Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism

Yoo, Yongmin; Heo, Tak-Sung; Lim, Dong-Jin; Seo, Deaho

doi:10.48550/arxiv.2303.03165

Cited by 1 publication

(2 citation statements)

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“…In the latest research conducted in 2023, the exploration of the Cooperative Patent Classification (CPC) system has continued to evolve. Yoo et al [11] examine multi-label classification of Artificial Intelligence-related patents, utilizing Modified D2SBERT and Sentence Attention mechanisms. Meanwhile, Ha and Lee [12] explores the effectiveness of the CPC system, focusing on patent embeddings.…”

Section: Related Workmentioning

confidence: 99%

“…In the ever-evolving landscape of patent analysis and classification, the year 2023 has witnessed the emergence of significant research contributions. Yoo et al [11] delve into multi-label classification of Artificial Intelligencerelated patents, employing advanced techniques. Ha and Lee [12] focus on evaluating the Cooperative Patent Classification (CPC) system, with a particular emphasis on patent embeddings.…”

Section: Introductionmentioning

confidence: 99%

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Similarity Matching for Patent Documents Using Ensemble BERT-Related Model and Novel Text Processing Method

Yu,

Liu,

Lin

et al. 2024

JAIT

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In the domain of analyzing patent documents, evaluating the semantic similarity between phrases poses a considerable challenge, particularly accentuating the inherent complexities associated with Cooperative Patent Classification (CPC) research. Firstly, this study addresses these challenges, recognizing early CPC work while acknowledging past struggles with language barriers and document intricacy. Secondly, it underscores the persisting difficulties of CPC research. To overcome these challenges and bolster the CPC system, this paper presents two key innovations. Firstly, it introduces an ensemble approach that incorporates four Bidirectional Encoder Representations from Transformers (BERT)-related models, enhancing semantic similarity accuracy through weighted averaging. Secondly, a novel text preprocessing method tailored for patent documents is introduced, featuring a distinctive input structure with token scoring those aids in capturing semantic relationships during CPC context training, utilizing Binary Cross-Entropy Loss (BCELoss). Our experimental findings conclusively establish the effectiveness of both our Ensemble Model and novel text processing strategies when deployed on the U.S. Patent Phrase to Phrase Matching dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%