DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem

• URL: http://arxiv.org/abs/2402.16159v5
• Date: Thu, 20 Jun 2024 12:43:57 GMT
• Title: DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem
• Authors: Somnath Banerjee, Avik Dutta, Aaditya Agrawal, Rima Hazra, Animesh Mukherjee,
• Abstract summary: This paper proposes a novel named entity recognition (NER) technique specifically tailored for the open-source software systems. Our approach aims to address the scarcity of annotated software data by employing a comprehensive two-step distantly supervised annotation process. It is noteworthy that our model significantly outperforms the state-of-the-art LLMs by a substantial margin.
• Score: 4.368725325557961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the AI revolution in place, the trend for building automated systems to support professionals in different domains such as the open source software systems, healthcare systems, banking systems, transportation systems and many others have become increasingly prominent. A crucial requirement in the automation of support tools for such systems is the early identification of named entities, which serves as a foundation for developing specialized functionalities. However, due to the specific nature of each domain, different technical terminologies and specialized languages, expert annotation of available data becomes expensive and challenging. In light of these challenges, this paper proposes a novel named entity recognition (NER) technique specifically tailored for the open-source software systems. Our approach aims to address the scarcity of annotated software data by employing a comprehensive two-step distantly supervised annotation process. This process strategically leverages language heuristics, unique lookup tables, external knowledge sources, and an active learning approach. By harnessing these powerful techniques, we not only enhance model performance but also effectively mitigate the limitations associated with cost and the scarcity of expert annotators. It is noteworthy that our model significantly outperforms the state-of-the-art LLMs by a substantial margin. We also show the effectiveness of NER in the downstream task of relation extraction.

Related papers

• A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models [16.250856588632637]
  The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence. These models are increasingly integrated into diverse applications, impacting both research and industry. This paper surveys hardware and software co-design approaches specifically tailored to address the unique characteristics and constraints of large language models.
  arXiv  Detail & Related papers  (2024-10-08T21:46:52Z)
• Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AI [2.9189409618561966]
  Book chapter explores the evolving landscape of Software Engineering in general, and Requirements Engineering (RE) in particular. We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems. Book provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary.
  arXiv  Detail & Related papers  (2024-02-26T19:19:47Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing [0.0]
  We developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain.
  arXiv  Detail & Related papers  (2022-01-13T10:43:49Z)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• Engineering an Intelligent Essay Scoring and Feedback System: An Experience Report [1.5168188294440734]
  We describe an exploratory system for assessing the quality of essays supplied by customers of a specialized recruitment support service. The problem domain is challenging because the open-ended customer-supplied source text has considerable scope for ambiguity and error. There is also a need to incorporate specialized business domain knowledge into the intelligent processing systems.
  arXiv  Detail & Related papers  (2021-03-25T03:46:05Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)
• Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline [11.155059219430207]
  This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems. We present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers.
  arXiv  Detail & Related papers  (2020-01-20T12:17:22Z)

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.