Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach

• URL: http://arxiv.org/abs/2311.03401v1
• Date: Sun, 5 Nov 2023 16:33:35 GMT
• Title: Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach
• Authors: Madhusudan Ghosh, Debasis Ganguly, Partha Basuchowdhuri, Sudip Kumar Naskar
• Abstract summary: We propose a factored approach to sequence modeling, which leverages a broad-level category information of methodology domains. We conduct experiments following a simulated chronological setup (newer methodologies not seen during the training process) Our experiments demonstrate that the factored approach outperforms state-of-the-art baselines by margins of up to 9.257% for the methodology extraction task with the few-shot setup.
• Score: 10.060305577353633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies. While methodologies themselves being conceptual in nature and rather difficult to automatically extract and characterise, in this paper, we seek to develop supervised models for automatic extraction of the names of the various constituents of a methodology, e.g., `R-CNN', `ELMo' etc. The main research challenge for this task is effectively modeling the contexts around these methodology component names in a few-shot or even a zero-shot setting. The main contributions of this paper towards effectively identifying new evolving scientific methodology names are as follows: i) we propose a factored approach to sequence modeling, which leverages a broad-level category information of methodology domains, e.g., `NLP', `RL' etc.; ii) to demonstrate the feasibility of our proposed approach of identifying methodology component names under a practical setting of fast evolving AI literature, we conduct experiments following a simulated chronological setup (newer methodologies not seen during the training process); iii) our experiments demonstrate that the factored approach outperforms state-of-the-art baselines by margins of up to 9.257\% for the methodology extraction task with the few-shot setup.

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