This paper presents a tag-based statistical math word problem solver with understanding, reasoning, and explanation. It analyzes the text and transforms both body and question parts into their tag-based logic forms, and then performs inference on them. The proposed tag-based approach provides the flexibility for annotating an extracted math quantity with its associated syntactic and semantic information, which can be used to identify the desired operand and filter out irrelevant quantities. The proposed approach is thus less sensitive to the irrelevant information and could provide the answer more precisely. Also, it can handle much more problem types other than addition and subtraction.

No abstract

No abstract