FiNCAT: Financial Numeral Claim Analysis Tool

01/26/2022
by   Sohom Ghosh, et al.
0

While making investment decisions by reading financial documents, investors need to differentiate between in-claim and outof-claim numerals. In this paper, we present a tool which does it automatically. It extracts context embeddings of the numerals using one of the transformer based pre-trained language model called BERT. After this, it uses a Logistic Regression based model to detect whether the numerals is in-claim or out-of-claim. We use FinNum-3 (English) dataset to train our model. After conducting rigorous experiments we achieve a Macro F1 score of 0.8223 on the validation set. We have open-sourced this tool and it can be accessed from https://github.com/sohomghosh/FiNCAT_Financial_Numeral_Claim_Analysis_Tool

READ FULL TEXT

page 1

page 2

page 3

research
04/22/2022

Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language

This work describes the development of different models to detect patron...
research
03/14/2021

Claim Verification using a Multi-GAN based Model

This article describes research on claim verification carried out using ...
research
02/24/2021

Hopeful_Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers

This paper aims to describe the approach we used to detect hope speech i...
research
02/19/2021

Using Transformer based Ensemble Learning to classify Scientific Articles

Many time reviewers fail to appreciate novel ideas of a researcher and p...
research
07/01/2019

Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning

Claims are a fundamental unit of scientific discourse. The exponential g...
research
01/28/2021

LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content

The conceptualization of a claim lies at the core of argument mining. Th...
research
08/31/2022

Audiogram Digitization Tool for Audiological Reports

A number of private and public insurers compensate workers whose hearing...

Please sign up or login with your details

Forgot password? Click here to reset