Semantics analysis model based on deep learning for vessel traffic service application

Vessel Traffic Service (VTS) significantly improves the navigation efficiency of ports. This paper proposes a model called Joint Extraction Triples from VHF Speech (JER-VHF) to ensure VTS. Numerous texts are extracted Very High Frequency (VHF) speech communication contents and these organized into dataset named VHFDT. The proposed model's transforming task transforms voice this triple representation. VHFDT has large number overlapping triples. Therefore, combined with three categories entity relations in sentences, including pre-training Chinese language for initializing embedding VHFDT, BiLSTM rich features, Multi-head Attention focusing on In experimental part, study uses Precision(P), Recall(R), F1 evaluate accuracy effectiveness method baseline models. According results, efficiently extracts key information complex environment achieves better work relational extraction than other achieved an F1-score 83.2% testing data, which is improvement 1.8% compared second-best model.