A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products
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Abstract (arXiv)
Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as supply chain monitoring and market research. However, there is a decided lack of annotated corpora and annotation guidelines in this domain. In this work, we present a corpus study, an annotation schema and associated guidelines, for the annotation of product entity and company-product relation mentions. We find that although product mentions are often realized as noun phrases, defining their exact extent is difficult due to high boundary ambiguity and the broad syntactic and semantic variety of their surface realizations. We also describe our ongoing annotation effort, and present a preliminary corpus of English web and social media documents annotated according to the proposed guidelines.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2021KazNERD: Kazakh Named Entity Recognition Datasetcited by 15
- 2023Named entity recognition using GPT for identifying comparable companiescited by 2
- 2024CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extractioncited by 0
- 2024PyTorch-IE: Fast and Reproducible Prototyping for Information Extractioncited by 0
- 2026Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Casecited by 0
- 2026Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case29 Jul 2026
- 2024PyTorch-IE: Fast and Reproducible Prototyping for Information Extraction16 May 2024
- 2024CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction22 Mar 2024
- 2023Named entity recognition using GPT for identifying comparable companies11 Jul 2023
- 2021KazNERD: Kazakh Named Entity Recognition Dataset26 Nov 2021