Method of Tibetan Person Knowledge Extraction
This work addresses a domain-specific problem for Tibetan language processing, supporting knowledge graph construction and related applications.
The paper tackled Tibetan person knowledge extraction by proposing an SVM and template-based approach, achieving greater improvement in extraction performance.
Person knowledge extraction is the foundation of the Tibetan knowledge graph construction, which provides support for Tibetan question answering system, information retrieval, information extraction and other researches, and promotes national unity and social stability. This paper proposes a SVM and template based approach to Tibetan person knowledge extraction. Through constructing the training corpus, we build the templates based the shallow parsing analysis of Tibetan syntactic, semantic features and verbs. Using the training corpus, we design a hierarchical SVM classifier to realize the entity knowledge extraction. Finally, experimental results prove the method has greater improvement in Tibetan person knowledge extraction.