FinEAS: Financial Embedding Analysis of Sentiment

Gutiérrez-Fandiño, Asier; Kolm, Petter N.; Alonso, Miquel Noguer i; Armengol-Estapé, Jordi

doi:10.2139/ssrn.4028072

Cited by 2 publications

(1 citation statement)

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“…We subsequently tried out various other transformer-based models present in the Huggingface [70] model repository. This included RoBERTa [41], FinBERT [3], FinEAS [30] and so on. We observed that FinBERT when fine-tuned using the expanded data set further improved the performance.…”

Section: Methodsmentioning

confidence: 99%

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

Ghosh¹,

Chopra²,

Naskar³

2023

Preprint

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Over the years, there has been a paradigm shift in how users access financial services. With the advancement of digitalization more users have been preferring the online mode of performing financial activities. This has led to the generation of a huge volume of financial content. Most investors prefer to go through these contents before making decisions. Every industry has terms that are specific to the domain it operates in. Banking and Financial Services are not an exception to this. In order to fully comprehend these contents, one needs to have a thorough understanding of the financial terms. Getting a basic idea about a term becomes easy when it is explained with the help of the broad category to which it belongs. This broad category is referred to as hypernym. For example, "bond" is a hypernym of the financial term "alternative debenture". In this paper, we propose a system capable of extracting and ranking hypernyms for a given financial term. The system has been trained with financial text corpora obtained from various sources like DBpedia [4], Investopedia, Financial Industry Business Ontology (FIBO), prospectus and so on. Embeddings of these terms have been extracted using FinBERT [3], FinISH [1] and fine-tuned using SentenceBERT [54]. A novel approach has been used to augment the training set with negative samples. It uses the hierarchy present in FIBO. Finally, we benchmark the system performance with that of the existing ones. We establish that it performs better than the existing ones and is also scalable. Our code base will be made available at: https:// github.com/sohomghosh/FinSim Financial Hypernym detection after acceptance of this manuscript.

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Section: Methodsmentioning

confidence: 99%

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

Ghosh¹,

Chopra²,

Naskar³

2023

Preprint

View full text Add to dashboard Cite

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End-to-End Transformer-Based Models in Textual-Based NLP

Rahali

Akhloufi

2023

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Transformer architectures are highly expressive because they use self-attention mechanisms to encode long-range dependencies in the input sequences. In this paper, we present a literature review on Transformer-based (TB) models, providing a detailed overview of each model in comparison to the Transformer’s standard architecture. This survey focuses on TB models used in the field of Natural Language Processing (NLP) for textual-based tasks. We begin with an overview of the fundamental concepts at the heart of the success of these models. Then, we classify them based on their architecture and training mode. We compare the advantages and disadvantages of popular techniques in terms of architectural design and experimental value. Finally, we discuss open research, directions, and potential future work to help solve current TB application challenges in NLP.

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FinEAS: Financial Embedding Analysis of Sentiment

Cited by 2 publications

References 0 publications

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

End-to-End Transformer-Based Models in Textual-Based NLP

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