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- Publisher Website: 10.18653/v1/N18-1191
- Scopus: eid_2-s2.0-85083504286
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Conference Paper: Mining evidences for concept stock recommendation
Title | Mining evidences for concept stock recommendation |
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Authors | |
Issue Date | 2018 |
Publisher | Association for Computational Linguistics |
Citation | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2018), New Orleans, 1-6 June 2018. In NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2018, v. 1, p. 2103-2112 How to Cite? |
Abstract | We investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to mine evidences from large scale textual data, which contain valuable market information. In particular, distributed word similarities trained over large scale raw texts are taken as a basis of relevance measuring, and deep reinforcement learning is leveraged to learn a strategy of topic expansion, given a small amount of manually labeled data from financial analysts. Results on two Chinese stock market datasets show that our method outperforms a strong baseline using information retrieval techniques. |
Persistent Identifier | http://hdl.handle.net/10722/321882 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Liu, Qi | - |
dc.contributor.author | Zhang, Yue | - |
dc.date.accessioned | 2022-11-03T02:22:06Z | - |
dc.date.available | 2022-11-03T02:22:06Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2018), New Orleans, 1-6 June 2018. In NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2018, v. 1, p. 2103-2112 | - |
dc.identifier.isbn | 9781948087278 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321882 | - |
dc.description.abstract | We investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to mine evidences from large scale textual data, which contain valuable market information. In particular, distributed word similarities trained over large scale raw texts are taken as a basis of relevance measuring, and deep reinforcement learning is leveraged to learn a strategy of topic expansion, given a small amount of manually labeled data from financial analysts. Results on two Chinese stock market datasets show that our method outperforms a strong baseline using information retrieval techniques. | - |
dc.language | eng | - |
dc.publisher | Association for Computational Linguistics | - |
dc.relation.ispartof | NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference | - |
dc.title | Mining evidences for concept stock recommendation | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.18653/v1/N18-1191 | - |
dc.identifier.scopus | eid_2-s2.0-85083504286 | - |
dc.identifier.volume | 1 | - |
dc.identifier.spage | 2103 | - |
dc.identifier.epage | 2112 | - |
dc.publisher.place | Stroudsburg, PA | - |