| Popis: |
This paper describes our contribution to the closed track of the Shared Task Translation Inference across Dictionaries (TIAD2017), 1 held in conjunction with the first Conference on Language Data and Knowledge (LDK-2017). In our approach, we use supervised machine learning to predict high-quality candidate translation pairs. We train a Support Vector Machine using several features, mostly of the translation graph, but also taking into consideration string similarity (Levenshtein distance). As the closed track does not provide manual training data, we define positive training examples as translation candidate pairs which occur in a cycle in which there is a direct connection. |
