Abstract: Aiming at the problem of the conflict between fine-grained and coarse-grained chunk recognition models in parsing, in order to solve the problem of multiple collocation rules in parsing and reduce the influence of collocation priority changes, we proposed a hierarchical parsing model which combined conditional random field (CRF) with multiple rules. First, CRF algorithm was used to identify the chunk tag sequence of the fine-grained sentence, and then the coarse-grained chunks were identified by combining statistics and multiple rules, and binary and ternary rules of different priorities were introduced into the identified chunks. The model realized the identification of fine-grained and coarse-grained chunks at the same time, which could better serve parsing. On the Chinese TreeBank8.0 corpus, the 5-fold cross-validation method was used for experimental verification. The results show that it is compared with the parsing using only binary and ternary rules, as well as the use of binary rules and CRF, the accuracy of the model is improved by nearly 12%,3%,5%, respectively, which verifies the effectiveness and stability of the model.

Key words: hierarchical parsing, conditional random field, multiple rules, chunk recognition