Collective Web-Based Parenthetical Translation Extraction Using Markov Logic Networks

Abstract

Parenthetical translations are translations of terms in otherwise monolingual text that appear inside parentheses. Parenthetical translations extraction (PTE) is the task of extracting parenthetical translations from natural language documents. One of the main difficulties in PTE is to detect the left boundary of the translated term in preparenthetical text. In this article, we propose a collective approach that employs Markov logic to model multiple constraints used in the PTE task. We show how various constraints can be formulated and combined in a Markov logic network (MLN). Our experimental results show that the proposed collective PTE approach significantly outperforms a current state-of-the-art method, improving the average F-measure up to 27.11% compared to the previous word alignment approach. It also outperforms an individual MLN-based system by 8.2% and a system based on conditional random fields by 5.9%.