A Pattern Matching method for finding Noun and Proper Noun Translations from Noisy Parallel Corpora

TL;DR

This paper introduces a pattern matching approach leveraging tagging and frequency data to extract bilingual noun and proper noun translations from noisy parallel corpora, achieving 73.1% precision.

Contribution

It presents novel anchor point finding and noise elimination techniques for improved translation extraction from unaligned noisy texts.

Findings

Achieved 73.1% precision in translation extraction

Developed new anchor point and noise elimination methods

Demonstrated application in domain-specific noun phrase compilation

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.