The invention provides a method for selecting positive sample data in source domain data to extend training data of a target domain by fusing semantic difference and label difference of sentences in the source domain and the target domain, so as to achieve the purpose of enhancing named entity recognition performance of the target domain Based on a conventional Bi-LSTM+CRF model, in order to fusesemantic differences and label differences of sentences in a source domain and a target domain, semantic difference and label difference are introduced through state representation and reward settingin reinforcement learning; therefore, the trained decision network can select sentences having positive influence on the named entity recognition performance of the target domain in the data of the source domain, expand the training data of the target domain, solve the problem of insufficient training data of the target domain, and improve the named entity recognition performance of the target domain at the same time

Semantic and label difference fused named entity identification field self-adaption method

The invention discloses an adaptive decision tree fall detection method and system, and belongs to the technical field of human body behavior recognition and judgment. The method comprises the following steps: 1, acquiring three-axis acceleration and three-axis angular velocity data of falling and non-falling actions of a human body, and performing screening; 2, calculating resultant accelerationand resultant angular acceleration, dividing a training set, substituting a test set and a verification set into a TSFRESH library to calculate features, and screening and deleting useless features; 3, selecting preliminary important features by using a random forest sieve; 4, establishing a decision tree model for training and verification, and testing a result; 5, continuing to obtain a new sample, repeating the steps 2 to 4, and updating the decision tree model. According to the invention, an accurate tumble judgment result can be obtained through a decision tree algorithm with a small calculation amount; after a certain number of samples are collected, the decision tree model is updated, so the judgment precision of the algorithm can be further improved.

Adaptive decision tree fall detection method and system

The invention relates to a text classification method and system, electronic equipment and a computer readable storage medium, and the method comprises the steps: adding an input layer of a BERT pre-training model, enabling the BERT pre-training model to participate in the training, and carrying out the classification and recognition of a to-be-classified text based on a classification model obtained after training, wherein the added input layer is a feature representation layer for assisting classification and recognition. By adding the input layer of the BERT pre-training model, the reference characteristic quantity during model text classification and recognition is increased, so that the text classification accuracy can be improved. Besides, the structure of the BERT pre-training modelis not changed, so that the BERT model does not need to be improved by a Google company and the like requesting to provide the BERT pre-training model, namely, the method is not limited by a basic model providing company, and the problem of inconvenience does not exist.

Text classification method and system, electronic equipment and computer readable storage medium

The invention relates to the technical field of data processing, and relates to an epidemic situation label data processing method and system. The method comprises the steps of: extracting tracking behavior characteristics of a tracking information sequence recorded by each user terminal; screening out a first tracking behavior characteristic of a epidemic situation related user and a second tracking behavior characteristic of a to-be-tested user; comparing the second tracking behavior characteristics of the to-be-tested user with the first tracking behavior characteristics of each epidemic situation related user; and determining characteristic attributes of the to-be-tested user under the different epidemic situation influence type labels, thereby sending corresponding epidemic situationlabel information to a user terminal of the to-be-tested user according to the characteristic attributes of the to-be-tested user under the different epidemic situation influence type labels for prompting. According to the invention, differentiated epidemic situation label analysis can be realized for each individual in a crowd, so that rapid and accurate prompt that the infectious disease epidemic situation may be affected is realized, and compared with a traditional scheme, the method and the system can effectively provide the accuracy degree of epidemic situation influence prompt.

Epidemic situation label data processing method and system

The invention discloses a traditional Chinese medicine diagnosis and treatment knowledge graph automatic construction method based on deep learning. The traditional Chinese medicine diagnosis and treatment knowledge graph automatic construction method comprises the steps: constructing an initialized literature medical record corpus, carrying out sentence segmentation and word segmentation on a medical record, and marking a theory-law-prescription-medicine entity in the medical record; predicting the entity through a bidirectional LSTM, and automatically extracting the entity from traditional Chinese medicine literature medical records through a deep learning model; and clustering similar entities appearing in the same medical record to form an entity group, then forming a triple accordingto a predefined relationship between entities, and constructing a knowledge graph. According to the invention, the relationship between traditional Chinese medicine diagnosis and treatment concepts ispredefined; construction of the knowledge graph is converted into a traditional Chinese medicine diagnosis and treatment named entity recognition task; and entities are automatically extracted from traditional Chinese medicine literature medical records through a deep learning model, and the entities are clustered to form an entity set, so that the many-to-many problem between traditional Chinesemedicine diagnosis and treatment concepts is solved, and the famous and old traditional Chinese medicine diagnosis and treatment thought in the medical records is completely displayed.

