Name
Abstract | ||
|---|---|---|
In annual securities report, various information such as results of diverse business performances, point of view about causation of these outcomes, and issues and challenges to be addressed in the near future are included. Most of previous researches proposed the extraction methods of important sentences containing causal information of past companyu0027s performances but not effort to address future companyu0027s issues from text materials. In this paper, we propose our original method to extract future-oriented sentences by the combination of two SVM identification models, one of which captures features of future and the other aims for purposes and means in sentences of Japanese annual reports. All mean evaluations of our models, which were precision, recall and F-score, showed more than almost 0.9 and indicated that by using our model, we can effectively collect future information about business activities from annual reports as well as other relevant sources, which would allow us to make unique investment decisions and to develop unprecedented investment methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IIAI-AAI.2019.00140 | IIAI-AAI |
Field | DocType | Citations |
Data science,Computer science,Business activities,Support vector machine,Causation,Annual report,Investment decisions,Recall | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshinori Tanaka | 1 | 0 | 0.34 |
| Syunya Kodera | 2 | 0 | 0.34 |
| Fumihito Sato | 3 | 0 | 0.34 |
| Hiroki Sakaji | 4 | 30 | 17.97 |
| Kiyoshi Izumi | 5 | 127 | 37.12 |