4 results about "Word representation" patented technology

Embodiments of the present application provide an information retrieval method and device, electronic equipment and computer readable storage medium, and relate to the field of artificial intelligence. The method comprises: dividing a question to obtain at least one question word, and dividing a target document containing an answer to obtain at least one document word; determining at least one document word representation corresponding to at least one document word based on at least one question word and at least one document word; dividing the target document into at least one short sentence, and determining at least one short sentence representation corresponding to at least one short sentence based on at least one document word representation; and determining at least one target short sentence from at least one short sentence as an answer corresponding to the question based on at least one short sentence representation. The present application solves the problems of incomplete semantics and missing extraction in the prior art when extracting long answers, thereby saving the time cost of user information retrieval and improving the user's retrieval experience.

The application discloses a patent technology prediction system and method fusing a time sequence knowledge graph and contrast learning, relates to the patent technology prediction field, and is proposed in view of the problem of inaccurate technology prediction in the prior art. Patent elements are extracted and standardized; quadruples are constructed and heterogeneous relationships are defined; time slicing is performed and a graph snapshot sequence is generated; structural flow and heterogeneous graph structures are encoded respectively; time flow is encoded; patent semantics are represented and a unified space is aligned; patent representation and real associated technology keyword representation are explicitly semantically aligned based on a contrast learning enhancement mechanism; coarse retrieval results are obtained; fine retrieval results are obtained and rearranged; coarse ranking scores and fine correction scores are fused and output; multi-level ranking targets are jointly designed; a rearranger and a Gold Injection strategy are trained; and an overall loss function and end-to-end training are performed. The application has the advantages of being capable of depicting the dynamic evolution process of patent technology association changing over time, improving the overall quality of prediction results, and the like.

A method may include: receiving a seed topic word distribution; receiving a corpus of documents; generating bag of words representations for the corpus of documents; converting the corpus of documents to vector representations; training a topic modeling system using the seed topic word distribution and concatenated bag of words representations and the vector representations resulting in a topic word distribution and a document word distribution; generating a plurality of new generated topics based on the topic word distribution; precomputing a topic word distribution penalty and a topic word distribution reward for the plurality of topics; penalizing the topic modeling system in response to a divergence and rewarding the topic modeling system in response to a similarity; determining a total loss from a neural network loss, the topic word distribution penalty, and the topic word distribution reward; and training the topic modeling system based on the total loss.

The application provides a high-resolution remote sensing sample labeling method based on a topic model, comprising the following steps: obtaining a training sample set and a sample set to be labeled; extracting traditional features and deep features of the training sample set; performing feature quantization on the traditional features and the deep features of the training sample set to obtain a bag-of-words representation of the training sample set; constructing a visual topic model, inputting the training sample set represented by the bag-of-words into the visual topic model to obtain a topic distribution of the training sample set; constructing and training a weak classifier model by using the topic distribution of the training sample set and labeling information, wherein the weak classifier model comprises at least two weak classifiers; and labeling the sample set to be labeled by using the weak classifier model. The method uses a small amount of labeled training sample set to assist in iteratively training multiple weak classifiers, the accuracy of the weak classifiers is improved, the generalization ability of the finally obtained weak classifier model is strong, and the weak classifier model can accurately classify remote sensing samples and remote sensing images.