Arabic Entity Graph Extraction Using Morphology, Finite State Machines, and Graph Transformations

Research on automatic recognition of named entities from Arabic text uses techniques that work well for the Latin based languages such as local grammars, statistical learning models, pattern matching, and rule-based techniques. These techniques boost their results by using application specific corpora, parallel language corpora, and morphological stemming analysis. We propose a method for extracting entities, events, and relations amongst them from Arabic text using a hierarchy of finite state machines driven by morphological features such as part of speech and gloss tags, and graph transformation algorithms. We evaluated our method on two natural language processing applications. We automated the extraction of narrators and narrator relations from several corpora of Islamic narration books. We automated the extraction of genealogical family trees from Biblical texts. In all applications, our method reports high precision and recall and learns lemmas about phrases that improve results.