Automatic lexicon acquisition from encyclopedia.

Lo, Ka Kan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 97-104).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.3Chapter 1.2 --- New paradigm in language learning --- p.5Chapter 1.3 --- Semantic Relations --- p.7Chapter 1.4 --- Contribution of this thesis --- p.9Chapter 2 --- Related Work --- p.13Chapter 2.1 --- Theoretical Linguistics --- p.13Chapter 2.1.1 --- Overview --- p.13Chapter 2.1.2 --- Analysis --- p.15Chapter 2.2 --- Computational Linguistics - General Learning --- p.17Chapter 2.3 --- Computational Linguistics - HPSG Lexical Acquisition --- p.20Chapter 2.4 --- Learning approach --- p.22Chapter 3 --- Background --- p.25Chapter 3.1 --- Modeling primitives --- p.26Chapter 3.1.1 --- Feature Structure --- p.26Chapter 3.1.2 --- Word --- p.28Chapter 3.1.3 --- Phrase --- p.35Chapter 3.1.4 --- Clause --- p.36Chapter 3.2 --- Wikipedia Resource --- p.38Chapter 3.2.1 --- Encyclopedia Text --- p.40Chapter 3.3 --- Semantic Relations --- p.40Chapter 4 --- Learning Framework - Syntactic and Semantic --- p.46Chapter 4.1 --- Type feature scoring function --- p.48Chapter 4.2 --- Confidence score of lexical entry --- p.50Chapter 4.3 --- Specialization and Generalization --- p.52Chapter 4.3.1 --- Further Processing --- p.54Chapter 4.3.2 --- Algorithm Outline --- p.54Chapter 4.3.3 --- Algorithm Analysis --- p.55Chapter 4.4 --- Semantic Information --- p.57Chapter 4.4.1 --- Extraction --- p.58Chapter 4.4.2 --- Induction --- p.60Chapter 4.4.3 --- Generalization --- p.63Chapter 4.5 --- Extension with new text documents --- p.65Chapter 4.6 --- Integrating the syntactic and semantic acquisition framework --- p.65Chapter 5 --- Evaluation --- p.68Chapter 5.1 --- Evaluation Metric - English Resource Grammar --- p.68Chapter 5.1.1 --- English Resource Grammar --- p.69Chapter 5.2 --- Experiments --- p.71Chapter 5.2.1 --- Tasks --- p.71Chapter 5.2.2 --- Evaluation Measures --- p.77Chapter 5.2.3 --- Methodologies --- p.78Chapter 5.2.4 --- Corpus Preparation --- p.79Chapter 5.2.5 --- Results --- p.81Chapter 5.3 --- Result Analysis --- p.85Chapter 6 --- Conclusions --- p.95Bibliography --- p.9

Efficacy of Beam Thresholding, Unification Filtering and Hybrid Parsing in Probabilistic HPSG Parsing

We investigated the performance e#cacy of beam search parsing and deep parsing techniques in probabilistic HPSG parsing using the Penn treebank. We first tested the beam thresholding and iterative parsing developed for PCFG parsing with an HPSG. Next, we tested three techniques originally developed for deep parsing: quick check, large constituent inhibition, and hybrid parsing with a CFG chunk parser. The contributions of the large constituent inhibition and global thresholding were not significant, while the quick check and chunk parser greatly contributed to total parsing performance. The precision, recall and average parsing time for the Penn treebank (Section 23) were 87.85%, 86.85%, and 360 ms, respectively