Exemplar-based models of language propose that human language production and understanding operate with a store of concrete linguistic experiences rather than with abstract linguistic rules. While exemplarbased models are well acknowledged in areas like phonology and morphology, common wisdom has it that they are intrinsically flawed for syntax where infinite generative capacity is needed. This paper shows that this common wisdom is wrong. It starts out by reviewing an exemplar-based syntactic model, known as Data-Oriented Parsing, or DOP, which operates on a corpus of phrase-structure trees. While this model is productive, it is inadequate from the point of grammatical productivity. We therefore extend it to the more sophisticated linguistic representations proposed by Lexical-Functional Grammar theory, resulting in the model known as LFG-DOP, which does allow for meta-linguistic judgments of acceptability. We show how DOP deals with first language acquisition, suggesting a unified model for language learning and language use, and go into a number of syntactic phenomena that can be explained by DOP but that challenge rulebased models. We argue that if there is anything innate in language cognition it is not Universal Grammar but “Universal Representation”. 1.

A dissertation submitted in fulfilment of the requirements for the award of

Is there a single mechanism that underlies all perceptual and cognitive processing? This paper aims to solve a small part of Newell's challenge (A. Newell 1990, Unified Theories of Cognition, Harvard University Press) and proposes a model that unifies three different modalities: language, music and problem-solving. In doing so, we will focus on tree structures. Trees are ubiquitous in modeling high-level perception and cognition and have been used to represent grouping structures in linguistic, musical and visual perception and deductive structures in reasoning, learning and problem solving. We will show that an instantiation of the Data-Oriented Parsing (DOP) framework can accurately predict the correct tree structure for linguistic utterances, musical pieces and physics problems. The key idea of the DOP framework is that new input is analyzed by combining subtrees from a representative corpus of previous trees. While the labeling of the trees and the details of the combination operation may differ across the modalities, we argue that there is one model for predicting the tree that humans come up with. We report on experiments with manually annotated corpora for the three modalities, showing that the best performing model is the one which takes into account subtrees of arbitrary size and which selects the most probable tree from among the shortest derivations of an input.

Parsing models have many applications in AI, ranging from natural language processing (NLP) and computational music analysis to logic programming and computational learning. Broadly conceived, a parsing model seeks to uncover the underlying structure of an input, that is, the various ways in which

This thesis explores certain linguistic and statistical extensions of Data-Oriented Parsing (DOP). The central idea in DOP is to analyse new input on the basis of a collection of fragment-probability pairs. In its simplest version, Tree-DOP, the fragments used are subparts of simple phrase structure trees. Resolving ambiguity (i.e. selecting the optimal analysis) involves identifying the Most Probable Parse (MPP). Though empirical evaluation has shown state-of-the-art results, the linguistic expressive mechanism of this model is very limited. In addition, the algorithm used to compute the MPP has been shown to suffer from several disadvantages. The aim of the thesis is two-fold. In the first part, we seek to explore how the linguis-tic dimension of DOP can be enhanced. To this end, we investigate how the framework can be applied to representations based on a richer annotation scheme, specifically that of Head-driven Phrase Structure Grammar (HPSG). This investigation culminates in the development of an HPSG-DOP model, which takes maximal advantage of the un-derlying formalism. The proposed model embodies a number of positive characteristics

We present an investigation into parsing the Penn Chinese Treebank using a DataOriented Parsing (DOP) approach. DOP comprises an experience-based approach to natural language parsing. Most published research in the DOP framework uses PStrees as its representation schema. Drawbacks of the DOP approach centre around issues of efficiency. We incorporate recent advances in DOP parsing techniques into a novel DOP parser which generates a compact representation of all subtrees which can be derived from any full parse tree.

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