The First Workshop on Financial Technology and Natural Language Processing, pp. 102-107, Aug. 12, 2019
The First Workshop on Financial Technology and Natural Language Processing (FinNLP2019) in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI) 2019
This paper proposes a pointwise prediction for a sentence boundary detection task. The proposed pointwise prediction is combined with our original word embedding method and three-layered perceptron. It predicts whether the targeted words have the role of the beginning/end of a sentence or not by using word features around the targeted words. Wetested our model by changing some parameters in our model andthenensembled these models with various parameters. Consequently, the ensembled model achieved 0.88 and 0.84 averaged f1-score by testing the data both in English and French, and it also obtained 0.84 in English and 0.86 in French as the final results of this shared task. In addition, wedeveloped a baseline model, that is, a rule-based prediction model, for comparison. The result shows that the proposed pointwise prediction model outperformed the rule-based prediction model in any index.
@inproceedings{Hirano2019-finnlp,
title={{mhirano at the FinSBD Task: Pointwise Prediction Based on Multi-layer Perceptron for Sentence Boundary Detection}},
author={Masanori HIRANO and Hiroki SAKAJI and Kiyoshi IZUMI and Hiroyasu MATSUSHIMA},
booktitle={The First Workshop on Financial Technology and Natural Language Processing},
pages={102-107},
url={https://aclanthology.org/W19-5518/},
year={2019}
}