Gesar Epic Entity Extraction Method Incorporating BERT Syllable Embedding

While the traditional syllable vector representation is too homogeneous, which leads to the optimal performance of the downstream task. To address this problem, this paper proposes the BERT-BILSTM-CRF method with Tibetan syllables as the basic unit.

BERT, as a multi-layer representation learning, allows for enhanced semantic representation of Tibetan syllables and dynamic generation of syllable vectors based on contextual features through representation learning of Tibetan syllables, and thus more accurate identification of Gesar epic named entities. What is shown experimentally is that the method works well on the Gesar Classic corpus of the Four Descending Histories.