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Method and Dataset Mining in Scientific Papers
Literature analysis facilitates researchers better understanding the dev...
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AI in society and culture: decision making and values
With the increased expectation of artificial intelligence, academic rese...
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TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph
Knowledge graph is a kind of valuable knowledge base which would benefit...
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Using the Full-text Content of Academic Articles to Identify and Evaluate Algorithm Entities in the Domain of Natural Language Processing
In the era of big data, the advancement, improvement, and application of...
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On Quantifying and Understanding the Role of Ethics in AI Research: A Historical Account of Flagship Conferences and Journals
Recent developments in AI, Machine Learning and Robotics have raised con...
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The Key Concepts of Ethics of Artificial Intelligence - A Keyword based Systematic Mapping Study
The growing influence and decision-making capacities of Autonomous syste...
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Increasing Papers' Discoverability with Precise Semantic Labeling: the sci.AI Platform
The number of published findings in biomedicine increases continually. A...
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AI Marker-based Large-scale AI Literature Mining
The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets and metrics are used as AI markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the entity extraction model is used in this study to extract AI markers from large-scale AI literature. Secondly, original papers are traced for AI markers. Statistical and propagation analysis are performed based on tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters and the influencing relationships amongst different research scene clusters are explored. The above-mentioned mining based on AI markers yields many meaningful discoveries. For example, the propagation of effective methods on the datasets is rapidly increasing with the development of time; effective methods proposed by China in recent years have increasing influence on other countries, whilst France is the opposite. Saliency detection, a classic computer vision research scene, is the least likely to be affected by other research scenes.
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