In this study, we construct a system to predict argument labels for statements in meeting minutes by using sequence labeling methodology and validate the effectiveness of prediction performance for various input methods of utterance data to predict argument labels for money expressions effectively. To evaluate the validation of the system, we will use the Budget Argument Mining task data in NTCIR 16 QA Lab Poliinfo 3. We train an argument label prediction model on the training data that exists in the data, dividing it into two types: data for model training and data for model validation. As the prediction model, we use the Bidirectional LSTM CNNs CRF model to predict argument labels for each word in the input data and output a series of argument labels. In the experiment, we compare the prediction accuracy of models obtained by changing the data input method, such as the range of sentences containing money expressions. As a result of the experiment, we found that the prediction accuracy of argument labels was higher when each sentence was entered into the model rather than when all the statements of the assembly member were entered into the model. Furthermore, we found that the prediction accuracy of the argument labels can be improved by replacing the numbers in the money expression with special tokens.conference pape
