Combining machine learning and semantic features in the classification of corporate disclosures

DOI10.1007/s10849-019-09283-6zbMath1475.68399OpenAlexW2919606042MaRDI QIDQ2425335

Philipp Heinrich, Lutz Schröder, Elisabeth Scherr, Ulrich Rabenstein, Martin Schmitt, Klaus Henselmann, Stefan Evert

Publication date: 26 June 2019

Published in: Journal of Logic, Language and Information (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10849-019-09283-6

zbMATH Keywords

NLPmachine learningontologycorporate disclosures

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonlinear programming (90C30) Learning and adaptive systems in artificial intelligence (68T05) Knowledge representation (68T30) Natural language processing (68T50)

Uses Software

• WordNet
• Stanford Tagger
• Scikit

Cites Work

• Unnamed Item
• Unnamed Item
• Modeling, learning, and processing of text-technological data structures

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