Extracting Complex Relations from Banking Documents
In order to automate banking processes (e.g. payments, money transfers, foreign trade), we need to extract banking transactions from different types of mediums such as faxes, e-mails, and scanners. Banking orders may be considered as complex documents since they contain quite complex relations compared to traditional datasets used in relation extraction research. In this paper, we present our method to extract intersentential, nested and complex relations from banking orders, and introduce a relation extraction method based on maximal clique factorization technique. We demonstrate 11{\%} error reduction over previous methods.
Code (0)
등록된 구현이 없습니다.
Tasks
RelationRelation ExtractionSimilar Papers 제목 키워드 기반
Towards Dynamic Feature Selection with Attention to Assist Banking Customers in Establishing a New Business
Establishing a new business may involve Knowledge acquisition in various areas, from personal to business and marketing sources. This task is challenging as it requires examining various data islands to uncover hidden pa…
feature selectionMarketingExtracting Summary Knowledge Graphs from Long Documents
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications. Extracting compact knowledge graphs containing only salient entities and relations is impo…
Graph LearningKnowledge GraphsText SummarizationMultimodal Document Analytics for Banking Process Automation
Traditional banks face increasing competition from FinTechs in the rapidly evolving financial ecosystem. Raising operational efficiency is vital to address this challenge. Our study aims to improve the efficiency of docu…
token-classificationToken ClassificationGPT3-to-plan: Extracting plans from text using GPT-3
Operations in many essential industries including finance and banking are often characterized by the need to perform repetitive sequential tasks. Despite their criticality to the business, workflows are rarely fully auto…
TranslationExtracting Sentiment Attitudes From Analytical Texts
In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations. For each document we annotated sentiments from the author to mentioned named entities, and sentiments of …
BIG-bench Machine Learning