Paper Reading AI Learner

Classification of Contract-Amendment Relationships

2021-06-08 07:57:10
Fuqi Song
arXiv_CL
               
Abstract

In Contract Life-cycle Management (CLM), managing and tracking the master agreements and their associated amendments is essential, in order to be kept informed with different due dates and obligations. An automatic solution can facilitate the daily jobs and improve the efficiency of legal practitioners. In this paper, we propose an approach based on machine learning (ML) and Natural Language Processing (NLP) to detect the amendment relationship between two documents. The algorithm takes two PDF documents preprocessed by OCR (Optical Character Recognition) and NER (Named Entity Recognition) as input, and then it builds the features of each document pair and classifies the relationship. We experimented with different configurations on a dataset consisting of 1124 pairs of contract-amendment documents in English and French. The best result obtained a F1-score of 91%, which outperformed 23% compared to a heuristic-based baseline.

Abstract (translated)

URL

https://arxiv.org/abs/2106.14619

PDF

https://arxiv.org/pdf/2106.14619.pdf

Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot