Identifying and classifying personal, geographic, institutional or other names in a text is an important task for numerous applications. This paper describes and evaluates a language-independent bootstrapping algorithm based on iterative learning and re-estimation of contextual and mOrphological patterns captured in hierarchically smoothed trie models. The algorithm learns from unannotated text and achieves competitive performance when trained on a very short labelled name list with no other required language-specific information, tokenizers or tools.
The first paper REF on Turkish NER describes a language independent bootstrapping algorithm that learns from word internal and contextual information of entities.
Language Independent Named Entity Recognition Combining Morphological And Contextual Evidence
Joint SIGDAT Conference On Empirical Methods In Natural Language Processing And Very Large Corpora
Computer Science