Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments
This addresses a specific challenge in NLP applications like Machine Translation, though it appears incremental in scope.
The paper tackled the problem of Prepositional Phrase attachment disambiguation in English by using bilingual parsing and alignments with Hindi, improving performance by 10% over a baseline MSTParser model.
In this paper, we attempt to solve the problem of Prepositional Phrase (PP) attachments in English. The motivation for the work comes from NLP applications like Machine Translation, for which, getting the correct attachment of prepositions is very crucial. The idea is to correct the PP-attachments for a sentence with the help of alignments from parallel data in another language. The novelty of our work lies in the formulation of the problem into a dual decomposition based algorithm that enforces agreement between the parse trees from two languages as a constraint. Experiments were performed on the English-Hindi language pair and the performance improved by 10% over the baseline, where the baseline is the attachment predicted by the MSTParser model trained for English.