Sebastian Erhardt, M.Sc.

Konferenzbeiträge

Erhardt, Sebastian; Ghosh, Mainak; Buunk, Erik; Rose, Michael; Harhoff, Dietmar (2024). Logic Mill - A Knowledge Navigation System, CEUR Workshop Proceedings 3775, 25-35.

• Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. It leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million
  documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.
• https://ceur-ws.org/Vol-3775/paper7.pdf
• Also published as: arXiv preprint 2301.00200
• Event: 5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2024) co-located with the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024), Washington D.C., 2024-10-24

Diskussionspapiere

Ghosh, Mainak; Erhardt, Sebastian; Rose, Michael; Buunk, Erik; Harhoff, Dietmar (2024). PaECTER: Patent-level Representation Learning using Citation-informed Transformers, arXiv preprint 2402.19411. DOI

• PaECTER is a publicly available, open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain. More specifically, our model outperforms the next-best patent specific pre-trained language model (BERT for Patents) on our patent citation prediction test dataset on two different rank evaluation metrics. PaECTER predicts at least one most similar patent at a rank of 1.32 on average when compared against 25 irrelevant patents. Numerical representations generated by PaECTER from patent text can be used for downstream tasks such as classification, tracing knowledge flows, or semantic similarity search. Semantic similarity search is especially relevant in the context of prior art search for both inventors and patent examiners. PaECTER is available on Hugging Face.

Erhardt, Sebastian; Ghosh, Mainak; Buunk, Erik; Rose, Michael; Harhoff, Dietmar (2022). Logic Mill - A Knowledge Navigation System, arXiv preprint 2301.00200.

• Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.
• https://doi.org/10.48550/arXiv.2301.00200
• Also published in: CEUR Workshop Proceedings 3775