Multilingual Lexicon Bootstrapping - Improving a Lexicon Induction System Using a Parallel Corpus

We address the task of improving the quality of lexicon bootstrapping, i.e., of expanding a semantic lexicon on a given corpus. A main problem of iterative bootstrapping techniques is the fact that lexicon quality degrades gradually as more and more false terms are added. We propose to exploit linguistic variation between languages to reduce this problem of semantic drift with a knowledge-lean and language-independent ensemble method. Our results on English and German show that lexicon bootstrapping benefits significantly from the multilingual symbiosis.