Semantic Recognition System Research Articles

English-Chinese Corpus Collection and Artificial Intelligence Translation Based on Dynamic Clustering Model

This paper studies the English-Chinese corpus collection system and artificial intelligence translation function based on the dynamic clustering model. This paper attempts to introduce the semantic analysis of English-Chinese parallel corpora into the semantic recognition system. By labeling different levels of the corpus, including grammatical labeling, shallow semantic labeling and deep semantic labeling, the corresponding English-Chinese translation teaching system is designed, and the teaching system is improved by using the comparatively systematic teaching effects in actual teaching. On the basis of self-built Chinese-English parallel corpus, this paper extracts the Chinese high-frequency verb "to improve" to discover and summarize its English translation rules in different texts.

Image Semantic Analysis in Visual Media Art Using Machine Learning and Neural Machine Translation

The current archaeological work in China has the problems of high cost, large material consumption, more attention on human detection and long time-consuming. It is urgent to use modern high-precision detection technology for auxiliary work. This exploration will also use the semantic recognition system based on deep learning and neural network for the recognition of oracle bone inscriptions in archaeology. Therefore, combined with the concept of multimedia semantic recognition and analysis, a unified real-time target detection semantic analysis model named You Only Look Once (YOLOv2) is established based on the deep convolutional neural network under deep learning in the field of machine learning, to test the semantic analysis of oracle bone inscriptions. Moreover, Faster Region-Convolutional Neural Network (Faster R-CNN) and traditional YOLO models are selected to conduct the controlled experiments. A YOLOv2 recognition system is established based on Diffusion-Convolutional Neural Networks (DCNN) under deep learning. First, the concept and performance of DCNN are studied. Next, the basic information of oracle bone inscriptions is analyzed. A recognition system based on DCNN is established. On the premise that the three models can identify different directions of the same oracle bone inscriptions sample, the detection accuracy and detection loss value of YOLOv2 are better than those of the other two models, the detection accuracy is as high as 0.90, and the loss value is less than 0.10. Therefore, it is considered that this YOLOv2 semantic analysis model can be applied in oracle bone inscriptions and other archaeological work, which improves the work efficiency and simplifies the human work items for the domestic archaeological work. This semantic analysis model is of great help to the pattern recognition of cultural relics in archaeological work, and can help professionals analyze the meaning of patterns faster when there are massive similar oracle bone inscriptions.

WiFiMap+: High-Level Indoor Semantic Inference With WiFi Human Activity and Environment

Existing indoor semantic recognition schemes are capable of discovering patterns through smartphone sensing; however, there is a lack of a device-free indoor semantic recognition system. In this paper, we propose WiFiMap+, which is a first-ever automatical inference system using WiFi signals to recognize high-level indoor semantics from human activities and environments, where the high-level indoor semantics consist of indoor facilities and environments. To characterize the static indoor environments and dynamical human activities separately with channel state information (CSI), we propose a novel two-stream architecture to generate the spatial streams and the movement streams independently. Compared to the recent research on activity recognition, this two-stream architecture can make the content area of CSI samples extend from human activities to indoor environments. For obtaining accurate indoor environment detection, we propose a CSI-environment model with a spatial stream generation algorithm, which can reduce the effect of human activities on environment detection. For stable activity recognition, we also propose an environment-based testing sample representation method, which can utilize the environment knowledge to overcome the diversity of CSI caused by the environment changes. Finally, we implement WiFiMap+ using commercial WiFi devices and evaluate its performance for seven common semantic detection cases in six-room scenarios. The experimental results show that our proposed WiFiMap+ is robust to the multi-room scenario and can achieve the average accuracy of $\text{92.8}\%$ and the lowest accuracy of about $\text{82}\%$ .

Semantic recognition system

A semantic recognition system of the present invention provides a user interface capable of receiving speech input to a user and an application interface that conveys an input content of the user to an application. The semantic recognition system includes a speech signal input part for receiving input speech signals, a speech recognizer for recognizing a corresponding word based on the input speech signals, a recognized word-semantic number converter including a semantic number-registered word list indicating the correspondence between a semantic number representing a meaning of a word and a registered word belonging to the semantic number, an application interface and an application handling the semantic numbers as data. The corresponding word is recognized by the speech recognizer, based on the speech signals input to the speech signal input part. The recognized word is converted to a corresponding semantic number by the recognized word-semantic number converter. The converted semantic number is notified to the application via the application interface.