A Metric for Example Matching in Example-Based Machine Translation

Abstract

This paper proposes a metric for example matching under the example-based machine translation. Our metric served as similarity measure is employed to retrieve the most similar examples to a given query. Basically it makes use of simple information such as lemma and part-of-speech information of typographically mismatched words. In addition, it uses the contiguity information of matched word units to catch the full context. Finally we show the results for the correctness of the proposed metric.