Name
Title | ||
|---|---|---|
A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | LREC | Annotation,Computer science,Natural language processing,Artificial intelligence,Named-entity recognition,Schema (psychology),Relationship extraction |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saskia Schön | 1 | 0 | 0.34 |
| Veselina Mironova | 2 | 2 | 1.07 |
| aleksandra gabryszak | 3 | 5 | 2.79 |
| Leonhard Hennig | 4 | 72 | 10.62 |