Traditional Chinese medicine diagnosis and treatment knowledge graph automatic construction method based on deep learning

The invention discloses a Chinese word vector generation method based on similar contexts and reinforcement learning. The problems that an existing Chinese word vector generation method carries out prediction on the basis of the relation between adjacent contexts of target words and the target words, the situation that although some words in Chinese are adjacent, semantics are irrelevant is not considered, and the expression quality of word vectors is not high are solved. The method comprises the following steps of: selecting a corpus, and preprocessing the corpus so as to construct a Chinesecorpus; carrying out similar context discovery on the Chinese target words to obtain similar contexts related to semantics of the Chinese target words; and constructing a Chinese word vector reinforcement learning framework, and performing reinforcement learning to obtain word vector representation of the Chinese target word. According to the method, the problem that adjacent Chinese words are irrelevant can be solved, and high-quality Chinese word vectors are generated.

Chinese word vector generation method based on similar contexts and reinforcement learning

The invention discloses a traditional Chinese medicine adverse effect identification method based on a deep belief network. According to the method, the traditional Chinese medicine adverse effects can be effectively collected and analyzed, and construction of a traditional Chinese medicine adverse effect entity database is facilitated. Traditional Chinese medicine toxic and side effect research and an artificial intelligence method are organically unified, full-automatic extraction of entities with adverse effects of the medicines is achieved, time and economic losses of manual extraction of adverse effects of the medicines are reduced, and optimization of traditional Chinese medicine entity identification research methodology is achieved. Later, adverse effect identification and relationship mining for specific traditional Chinese medicines can be realized according to different traditional Chinese medicine literature sets; the method can be effectively applied to various traditional Chinese medicine literatures, a deep belief network model does not need to be retrained, and adverse effect entities of the medicines can be directly identified and extracted from input traditional Chinese medicine literatures.

Traditional Chinese medicine adverse effect identification method based on deep belief network

The invention discloses a traditional Chinese medicine syndrome differentiation auxiliary decision method based on a point sequence convolutional neural network (PCNN) and an attention mechanism. Themethod comprises the steps of performing word segmentation processing on each medical record by means of a language technical platform tool; performing characteristic coding on each medical record bymeans of the PCNN network, constructing corresponding characteristic vector expression; and performing symptom predicting by means of the attention mechanism. According to the method, the characteristics such as symptom, disease cause and disease mechanism are automatically extracted by means of the point sequencing CNN network, constructing a corresponding characteristic code without artificial characteristic marking; and introducing the attention mechanism for eliminating noise corpus, distributing different weights to the medical records, and improving classification precision. According tothe method of the invention, syndrome differentiation rule mining is performed in multiple aspects of symptom, disease cause and disease mechanism of the medical record data, thereby improving reliability of a syndrome differentiation result. Furthermore the method of the invention is suitable for syndrome differentiation rule discovery of a majority of diseases and has high extendability.

Traditional Chinese medicine syndrome differentiation auxiliary decision method based on PCNN and attention mechanism

The invention discloses an emotion recognition method based on a space-time convolution kernel block, and is applied to the fields of human-computer interaction, distance education, medical care and the like. Aiming at problems existing in the prior art, the time dimension and space dimension feature information of an EEG signal is fully considered, the accuracy of emotion recognition is enhancedby fusing time dependence and spatial dependence between different electrode positions; specifically, firstly, a DEAP emotion public data set is obtained, data preprocessing is achieved, denoising operation is completed by adopting a low-pass filter, data are input into an STCB_CNN model to be trained, and the trained model is used for emotion recognition; and the method provided by the inventioncan achieve higher recognition precision.

Emotion recognition method based on space-time convolution kernel block

The invention provides a traditional Chinese medicine literature intelligent mining and formulating assistant decision making method and system and belongs to the technical fields of data mining, machine learning and traditional Chinese medicine diagnosis assistant information technologies. According to the method and system of the invention, on the basis of historical classic traditional Chinesemedical literature, and existing real medical cases, prescriptions can be automatically formulated according to the symptoms and diagnosis syndromes of patients; an intelligent formulating process isconsistent with an actual traditional Chinese medicine formulating process; and components are added to and subtracted from classic prescriptions, so that prescriptions can be formulated. The system includes a symptom addition and subtraction determination module, a medicine addition and subtraction determination module, and a prescription determination module. With the method and system adopted,new ideas can be provided for doctors in formulating prescriptions; assistant decision making can be provided for the doctors; the clinic consultation time of the doctors can be shortened; and the doctors can diagnose more patients.

Li Qiaoqin

Papers

Chinese word vector generation method based on similar contexts and reinforcement learning

Traditional Chinese medicine adverse effect identification method based on deep belief network

Traditional Chinese medicine syndrome differentiation auxiliary decision method based on PCNN and attention mechanism

Emotion recognition method based on space-time convolution kernel block

Traditional Chinese medicine literature intelligent mining and formulating assistant decision making method and system