4results about How to "Rich semantic features" patented technology

The application discloses a text reading comprehension method and device based on article difference perception representation, a storage medium and an electronic equipment, belongs to the field of natural language processing and artificial intelligence, and aims to solve the technical problems of how to effectively utilize article information to improve the accuracy of answer selection and how to realize effective matching between a question and options, thereby improving the prediction accuracy of a text reading comprehension system, and adopts the technical scheme that: ① a text reading comprehension method based on article difference perception representation comprises the following modules: a pre-training embedding representation module, a feature filtering module, an article difference perception representation interaction module and a label prediction module. ② a text reading comprehension device based on article difference perception representation comprises a text reading comprehension data set acquisition unit, a text reading comprehension model construction unit and a text reading comprehension model training unit.

The application discloses a kind of aspect-level sentiment analysis method based on multilevel knowledge enhancement, belong to sentiment analysis field. Including the following steps: S1: text word vector is obtained using GloVe word embedding tool, the syntax dependency tree of sentence is constructed using Stanza, and the dependency graph is constructed accordingly;S2: the context representation of sentence is extracted by inputting word vector into BiLSTM, and the dependency graph is updated using sentiment dictionary and sensitive relationship set, to realize the sentiment and syntax enhancement of sentence;S3: the enhanced dependency graph is input into GCN to model node features, to obtain specific aspect representation;S4: aspect word is enhanced using concept atlas to obtain aspect word representation, which is fused with specific aspect representation to obtain aspect representation;S5: aspect representation and context representation are coordinated and optimized using interactive attention, to obtain the final representation of sentence, so as to determine aspect sentiment tendency.The application can effectively improve the accuracy of specific aspect sentiment classification, and help merchants accurately locate problems in products or services.

The application discloses a sentence pair semantic matching method and system, belongs to the natural language processing technical field and the computer artificial intelligence field, and aims to solve the technical problems of how to construct and utilize context information of a sentence pair to strengthen a sentence pair interaction process and how to realize direct interaction between the context information and the sentence pair, thereby improving the accuracy of sentence pair semantic matching. The method specifically comprises the following steps: obtaining a sentence pair semantic matching data set; downloading a publicly disclosed sentence pair semantic matching data set from a network; constructing a sentence pair semantic matching model based on a Bilinear Triple-Attention mechanism; and training the sentence pair semantic matching model on a sentence pair semantic matching training data set. The system comprises a data set obtaining unit, a model constructing unit and a model training unit.

The application discloses a three-dimensional feature representation-based intelligent text reading comprehension method and device, a storage medium and an electronic device, and belongs to the fields of natural language processing and artificial intelligence; the technical problem to be solved by the application is how to capture direct interaction features between three-sequence data and how to enhance sufficient interaction among an article, a question and options, so as to improve the prediction accuracy of an intelligent text reading comprehension system; the technical scheme adopted is as follows: ① a three-dimensional feature representation-based intelligent text reading comprehension method, comprising the following modules: a pre-training embedding representation module, a feature filtering module, a 3D CNN interaction feature module and a label prediction module; ② a three-dimensional feature representation-based intelligent text reading comprehension device, comprising: an intelligent text reading comprehension data set acquisition unit, an intelligent text reading comprehension model construction unit and an intelligent text reading comprehension model training unit.