Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Conference on Neural Information Processing Systems 2013.

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[4]

Louiqa Raschid, Doug Burdick, Mark Flood, John Grant, Joe Langsam, Ian Soboroff, and Elena Zotkina. 2017. Financial Entity Identification and Information Integration (FEIII) Challenge 2017: The Report of the Organizing Committee. In Proceedings of the Workshop on Data Science for Macro-Modeling (DSMM@SIGMOD).

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Repke TKrestel R(2021)Extraction and Representation of Financial Entities from TextData Science for Economics and Finance10.1007/978-3-030-66891-4_11(241-263)Online publication date: 9-Jan-2021
https://doi.org/10.1007/978-3-030-66891-4_11
Raschid LBurdick DFlood MGrant JLangsam JSoboroff I(2017)Financial Entity Identification and Information Integration (FEIII) 2017 ChallengeProceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets10.1145/3077240.3077248(1-4)Online publication date: 14-May-2017
https://dl.acm.org/doi/10.1145/3077240.3077248

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DSMM'17: Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets

May 2017

58 pages

ISBN:9781450350310

DOI:10.1145/3077240

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 May 2017

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SIGMOD/PODS'17: International Conference on Management of Data

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IL, Chicago, USA

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Repke TKrestel R(2021)Extraction and Representation of Financial Entities from TextData Science for Economics and Finance10.1007/978-3-030-66891-4_11(241-263)Online publication date: 9-Jan-2021
https://doi.org/10.1007/978-3-030-66891-4_11
Raschid LBurdick DFlood MGrant JLangsam JSoboroff I(2017)Financial Entity Identification and Information Integration (FEIII) 2017 ChallengeProceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets10.1145/3077240.3077248(1-4)Online publication date: 14-May-2017
https://dl.acm.org/doi/10.1145/3077240.3077248

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Empirical models based on features ranking techniques for corporate financial distress prediction

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