30 results about "Measure word" patented technology

A computer system for publishing content includes a categorization member, a rating module, a user profiling assembly and a suggested-reading engine. The categorization member enables an author-user and reader-users of an authored work to categorize content of the authored work using a fixed taxonomy, keywords, tags and/or keyword combinations. The rating module enables reader-users to rate authored works and makes a determination of quality of each authored work in the system. Rating includes reader-user response activity such as number of times the reader recommends, forwards or otherwise effectively promotes the authored work. The user profiling assembly measures words and word combinations used (written or read) by a user and generates therefrom a profile of the user. The suggested-reading engine is responsive to the rating module and provides a ranked list of suggested reading (authored works) for a user. The suggested reading is based on the user's ratings of authored works, respective relevance of authored works to the user and editorial promotion of certain articles/authored works.

The invention discloses an entity relation joint extraction method and device based on a neural network. The method comprises the following steps: firstly, fusing a pre-trained ERNIE word vector, a pre-trained CWV word vector and relative position information of words by utilizing a single-layer Transform network; secondly, improving a handshake labeling strategy, and introducing a vectorized entity type label to make full use of entity type information; then, obtaining candidate entity relationship triples in the sentences through an annotation decoding method; and finally, indexing articles, sentences, entities and relationships by using UCL according to the characteristics that the UCL national standard can efficiently organize contents and effectively associate information. The entity relationship triple can be directly extracted from the sentence, the problems of redundant entities, nested entities, overlapping relationships and the like are avoided, a small amount of annotation data can be used for quickly checking the entity relationship, and the data can be objectively and normatively indexed.

The invention relates to the field of artificial intelligence, in particular to a semantic matching method and device based on knowledge distillation, computer equipment and a storage medium. The method comprises the steps: obtaining a to-be-processed text and a standard text; respectively processing the to-be-processed text and the standard text to obtain corresponding character vectors, word vectors and character and word combination vectors; inputting the character vectors, the word vectors and the word combination vectors corresponding to the to-be-processed text and the standard text intoa pre-trained target semantic matching model, so as to calculate the target similarity of the to-be-processed text and the standard text through the target semantic matching model, wherein the targetsemantic matching model is obtained by training according to a twin network mode of knowledge distillation; and outputting a standard text corresponding to the to-be-processed text according to the target similarity. In addition, the invention also relates to the blockchain technology, and the standard text and the target semantic matching model can be stored in the blockchain. By adopting the method, the matching efficiency can be improved.

The invention belongs to the technical field of data processing, and discloses a processing system and method for extracting fine-grained typical opinion data of a user, which are used for cleaning the data and filtering out noise data. Performing character segmentation and word segmentation on the cleaned data; a word embedding model is used for training word vectors, word vector representation is carried out, and a corresponding file is generated; defining common attributes of the product; extracting attributes related to the product in the user comments; converting all the extracted attribute texts into vector representation by adopting a word vector weighted averaging method; clustering the texts; and obtaining user typical opinions of different attributes of the product. According tothe method, the attribute texts related to the product in the user comments are extracted, the texts with the same attribute and the same emotional tendency are clustered into the same cluster as muchas possible, and the user typical opinions with different attributes are obtained. The accuracy of the clustering result is effectively improved, the granularity of the clustering result is smaller,and the typical opinions of the user for different attributes of the product are quickly obtained.

The invention relates to the field of artificial intelligence, in particular to a semantic matching method and device based on a multi-task twin network, equipment and a medium. The method comprises the steps of obtaining a to-be-processed text and a standard text; inputting the to-be-processed text into a first feature extractor to obtain a word quantity, a word vector and a word combination vector corresponding to the to-be-processed text, and inputting the standard text into a second feature extractor to obtain a word vector, a word vector and a word combination vector corresponding to thestandard text, wherein the first feature extractor and the second feature extractor are twin networks; inputting the obtained character vector, word vector and word combination vector into a pre-trained target semantic matching model to obtain target similarity between the to-be-processed text and the standard text; and outputting a standard text corresponding to the to-be-processed text accordingto the target similarity. In addition, the invention also relates to the blockchain technology, and the standard text and the target semantic matching model can be stored in the blockchain. The method can improve the accuracy.

The embodiment of the invention discloses a semantic recognition method and device combined with knowledge graph entity information and related equipment. The method comprises the following steps: calculating a graph hidden state vector of each training entity in a training corpus, and an adjacent semantic vector, a word embedding vector and a position vector of each word, and combining the graph hidden state vector, the adjacent semantic vector, the word embedding vector and the position vector to obtain an input vector of each word; through predicting the input vector by an encoder of the Transform model, and obtaining the prediction probability of the semantics to which the input vector belongs; according to the probability between the prediction probability and the real semantics to which the input vector belongs, optimizing model parameters of the encoder to obtain a semantic recognition model; and finally, performing semantic recognition through the semantic recognition model. According to the method, the entity features obtained by the semantic recognition model are more comprehensive, so that the semantic recognition accuracy of the semantic recognition model is improved.

