  Automatic Word problem solving has always posed a great challenge for the NLP
community. Usually a word problem is a narrative comprising of a few sentences
and a question is asked about a quantity referred in the sentences. Solving
word problem involves reasoning across sentences, identification of operations,
their order, relevant quantities and discarding irrelevant quantities. In this
paper, we present a novel approach for automatic arithmetic word problem
solving. Our approach starts with frame identification. Each frame can either
be classified as a state or an action frame. The frame identification is
dependent on the verb in a sentence. Every frame is unique and is identified by
its slots. The slots are filled using dependency parsed output of a sentence.
The slots are entity holder, entity, quantity of the entity, recipient,
additional information like place, time. The slots and frames helps to identify
the type of question asked and the entity referred. Action frames act on state
frame(s) which causes a change in quantities of the state frames. The frames
are then used to build a graph where any change in quantities can be propagated
to the neighboring nodes. Most of the current solvers can only answer questions
related to the quantity, while our system can answer different kinds of
questions like `who', `what' other than the quantity related questions `how
many'.
  There are three major contributions of this paper. 1. Frame Annotated Corpus
(with a frame annotation tool) 2. Frame Identification Module 3. A new easily
understandable Framework for word problem solving
