Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Named Entity Recognition on Transaction Descriptions

Johansson, Nik LU (2022) In LU-CS-EX EDAM05 20212
Department of Computer Science
Abstract
With the surge of open banking, there is a large increase in applications based on transaction data. Therefore, there is a need for being able to extract important information from Swedish transaction descriptions in a structured way.
We designed models for named entity recognition on transaction descriptions that can identify and classify organizations, locations, persons, payment providers (e.g Swish, Klarna, or PayPal) and products/apps. With our best NER model, we reached a chunk F1 score 0.849, despite only using 2200 transactions for training and transaction descriptions being messy. This is, to the best of our knowledge, the first published report on named entity recognition for transaction descriptions.
Finally, we used our named... (More)
With the surge of open banking, there is a large increase in applications based on transaction data. Therefore, there is a need for being able to extract important information from Swedish transaction descriptions in a structured way.
We designed models for named entity recognition on transaction descriptions that can identify and classify organizations, locations, persons, payment providers (e.g Swish, Klarna, or PayPal) and products/apps. With our best NER model, we reached a chunk F1 score 0.849, despite only using 2200 transactions for training and transaction descriptions being messy. This is, to the best of our knowledge, the first published report on named entity recognition for transaction descriptions.
Finally, we used our named entity recognition model and its output to complement a commercial transaction categorization system and improve the performance of the two existing models by 7.4% and 1.1% respectively. (Less)
Please use this url to cite or link to this publication:
author
Johansson, Nik LU
supervisor
organization
alternative title
Named Entity Recognition på Transaktionsbeskrivningar
course
EDAM05 20212
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
LU-CS-EX
report number
2022-13
ISSN
1650-2884
language
English
id
9077863
date added to LUP
2022-03-30 13:54:40
date last changed
2022-04-04 08:19:34
@misc{9077863,
  abstract     = {{With the surge of open banking, there is a large increase in applications based on transaction data. Therefore, there is a need for being able to extract important information from Swedish transaction descriptions in a structured way.
We designed models for named entity recognition on transaction descriptions that can identify and classify organizations, locations, persons, payment providers (e.g Swish, Klarna, or PayPal) and products/apps. With our best NER model, we reached a chunk F1 score 0.849, despite only using 2200 transactions for training and transaction descriptions being messy. This is, to the best of our knowledge, the first published report on named entity recognition for transaction descriptions.
Finally, we used our named entity recognition model and its output to complement a commercial transaction categorization system and improve the performance of the two existing models by 7.4% and 1.1% respectively.}},
  author       = {{Johansson, Nik}},
  issn         = {{1650-2884}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{LU-CS-EX}},
  title        = {{Named Entity Recognition on Transaction Descriptions}},
  year         = {{2022}},
}