingua bridge MT engine

A Compositional Treatment of Polysemous Arguments in Categorial Grammar

We discuss an extension of the standard logical rules (functional application and abstraction) in Categorial Grammar (CG), in order to deal with some specific cases of polysemy. We borrow from Generative Lexicon theory which proposes the mechanism of {\em coercion}, next to a rich nominal lexical semantic structure called {\em qualia structure}. In a previous paper we introduced coercion into the framework of {\em sign-based} Categorial Grammar and investigated its impact on traditional Fregean compositionality. In this paper we will elaborate on this idea, mostly working towards the introduction of a new semantic dimension. Where in current versions of sign-based Categorial Grammar only two representations are derived: a prosodic one (form) and a logical one (modelling), here we introduce also a more detaled representation of the lexical semantics. This extra knowledge will serve to account for linguistic phenomena like {\em metonymy\/}.Comment: LaTeX file, 19 pages, uses pubsmacs, pubsbib, pubsarticle, leqn

Treebank-based acquisition of wide-coverage, probabilistic LFG resources: project overview, results and evaluation

This paper presents an overview of a project to acquire wide-coverage, probabilistic Lexical-Functional Grammar (LFG) resources from treebanks. Our approach is based on an automatic annotation algorithm that annotates “raw” treebank trees with LFG f-structure information approximating to basic predicate-argument/dependency structure. From the f-structure-annotated treebank we extract probabilistic unification grammar resources. We present the annotation algorithm, the extraction of lexical information and the acquisition of wide-coverage and robust PCFG-based LFG approximations including long-distance dependency resolution. We show how the methodology can be applied to multilingual, treebank-based unification grammar acquisition. Finally we show how simple (quasi-)logical forms can be derived automatically from the f-structures generated for the treebank trees

Information extraction

In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking, so we study the expansion of knowledge graphs to use context information for reasoning and merging of templates