Treebank-based automatic acquisition of wide coverage, deep linguistic resources for Japanese
Masanori Oya · Dublin City University Open Access Institutional Repository (Dublin City University) · 2010
The objective f this thesis is to design, implement and evaluate a methodology for the automatic acquisition of wide-coverage treebank-based deep linguistic resources fr Japanese, as part of the GramLab project which focuses on the automatic treebank-based induction of multilingual resources in the framework of Lexical-Functional Grammar (LFG). After introducing the basic framework of LFG in Chapter 2, I describe the core syntactic and morphological aspects of Japanese in Chapter 3: non-configurationality; the concept of "bunsetsu" r syntactic units and their dependency relationship represented in Directed Acyclic Graphs (DAGs); topicalisation by a particular particle; and frequent use of zero pronouns with or without over antecedents. Inflecting parts-of-speech and non-inflecting parts-of-speech of Japanese are also described with examples. In Chapter 4, I provide the linguistic representation of core grammatical features and functions of Japanese in the framework of LFG.I use Directed Acyclic Graphs (DAG) as a framework for the unified representation f surface syntactic, morphological and lexical information in an LFG f-structure. In Chapters 5 and 6, I describe the automatic annotation algorithm of LFG f-structure functional equations (i.e. labelled dependencies) to the Kyoto Text Corpus version 4.0 (KTC4) and the output of Kurohashi-Nagao Parser (KNP provide unlabelled dependencies only. The method presented in this dissertation also includes zero pronoun identification. Finally in Chapter 7 I evaluate the performance of the f-structure annotation algorithm with zero-pronoun identification for KTC4 against a manually-corrected Gold Standard of 500 sentences randomly chosen from KTC4. Using KTC4 treebank trees, currently my method achieves a pred-only dependency f-score of 94.72%. The parsing experiments using KNP output yield a pred-only dependency f-score of 82.38%.