Igor Lončarski


2022

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Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports
Matthew Purver | Matej Martinc | Riste Ichev | Igor Lončarski | Katarina Sitar Šuštar | Aljoša Valentinčič | Senja Pollak
Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference

We describe initial work into analysing the language used around environmental, social and governance (ESG) issues in UK company annual reports. We collect a dataset of annual reports from UK FTSE350 companies over the years 2012-2019; separately, we define a categorized list of core ESG terms (single words and multi-word expressions) by combining existing lists with manual annotation. We then show that this list can be used to analyse the changes in ESG language in the dataset over time, via a combination of language modelling and distributional modelling via contextual word embeddings. Initial findings show that while ESG discussion in annual reports is becoming significantly more likely over time, the increase varies with category and with individual terms, and that some terms show noticeable changes in usage.

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Sentiment Classification by Incorporating Background Knowledge from Financial Ontologies
Timen Stepišnik-Perdih | Andraž Pelicon | Blaž Škrlj | Martin Žnidaršič | Igor Lončarski | Senja Pollak
Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022

Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. This paper presents a practical use of an ontology for the purpose of data set generalization in an oversampling setting, with the aim of improving classification models. We demonstrate our solution on a novel financial sentiment data set using the Financial Industry Business Ontology (FIBO). The results show that generalization-based data enrichment benefits simpler models in a general setting and more complex models such as BERT in low-data setting.