Towards a machine-learning architecture for lexical functional grammar parsing

Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages. The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing. In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages

Treebank-based acquisition of Chinese LFG resources for parsing and generation

This thesis describes a treebank-based approach to automatically acquire robust,wide-coverage Lexical-Functional Grammar (LFG) resources for Chinese parsing and generation, which is part of a larger project on the rapid construction of deep, large-scale, constraint-based, multilingual grammatical resources. I present an application-oriented LFG analysis for Chinese core linguistic phenomena and (in cooperation with PARC) develop a gold-standard dependency-bank of Chinese f-structures for evaluation. Based on the Penn Chinese Treebank, I design and implement two architectures for inducing Chinese LFG resources, one annotation-based and the other dependency conversion-based. I then apply the f-structure acquisition algorithm together with external, state-of-the-art parsers to parsing new text into "proto" f-structures. In order to convert "proto" f-structures into "proper" f-structures or deep dependencies, I present a novel Non-Local Dependency (NLD) recovery algorithm using subcategorisation frames and f-structure paths linking antecedents and traces in NLDs extracted from the automatically-built LFG f-structure treebank. Based on the grammars extracted from the f-structure annotated treebank, I develop a PCFG-based chart generator and a new n-gram based pure dependency generator to realise Chinese sentences from LFG f-structures. The work reported in this thesis is the first effort to scale treebank-based, probabilistic Chinese LFG resources from proof-of-concept research to unrestricted, real text. Although this thesis concentrates on Chinese and LFG, many of the methodologies, e.g. the acquisition of predicate-argument structures, NLD resolution and the PCFG- and dependency n-gram-based generation models, are largely language and formalism independent and should generalise to diverse languages as well as to labelled bilexical dependency representations other than LFG

Adapting and developing linguistic resources for question answering

As information retrieval becomes more focussed, so too must the techniques involved in the retrieval process. More precise responses to queries require more precise linguistic analysis of both the queries and the factual documents from which the information is being retrieved. In this thesis, I present research into using existing linguistic tools to analyse questions. These tools, as supplied, often underperform on question analysis. I present my work on adapting these tools, and creating new resources for use in developing new tools tailored to question analysis. My work has shown that in order to adapt the treebank- and f-structure annotation algorithmbased wide coverage LFG parsing resources of Cahill et al. (2004) to analyse questions from the ATIS corpus, only the c-structure parser needs to be retrained, the annotation algorithm remains unchanged. The retrained c-structure parser needs only a small amount of appropriate training data added to its training corpus to gain a significant improvement in both c-structure parsing and f-structure annotation. Given the improvements made with a relatively small amount of question data, I developed QuestionBank, a question treebank, to determine what further gains can be made using a larger amount of question data. My question treebank is a corpus of 4000 parse annotated questions. The questions were taken from a number of sources and the question treebank was “bootstrapped” in an incremental parsing, hand correction and retraining approach from raw data using existing probabilistic parsing resources. Experiments with QuestionBank show that it is an effective resource for training parsers to analyse questions with an improvement of over 10% on the baseline parsing results. In further experiments I show that a parser retrained with QuestionBank can also parse newspaper text (Penn-II Treebank Section 23) with state-of-the-art accuracy. Long distance dependencies (LDDs) are a vital part of question analysis in determining semantic roles and question focus. I have designed and implemented a novel method to recover WH-traces and coindexed antecedents in c-structure trees from parser output which uses the f-structure LDD resolution method of Cahill et al (2004) to resolve the dependencies and then “reverse engineers” the corresponding syntactic components in the c-structure tree