Work

BUSTER: a “BUSiness Transaction Entity Recognition” dataset

Year: 2023

Type: conference-paper

Abstract: Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.

Source: Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP)

DOI: 10.18653/v1/2023.emnlp-industry.57

Authors: Andrea Zugarini, Andrew Zamai, Marco Ernandes, Leonardo Rigutini

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: European Regional Development Fund

Awards: G1464101492, G1888088267

OpenAlex ID: W4389524011

FWCI: 0.595

Cites: 18

Cited by: 3

Related to: 10