uOttawa at LegalLens-2024: Transformer-based Classification Experiments

2024-10-28 · Nima Meghdadi, Diana Inkpen

This paper presents the methods used for LegalLens-2024 shared task, which focused on detecting legal violations within unstructured textual data and associating these violations with potentially affected individuals. The shared task included two subtasks: A) Legal Named Entity Recognition (L-NER) and B) Legal Natural Language Inference (L-NLI). For subtask A, we utilized the spaCy library, while for subtask B, we employed a combined model incorporating RoBERTa and CNN. Our results were 86.3% in the L-NER subtask and 88.25% in the L-NLI subtask. Overall, our paper demonstrates the effectiveness of transformer models in addressing complex tasks in the legal domain. The source code for our implementation is publicly available at https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification

📄 PDF Abstract BibTeX arXiv:2410.21139

Code (1)

nimameghdadi/uottawa-at-legallens-2024-transformer-based-classification 공식 구현

Tasks

Classificationnamed-entity-recognitionNamed Entity RecognitionNatural Language Inference NER

Methods 이 논문이 사용한 방법론

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Attention 설명 없음

Adam 설명 없음

Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

Weight Decay 설명 없음

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text

2024-10-15 · Ben Hagag, Liav Harpaz, Gil Semo, Dor Bernsohn 외

This paper presents the results of the LegalLens Shared Task, focusing on detecting legal violations within text in the wild across two sub-tasks: LegalLens-NER for identifying legal violation entities and LegalLens-NLI …

NER

Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and Resolution

2024-10-30 · Shikha Bordia

In this work, we present two systems -- Named Entity Resolution (NER) and Natural Language Inference (NLI) -- for detecting legal violations within unstructured textual data and for associating these violations with pote…

Entity Resolution Natural Language Inference NER

uOttawa: System description for SemEval 2013 Task 2 Sentiment Analysis in Twitter

2013-06-01 · SEMEVAL 2013 6 · Hamid Poursepanj, Josh Weissbock, Diana Inkpen

Sentiment Analysis Task 2

Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text

2018-08-01 · COLING 2018 8 · Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Inkpen 외

In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL). We use CSC-MTL to improve the performance of cyber-aggression detec…

General Classification Multi-Task Learning text-classification Text Classification

LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text

2024-02-06 · Dor Bernsohn, Gil Semo, Yaron Vazana, Gila Hayat 외

In this study, we focus on two main tasks, the first for detecting legal violations within unstructured textual data, and the second for associating these violations with potentially affected individuals. We constructed …

Experimental Design