Chinese Semantic Role Labeling (SRL) is the core technology of semantic understanding. In the field of Chinese information processing, where statistical machine learning is still the mainstream, the traditional labeling methods rely heavily on the parsing degree of syntax and semantics of sentences. Therefore, the labeling precision is limited and cannot meet the current needs. This paper adopts the model based on a bidirectional long short-term memory network combined with the Conditional Random Field (Bi-LSTM-CRF). In the feature processing stage, pooling technology is combined with sampling and selecting multifeature vector groups to improve the performance of the sequence labeling model. Lexical, syntactic, and other multilevel linguistic features are integrated into the training to realize in-depth improvement of the original labeling model. Through several groups of experiments, the precision of model annotation in this paper has been significantly improved combined with linguistic-assisted analysis, which proves that it can optimize the annotation performance of the model by integrating relevant linguistic features into the model based on Bi-LSTM-CRF and sampling and extracting multifeature groups; the evaluation of F increases to 82.18 percent.
Abstract-This paper aims at drawing and analyzing the Articulation Places during production of Mandarin Chinese consonants by means of Electropalatography (EPG). Their tongue characteristics of onset are obtained: the articulation places of plosives are latter than the same manner of place of affricates, and the concentration degree of tongue-palate contact from both sides to the middle concentration degree is slightly higher; the tongue and hard palate contact of affricates is bigger than fricatives', tongue position is also slightly higher than fricatives'; aspiration will have an influence on target palate position, aspiration consonants' TC、AC is smaller than unaspiration's generally, tongue position of unaspiration is slightly higher than aspiration's.
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