A Pattern Matching method for finding Noun and Proper Noun Translations from Noisy Parallel Corpora
Pascale Fung
Abstract
We present a pattern matching method for compiling a bilingual lexicon of nouns and proper nouns from unaligned, noisy parallel texts of Asian/Indo-European language pairs. Tagging information of one language is used. Word frequency and position information for high and low frequency words are represented in two different vector forms for pattern matching. New anchor point finding and noise elimination techniques are introduced. We obtained a 73.1\% precision. We also show how the results can be used in the compilation of domain-specific noun phrases.
Create a lesson
Related papers
A Memory-Based Approach to Learning Shallow Natural Language Patterns
Shlomo Argamon, Ido Dagan, Yuval Krymolowski
A Comparison of WordNet and Roget's Taxonomy for Measuring Semantic Similarity
Michael Mc Hale
Some Ontological Principles for Designing Upper Level Lexical Resources
Nicola Guarino
Towards an implementable dependency grammar
Timo Jarvinen, Pasi Tapanainen
A Variant of Earley Parsing
Mark-Jan Nederhof, Giorgio Satta
Segregatory Coordination and Ellipsis in Text Generation
James Shaw