2022/01/14 by M. Kaufmann, Kaufmann, Michael
Computer Science · Decision Sciences · #Advanced Text Analysis Techniques #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2201.05327
openalex publication_date 2022/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Entity relationship extraction envisions the automatic generation of semantic\ndata models from collections of text, by automatic recognition of entities, by\nassociation of entities to form relationships, and by classifying these\ninstances to assign them to entity sets (or classes) and relationship sets (or\nassociations). As a first step in this direction, the Lokahi prototype can\nextract entities based on the TF*IDF measure, and generate semantic\nrelationships based on document-level co-occurrence statistics, for example\nwith likelihood ratios and pointwise mutual information. This paper presents\nresults of an explorative, prototypical, qualitative and synthetic research,\nsummarizes insights from two research projects and, based on this, indicates an\noutline for further research in the field of entity relationship extraction\nfrom text.\n