An empirical evaluation of AMR parsing for legal documents

11/20/2018
by   Sinh Vu Trong, et al.
0

Many approaches have been proposed to tackle the problem of Abstract Meaning Representation (AMR) parsing, helps solving various natural language processing issues recently. In our paper, we provide an overview of different methods in AMR parsing and their performances when analyzing legal documents. We conduct experiments of different AMR parsers on our annotated dataset extracted from the English version of Japanese Civil Code. Our results show the limitations as well as open a room for improvements of current parsing techniques when applying in this complicated domain.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/09/2019

Vietnamese transition-based dependency parsing with supertag features

In recent years, dependency parsing is a fascinating research topic and ...
research
03/15/2022

Toward Improving Attentive Neural Networks in Legal Text Processing

In recent years, thanks to breakthroughs in neural network techniques es...
research
11/16/2017

ConvAMR: Abstract meaning representation parsing for legal document

Convolutional neural networks (CNN) have recently achieved remarkable pe...
research
04/26/2021

Toward Code Generation: A Survey and Lessons from Semantic Parsing

With the growth of natural language processing techniques and demand for...
research
04/14/2022

Brazilian Court Documents Clustered by Similarity Together Using Natural Language Processing Approaches with Transformers

Recent advances in Artificial intelligence (AI) have leveraged promising...
research
10/20/2020

Pushing the Limits of AMR Parsing with Self-Learning

Abstract Meaning Representation (AMR) parsing has experienced a notable ...
research
06/04/2020

SMIE: Weakness is Power!: Auto-indentation with incomplete information

Automatic indentation of source code is fundamentally a simple matter of...

Please sign up or login with your details

Forgot password? Click here to reset