Enhancing practical TAG parsing efficiency by capturing redundancy

International audienceParsing efficiency within the context of tree adjoining grammars (TAGs) depends not only on the size of the input sentence but also, linearly, on the size of the input TAG, which can attain several thousands of elementary trees. We propose a factorized, finite-state TAG representation which copes with this combinatorial explosion. The associated parsing algorithm substantially increases the parsing performance on a real-size French TAG grammar

Using rewriting techniques to produce code generators and proving them correct

AbstractA major problem in deriving a compiler from a formal definition is the production of correct and efficient object code. In this context, we propose a solution to the problem of code-generator generation.Our approach is based on a target machine description where the basic concepts used (storage classes, access modes, access classes and instructions) are hierarchically described by tree patterns. These tree patterns are terms of an abstract data type. The program intermediate representation (input to the code generator) is a term of the same abstract data type.The code generation process is based on access modes and instruction template-driven rewritings. The result is that each program instruction is reduced to a sequence of elementary machine instructions, each of them representing an instance of an instruction template.The axioms of the abstract data type are used to prove that the rewritings preserve the semantics of the intermediate representation

Factoring Predicate Argument and Scope Semantics : underspecified Semantics with LTAG

In this paper we propose a compositional semantics for lexicalized tree-adjoining grammar (LTAG). Tree-local multicomponent derivations allow separation of the semantic contribution of a lexical item into one component contributing to the predicate argument structure and a second component contributing to scope semantics. Based on this idea a syntax-semantics interface is presented where the compositional semantics depends only on the derivation structure. It is shown that the derivation structure (and indirectly the locality of derivations) allows an appropriate amount of underspecification. This is illustrated by investigating underspecified representations for quantifier scope ambiguities and related phenomena such as adjunct scope and island constraints

Some Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars

In this paper we present some novel applications of Explanation-Based Learning (EBL) technique to parsing Lexicalized Tree-Adjoining grammars. The novel aspects are (a) immediate generalization of parses in the training set, (b) generalization over recursive structures and (c) representation of generalized parses as Finite State Transducers. A highly impoverished parser called a ``stapler'' has also been introduced. We present experimental results using EBL for different corpora and architectures to show the effectiveness of our approach.Comment: uuencoded postscript fil

Realization of tree adjoining grammars with unification

The syntactic generator of the WIP system is based on the representation formalism "Tree Adjoining Grammars" (TAGs). We have extended the formalism by associating elementary rules of the grammar (trees) with feature structures, leading to "Tree Adjoining Grammars with Unification" (UTAGs). The extended formalism facilitates a compact and adequate representation of complex syntactic features. The contradiction between the monotonic operation of unification and the combination operation for trees - adjunction - that is nonmonotonic in a way can be solved by several approaches to realization. Two of them are presented in this work and compared with respect to the restrictions that are given by the system, i.e., the adequacy of the realization for incremental and parallel generation. It can be shown that UTAGs are subsumed by FTAGs (Feature Structure based TAGs) that have been defined by Vijay-Shanker and Joshi. That is why the results for realization can be applied to both UTAGs and a restricted version of FTAGs

Representation of Knowledge for Chess Endgames Towards a Self-Improving System

This thesis describes an investigation of the problems involved in representing knowledge within the task area of elementary Chess endgames. Two major criteria are taken for the choice of a model of & the chessplayer's knowledge : firstly, that algorithms constructed using the model should be natural from the viewpoint of a chessplayer and commensurate with his, view of the complexity of the task, and secondly that the algorithms should be capable of refinement in the light of experience in a manner which preserves the previous property. Elementary chess endgames are studied as a field in which programs based on tree-searching and traditional evaluation functions have achieved poor results and where tree-searching seems to play little or no part for people. It is therefore possible to examine problems of knowledge representation and program refinement largely independently of the tree-searching paradigm. A long term aim of the research is to develop a representation suitable as the basis for a fully automatic system of algorithm refinement, whilst maintaining the criteria given above. A model is proposed and algorithms are given for two endgames, King and Rook against King (KRK) and King and Pawn against King (KPK) using this model. It is argued that both algorithms are reasonably natural and compact representations and experiments in refining these algorithms are described in detail. In both cases, the process of refinement is shown to be a reasonably straightforward one (for people) and one which maintains the properties of naturalness and compactness. The possibility of automating this process is considered

Multilayer wave functions: A recursive coupling of local excitations

Finding a succinct representation to describe the ground state of a disordered interacting system could be very helpful in understanding the interplay between the interactions that is manifested in a quantum phase transition. In this work we use some elementary states to construct recursively an ansatz of multilayer wave functions, where in each step the higher-level wave function is represented by a superposition of the locally "excited states" obtained from the lower-level wave function. This allows us to write the Hamiltonian expectation in terms of some local functions of the variational parameters, and employ an efficient message-passing algorithm to find the optimal parameters. We obtain good estimations of the ground-state energy and the phase transition point for the transverse Ising model with a few layers of mean-field and symmetric tree states. The work is the first step towards the application of local and distributed message-passing algorithms in the study of structured variational problems in finite dimensions.Comment: 23 pages, including 3 appendices and 6 figures. A shortened version published in EP