Meaning representation for natural language understanding
The history of natural language processing has seen multiple attempts to represent the meaning of the text. A good solution could spur the research of question answering systems, text summarization solutions, text simplification systems, etc. One of the recent promising solutions is called abstract meaning representation, or AMR. In this talk, we will discuss the peculiarities of building AMR graphs and NLP applications that can benefit from these graphs.