  We propose an ensemble approach to predict the labels in linear programming
word problems. The entity identification and the meaning representation are two
types of tasks to be solved in the NL4Opt competition. We propose the
ensembleCRF method to identify the named entities for the first task. We found
that single models didn't improve for the given task in our analysis. A set of
prediction models predict the entities. The generated results are combined to
form a consensus result in the ensembleCRF method. We present an ensemble text
generator to produce the representation sentences for the second task. We
thought of dividing the problem into multiple small tasks due to the overflow
in the output. A single model generates different representations based on the
prompt. All the generated text is combined to form an ensemble and produce a
mathematical meaning of a linear programming problem.
