Natural Language Processing Techniques for Long Financial Document

Maria Saveria Mavillonio

Discussion Papers from Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy

Abstract: In finance, Natural Language Processing (NLP) has become both a powerful and challenging tool, as extensive unstructured documents—such as business plans, financial reports, and regulatory filings—hold essential insights for strategic decision-making. This paper reviews the progression of NLP text representation methods, from foundational models to advanced Transformer architectures that greatly enhance semantic and contextual analysis. Yet, these models encounter limitations when applied to long financial documents, where computational efficiency and contextual coherence are critical. Recent innovations, including sparse attention mechanisms and domain-specific model adaptations, have improved the processing of lengthy texts, allowing for more accurate analysis of financial documents by capturing field-specific semantics. This paper also highlights the transformative role of NLP in financial analysis, especially where structured data is limited. Selecting the most suitable model for specific tasks is essential for maximizing NLP's impact in finance. Organized to provide a thorough overview, the paper covers text representation techniques, strategies for handling long texts, and applications in finance, establishing a foundation for advancing NLP-driven data analysis in this field.

Keywords: Long Text; Financial Document Representation; Natural Language Processing; Transformers (search for similar items in EconPapers)
JEL-codes: C45 G2 G23 L26 (search for similar items in EconPapers)
Date: 2024-11-01
New Economics Papers: this item is included in nep-big
Note: ISSN 2039-1854
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Persistent link: https://EconPapers.repec.org/RePEc:pie:dsedps:2024/317

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