Please use this identifier to cite or link to this item:
https://doi.org/10.1145/2019618.2019624
DC Field | Value | |
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dc.title | A multilabel text classification algorithm for labeling risk factors in sec form 10-K | |
dc.contributor.author | Huang, K.-W. | |
dc.contributor.author | Li, Z. | |
dc.date.accessioned | 2013-07-11T10:09:59Z | |
dc.date.available | 2013-07-11T10:09:59Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Huang, K.-W.,Li, Z. (2011). A multilabel text classification algorithm for labeling risk factors in sec form 10-K. ACM Transactions on Management Information Systems 2 (3). ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/2019618.2019624" target="_blank">https://doi.org/10.1145/2019618.2019624</a> | |
dc.identifier.issn | 2158656X | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/42467 | |
dc.description.abstract | This study develops, implements, and evaluates a multilabel text classification algorithm called the multilabel categorical K-nearest neighbor (ML-CKNN). The proposed algorithm is designed to automatically identify 25 types of risk factors with specific meanings reported in Section 1A of SEC form 10-K. The idea of ML-CKNN is to compute a categorical similarity score for each label by the K-nearest neighbors in that category. ML-CKNN is tailored to achieve the goal of extracting risk factors from 10Ks. The proposed algorithm can perfectly classify 74.94% of risk factors and 98.75% of labels. Moreover, ML-CKNN is empirically shown to outperform ML-KNN and other multilabel algorithms. The extracted risk factors could be valuable to empirical studies in accounting or finance. © 2011 ACM. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/2019618.2019624 | |
dc.source | Scopus | |
dc.subject | Annual reports | |
dc.subject | Multilabel classification | |
dc.subject | Risk factors | |
dc.subject | Text classification | |
dc.subject | Text mining | |
dc.type | Article | |
dc.contributor.department | INFORMATION SYSTEMS | |
dc.description.doi | 10.1145/2019618.2019624 | |
dc.description.sourcetitle | ACM Transactions on Management Information Systems | |
dc.description.volume | 2 | |
dc.description.issue | 3 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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