# A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products

arXiv: https://arxiv.org/abs/2004.03287 · HTML: https://mlsanity.com/paper/2004.03287/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [KazNERD: Kazakh Named Entity Recognition Dataset](https://mlsanity.com/paper/2111.13419/) (2021)
- [Named entity recognition using GPT for identifying comparable companies](https://mlsanity.com/paper/2307.07420/) (2023)
- [CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2403.15322/) (2024)
- [PyTorch-IE: Fast and Reproducible Prototyping for Information Extraction](https://mlsanity.com/paper/2406.00007/) (2024)
- [Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case](https://mlsanity.com/paper/2607.26780/) (2026)

## 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.