Abstract
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.
Highlights
In natural language processing (NLP), semantic role labeling (SRL) is one of the important techniques of semantic analysis. e purpose is to label all semantic roles related to predicates in sentences
It can promote the research of deep semantic analysis and text understanding. e main research methods for SRL include CRF, support vector machine (SVM), and other linear machine learning methods; research direction includes named entity recognition (NER), part-ofspeech (POS) for SRL, and so on
According to the existing research on SRL, taking the BiLSTM-CRF model as the basic model, this paper studies the improvement method of Chinese SRL, selectively expands the existing basic features, and adds multilevel linguistic features for comparative analysis and the improved precision is demonstrated in experiments
Summary
In natural language processing (NLP), semantic role labeling (SRL) is one of the important techniques of semantic analysis. e purpose is to label all semantic roles related to predicates in sentences. Compared with deep semantic analysis, SRL has the characteristics of simple labeling, clear structure, and easy display. It has a wide range of practical value prospects in many application fields such as question answering (QA) system, information extraction (IE), machine translation (MT), etc. What is more, it can promote the research of deep semantic analysis and text understanding. According to the existing research on SRL, taking the BiLSTM-CRF model as the basic model, this paper studies the improvement method of Chinese SRL, selectively expands the existing basic features, and adds multilevel linguistic features for comparative analysis and the improved precision is demonstrated in experiments
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