As a basic research of natural language processing, word sense disambiguation (WSD) has a very important influence on machine translation, classification tasks, retrieval tasks, etc. In order to solve the problem that existing disambiguation methods rely too much on knowledge base, a disambiguation method combining graph model and word vector is proposed in this paper. Firstly, in this method, the text data are preprocessed by removing punctuation marks and segmenting words. Secondly, the dependency relation is extracted by using the tool of PYLTP for dependency parsing, the words of dependency parent node are matched and the undirected graph is built, and the context knowledge of ambiguous words is selected according to the minimum path length set by the graph model. Finally, Word2Vec model is used to train Wikipedia corpus to obtain word vectors containing ambiguous words and contextual knowledge, and calculate the cross similarity of the word vector, the high mean similarity is regarded as the correct meaning of the ambiguous word. The effectiveness of the proposed method is verified by comparative experiments on the SEVAL-2007 Task# 5 dataset.
ObjectiveThe purpose of this study is to accurately monitor temperature during microwave hyperthermia. We propose a temperature estimation model BP‐Nakagami based on neural network for Nakagami distribution.MethodsIn this work, we designed the microwave hyperthermia experiment of fresh ex vivo pork tissue and phantom, collected ultrasonic backscatter data at different temperatures, modeled these data using Nakagami distribution, and calculated Nakagami distribution parameter m. A neural network model was built to train the relationship between Nakagami distribution parameter m and temperature, and a BP‐Nakagami temperature model with good fitting was obtained. The temperature model is used to draw the two‐dimensional temperature distribution map of biological tissues in microwave hyperthermia. Finally, the temperature estimated by the model is compared with the temperature measured by thermocouples.ResultsThe error between the temperature estimated by the temperature model and the temperature measured by the thermocouple is within 1°C in the range of 25°C–50°C for ex vivo pork tissue, and the error between the temperature estimated by the temperature model and the temperature measured by the thermocouple is within 0.5°C in the range of 25°C–50°C for phantom.ConclusionsThe results show that the temperature estimation model proposed by us is an effective model for monitoring the internal temperature change of biological tissues.
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