In rabbit experiments, some drugs used in clinics were selected and combined with electroacupuncture to observe the effect of drugs on acupuncture analgesia (AA). According to the effect of drugs on AA, the drugs were divided into three kinds: (1) Drugs with potentiating effect: fentanyl, pethidine, droperidol, perphenazine, metoclopramide, fenfluramine, tetrahydrocannabinol, fentanyl plus droperidol, fentanyl plus fenfluramine. (2) Drugs with reducing effect: ketamine and diazepam, (3) Drugs with no effect: sulpiride.
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.
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