@inproceedings{tandon-chatterjee-2023-lrl-nc,
title = "{LRL}{\_}{NC} at {S}em{E}val-2023 Task 6: Sequential Sentence Classification for Legal Documents Using Topic Modeling Features",
author = "Tandon, Kushagri and
Chatterjee, Niladri",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.20",
doi = "10.18653/v1/2023.semeval-1.20",
pages = "143--149",
abstract = "Natural Language Processing techniques can be leveraged to process legal proceedings for various downstream applications, such as sum- marization of a given judgement, prediction of the judgement for a given legal case, prece- dent search, among others. These applications will benefit from legal judgement documents already segmented into topically coherent units. The current task, namely, Rhetorical Role Pre- diction, aims at categorising each sentence in the sequence of sentences in a judgement document into different labels. The system proposed in this work combines topic mod- eling and RoBERTa to encode sentences in each document. A BiLSTM layer has been utilised to get contextualised sentence repre- sentations. The Rhetorical Role predictions for each sentence in each document are gen- erated by a final CRF layer of the proposed neuro-computing system. This system secured the rank 12 in the official task ranking, achiev- ing the micro-F1 score 0.7980. The code for the proposed systems has been made available at \url{https://github.com/KushagriT/SemEval23_} LegalEval{\_}TeamLRL{\_}NC",
}
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<abstract>Natural Language Processing techniques can be leveraged to process legal proceedings for various downstream applications, such as sum- marization of a given judgement, prediction of the judgement for a given legal case, prece- dent search, among others. These applications will benefit from legal judgement documents already segmented into topically coherent units. The current task, namely, Rhetorical Role Pre- diction, aims at categorising each sentence in the sequence of sentences in a judgement document into different labels. The system proposed in this work combines topic mod- eling and RoBERTa to encode sentences in each document. A BiLSTM layer has been utilised to get contextualised sentence repre- sentations. The Rhetorical Role predictions for each sentence in each document are gen- erated by a final CRF layer of the proposed neuro-computing system. This system secured the rank 12 in the official task ranking, achiev- ing the micro-F1 score 0.7980. The code for the proposed systems has been made available at https://github.com/KushagriT/SemEval23_ LegalEval_TeamLRL_NC</abstract>
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%0 Conference Proceedings
%T LRL_NC at SemEval-2023 Task 6: Sequential Sentence Classification for Legal Documents Using Topic Modeling Features
%A Tandon, Kushagri
%A Chatterjee, Niladri
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F tandon-chatterjee-2023-lrl-nc
%X Natural Language Processing techniques can be leveraged to process legal proceedings for various downstream applications, such as sum- marization of a given judgement, prediction of the judgement for a given legal case, prece- dent search, among others. These applications will benefit from legal judgement documents already segmented into topically coherent units. The current task, namely, Rhetorical Role Pre- diction, aims at categorising each sentence in the sequence of sentences in a judgement document into different labels. The system proposed in this work combines topic mod- eling and RoBERTa to encode sentences in each document. A BiLSTM layer has been utilised to get contextualised sentence repre- sentations. The Rhetorical Role predictions for each sentence in each document are gen- erated by a final CRF layer of the proposed neuro-computing system. This system secured the rank 12 in the official task ranking, achiev- ing the micro-F1 score 0.7980. The code for the proposed systems has been made available at https://github.com/KushagriT/SemEval23_ LegalEval_TeamLRL_NC
%R 10.18653/v1/2023.semeval-1.20
%U https://aclanthology.org/2023.semeval-1.20
%U https://doi.org/10.18653/v1/2023.semeval-1.20
%P 143-149
Markdown (Informal)
[LRL_NC at SemEval-2023 Task 6: Sequential Sentence Classification for Legal Documents Using Topic Modeling Features](https://aclanthology.org/2023.semeval-1.20) (Tandon & Chatterjee, SemEval 2023)
ACL