Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming

Abstract

In this thesis, I propose a new technique of Aligning English sentence words with its Semantic Representation using Inductive Logic Programming(ILP). My work focusses on Abstract Meaning Representation(AMR). AMR is a semantic formalism to English natural language. It encodes meaning of a sentence in a rooted graph. This representation has gained attention for its simplicity and expressive power. An AMR Aligner aligns words in a sentence to nodes(concepts) in its AMR graph. As AMR annotation has no explicit alignment with words in English sentence, automatic alignment becomes a requirement for training AMR parsers. The aligner in this work comprises of two components. First, rules are learnt using ILP that invoke AMR concepts from sentence-AMR graph pairs in the training data. Second, the learnt rules are then used to align English sentences with AMR graphs. The technique is evaluated on publicly available test dataset and the results are comparable with state-of-the-art aligner.