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.

Related papers

• Enhancing literature review with LLM and NLP methods. Algorithmic trading case [0.0]
  This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020.
  arXiv  Detail & Related papers  (2024-10-23T13:37:27Z)
• Model-Free Active Exploration in Reinforcement Learning [53.786439742572995]
  We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. Our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches.
  arXiv  Detail & Related papers  (2024-06-30T19:00:49Z)
• Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement [69.51496713076253]
  In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL.
  arXiv  Detail & Related papers  (2024-02-05T22:15:55Z)
• Behind the Intent of Extract Method Refactoring: A Systematic Literature Review [15.194527511076725]
  Code is widely recognized as an essential software engineering practice to improve the understandability and maintainability of the source code. The Extract Method is considered as "Swiss army knife" of applicabilitys, as developers often apply it to improve their code quality. In recent years, several studies attempted to recommend Extract Method, allowing the collection, analysis, and revelation of actionable data-driven insights.
  arXiv  Detail & Related papers  (2023-12-19T21:09:54Z)
• IRGen: Generative Modeling for Image Retrieval [82.62022344988993]
  In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling. We develop our model, dubbed IRGen, to address the technical challenge of converting an image into a concise sequence of semantic units. Our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks and two million-scale datasets.
  arXiv  Detail & Related papers  (2023-03-17T17:07:36Z)
• Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review [77.34726150561087]
  This review considers the existing methods for determining intrinsic motivation based on the world model obtained by the agent. The proposed unified framework describes the architecture of agents using a world model and intrinsic motivation to improve learning.
  arXiv  Detail & Related papers  (2023-01-24T15:13:02Z)
• Generalization Properties of Retrieval-based Models [50.35325326050263]
  Retrieval-based machine learning methods have enjoyed success on a wide range of problems. Despite growing literature showcasing the promise of these models, the theoretical underpinning for such models remains underexplored. We present a formal treatment of retrieval-based models to characterize their generalization ability.
  arXiv  Detail & Related papers  (2022-10-06T00:33:01Z)
• A Review on Method Entities in the Academic Literature: Extraction, Evaluation, and Application [15.217159196570108]
  In scientific research, the method is an indispensable means to solve scientific problems and a critical research object. Key entities in academic literature reflecting names of the method are called method entities. The evolution of method entities can reveal the development of a discipline and facilitate knowledge discovery.
  arXiv  Detail & Related papers  (2022-09-08T10:12:21Z)
• Partitioned Active Learning for Heterogeneous Systems [5.331649110169476]
  We propose the partitioned active learning strategy established upon partitioned GP (PGP) modeling. Global searching scheme accelerates the exploration aspect of active learning. Local searching exploits the active learning criterion induced by the local GP model.
  arXiv  Detail & Related papers  (2021-05-14T02:05:31Z)
• Recent Progress in Appearance-based Action Recognition [73.6405863243707]
  Action recognition is a task to identify various human actions in a video. Recent appearance-based methods have achieved promising progress towards accurate action recognition.
  arXiv  Detail & Related papers  (2020-11-25T10:18:12Z)
• A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs [2.299749220980997]
  We formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial. We propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods.
  arXiv  Detail & Related papers  (2020-07-01T00:35:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.