Corpus-Driven Annotation Enrichment

摘要

A reference library can be described as a corpus of an individual composition of documents containing related work of research, documents of favorite authors, or proceedings of a conference. Enriching documents with meaningful annotations is beneficial for the performance of applications like semantic search, content aggregation, automated relationship discovery, query answering and information retrieval. Available (semi-) automatic annotation tools ignore the individual composition of documents in corpora by annotating documents with generic named-entity related data. In this paper, we present and unsupervised corpus-driven annotation enrichment approach considering the composition of documents and use an EM-like algorithm to enrich weakly annotated documents with meaningful annotations of related documents from the same corpus.