Method of Tibetan Person Knowledge Extraction

TL;DR

This paper introduces a novel SVM and template-based method for extracting Tibetan person knowledge, enhancing Tibetan knowledge graph construction and supporting related applications.

Contribution

It presents a hierarchical SVM classifier combined with template analysis for improved Tibetan person knowledge extraction, a novel approach in this context.

Findings

Significant improvement in Tibetan person knowledge extraction accuracy

Effective use of shallow parsing and semantic features

Enhanced support for Tibetan question answering systems

Abstract

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.