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Automatic Classification and Tagging of Blog Articles with the Aim of Supporting Caregivers of Depressed Family Members
| Content Provider | Semantic Scholar |
|---|---|
| Author | Saito, Toshihide Aramaki, Eiji Miyabe, Mai Hirata, Keiji |
| Copyright Year | 2016 |
| Abstract | People who are close to and help depressed patients, as well as peripheral nurses, could be supported by performing information retrieval to eliminate or mitigate stress. It is difficult to find the information using existing keyword searches because the information caregivers need is specifically that about others in a situation similar to caregivers. To solve this problem, we introduce the structure of viewpoint, situation, and intention and take into account the context in which the structure occurs. The structure is represented using the tags generated by Conditional Random Fields (CRF). The context is represented using the labels recognized by Support Vector Machine (SVM). By 10-fold cross-validation of learning data, we evaluated a prototype system and have obtained a precision rate of 54.2 % for the context classification by SVN and 45.3% for the tag assignment by CRF. In the future, it is necessary to improve the accuracy of the context classification and tag assignment to achieve a dedicated search. Keywords—depression, classification, family caregiver, tagging, SVM, CRF. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iaeng.org/publication/IMECS2016/IMECS2016_pp303-306.pdf |
| Alternate Webpage(s) | https://www.fun.ac.jp/~hirata/Papers/IMECS2016-saito.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