The invention provides an input method and an input system for providing number selection for ordinal words, belonging to the field of input methods. On the basis of the traditional input method, the input system is additionally provided with a word bank containing the ordinal words and measure words and a determination unit for determining whether sequentially continuous ordinal words and measure words exist among characters input by a user or not according to the word bank. The input method comprises the following steps of: firstly, generating the word bank containing the ordinal words and the measure words; acquiring the characters input by the user by utilizing an input interface; determining whether sequentially continuous ordinal words and measure words exist among the characters input by the user or not according to the word bank; and adding the characters in candidate words corresponding to the characters input by the user according to an acquired determined result under the condition that the sequentially continuous ordinal words and measure words exist among the characters input by the user, thereby generating a candidate number and word list. By setting of the word bank containing the ordinal words and the measure words, the numbers and words input by the user and sequencing codes can be effectively identified, and thus an output result is enhanced when the ordinal words and the measure words are in continuation.

The occurrence cost of unknown words that are not registered in a morphological analysis dictionary is calculated by applying an occurrence cost regression model, which is a learning model. An information processing device includes a notation feature amount extraction unit that extracts a notation feature amount of a character string, a part-of-speech feature amount extraction unit that extracts a part-of-speech feature amount of the character string, and an occurrence cost estimation unit that receives the notation feature amount and the part-of-speech feature amount and calculates an occurrence cost of the character string by applying an occurrence cost regression model. The occurrence cost regression model is a learning model that estimates the occurrence cost from the notation feature amount and the part-of speech feature amount of a character string, generated by a learning process using registration data of an existing morphological analysis dictionary as teacher data.

The invention relates to a named entity recognition model training method, a named entity recognition model application method and a named entity recognition model training system, and belongs to the field of rail transit natural language processing, and the model training method comprises the following steps: preprocessing a fault text to obtain a word vector and a word vector; wherein the character vectors comprise the character vector of the named entity recognition task and the character vector of the word segmentation task; wherein the word vectors are used for judging whether two continuous word vectors in one sentence are associated into the same word or not; establishing a named entity recognition model; wherein the named entity recognition model comprises a named entity recognition task sub-model, a word segmentation task sub-model and an adversarial training structure; and alternately inputting the character vector of the named entity recognition task and the character vector of the word segmentation task into the adversarial training structure of the named entity recognition model for training to obtain a trained named entity recognition model. When the named entity recognition model is used for recognizing the category of the named entity, the recognition precision and the recognition effect are very high.

The invention discloses a 95598 customer appeal processing method and device and a storage medium. The processing method comprises the following steps: S101, data word segmentation: preprocessing voice translation text data of a customer, performing sentence segmentation on the data, and performing word segmentation on words of each sentence to obtain result data after word segmentation; s102, performing data vectorization, and generating word vectors by result data after word segmentation; s103, word vector processing: inputting the generated word vector into a customer appeal recognition algorithm model, extracting the core semantics of the text through a neural network structure, capturing the semantics of different words/words in the whole sentence meaning in the whole sentence meaning, and recognizing the appeal classification of the voice translation text data of the customer according to the semantics; and S104, outputting a result, and sending the result of the appeal classification to a corresponding processing end. According to the method, extraction of text semantics and generation of features can be realized under relatively low computing resources, and then different text semantic features are compared, so that the purpose of judging text contents is achieved.

The invention provides an extraction method and a retrieval method of customs data product words. The extraction method comprises the following steps: firstly, cleaning up redundant parts in a customsdescription text, and converting the redundant parts into a better processing form; then, heuristically finding out segmentation words in the customs description text, and segmenting product words and description parts; replacing quantifiers and date regularities in the text with space characters or deleting the quantifiers and date regularities; deleting a description part in the text through agrammatical rule, or extracting product word groups from the data by using mutual information and left and right information entropies to obtain product word groups of which the number of words is less than or equal to 5, and adding the product word groups into a lexicon. The retrieval method comprises the steps that firstly, word segmentation is conducted on a text to be retrieved, and then retrieval is conducted in a constructed word bank through a bit map or hash map structure. According to the method, the grammatical structure, the mutual information, the character information and the specific structure information of the customs data are combined, the advantages of various information can be fully combined, and the product words can be accurately extracted and retrieved.

The invention discloses a text sentiment analysis method based on word vector deformation and bidirectional bit order convolution, which comprises the following steps: obtaining an evaluation statement, inputting the evaluation statement into a trained bidirectional standard convolution network model with bit order information, and outputting a text sentiment analysis result through the model; wherein the bidirectional standard convolutional network model with bit sequence information comprises a word embedding layer, a bit sequence information layer, a word vector deformation layer, a convolutional layer, a double-end structure and a classification layer; the word embedding layer is used for converting words in an evaluation statement into word vectors which can be understood by a computer; the bit sequence information layer is used for adding bit sequence information to the word vector to obtain the word vector with the bit sequence information; the word vector deformation layer is used for deforming word vectors into word block matrixes and finally splicing the word block matrixes into sentence matrixes; the convolution layer is used for performing convolution operation on the sentence matrix to obtain word vector features; and the classification layer is used for carrying out classification operation on the spliced word vector features to obtain a final text sentiment analysis result.