Meaning Representation Parsing
This project is aimed at solving the task 8 of SemEval 2016, in which, any English sentence had to be converted into its Abstract Meaning Representation (AMR) [ISI, 2015] All the data and the code for the baseline algorithm was provided by the organizer of the task. In the baseline algorithm provided for the same [Flanigan et al. 2014] we noticed that the AMR graph generation hugely depended on the focus of the sentence. And the baseline failed to recognize the focus quite often. So we used an LSTM to recognize the focus separately and use this new focus for generating the rest of the graph. 1 Abstract Meaning Representation "Abstract Meaning Representation (AMR) is a compact, readable, whole sentence semantic annotation. Annotation components include entity identification and typing, PropBank semantic roles, individual entities playing multiple roles, entity grounding via wikification, as well as treatments of modality, negation, etc." [May, 2015] 1. Who is doing what to whom in a sentence 2. Different from a parse tree, it is abstract 3. AMR does not say anything about how it wants to be processed. 4. It is not an interlingua. Figure 1: An ideal AMR graph for the sentence "The boy wants the girl to believe him."