  Generation of scientific visualization from analytical natural language text
is a challenging task. In this paper, we propose Text2Chart, a multi-staged
chart generator method. Text2Chart takes natural language text as input and
produce visualization as two-dimensional charts. Text2Chart approaches the
problem in three stages. Firstly, it identifies the axis elements of a chart
from the given text known as x and y entities. Then it finds a mapping of
x-entities with its corresponding y-entities. Next, it generates a chart type
suitable for the given text: bar, line or pie. Combination of these three
stages is capable of generating visualization from the given analytical text.
We have also constructed a dataset for this problem. Experiments show that
Text2Chart achieves best performances with BERT based encodings with LSTM
models in the first stage to label x and y entities, Random Forest classifier
for the mapping stage and fastText embedding with LSTM for the chart type
prediction. In our experiments, all the stages show satisfactory results and
effectiveness considering formation of charts from analytical text, achieving a
commendable overall performance.
