3 results about "Adjective" patented technology

The invention relates to a method for analyzing a language structure by using a language expression, which is suitable for analyzing structures of more than 7,000 languages on the earth. The method comprises the following steps of: analyzing a morphologic element and a syntactic structure of a target sentence by a morphologic element and syntactic analysis unit; and based on a pre-training language expression learning algorithm provided by a language expression learning algorithm unit, a language expression calculation unit generates a language expression for the morphologies and syntactic structures of the sentences. Wherein the language expression calculation unit is used for dividing a sentence into an upper left area, a lower left area, an upper right area and a lower right area by taking a sentence center symbol as a benchmark, a subject symbol is distributed in the upper left area, a core verb symbol is distributed in the lower left area, and a verb type symbol is distributed in the lower right area; non-predicate symbols representing remaining phrases other than subjects and verbs are assigned to the right upper region. Furthermore, symbolization specific information including marks (1-4) indicating a person and a character number mark (.) indicating the number of word characters such as nouns and adjectives is added to at least one of the left upper mark, the left lower mark, the right upper mark and the right lower mark of each symbol. By means of the method, the sentences in the specific language can be accurately analyzed into structured syntactic expressions, and therefore the efficiency of language processing such as sentence recognition, semantic understanding, translation and retrieval is improved.

The application discloses a kind of based on template sentence enhancement's paraphrase sentence generation method, steps are as follows: step 1: obtain original sentence, and obtain the target paraphrase sentence of original sentence in parallel corpus, use the method of rule to retrieve 1 sentence from parallel predata with the highest similarity with target paraphrase sentence as optimal sentence as reference;Step 2: different part-of-speech special symbols are used to replace noun, verb, adjective and adverb in optimal sentence to construct template sentence;Step 3: original sentence and template sentence are spliced through special symbol and joint, and the spliced sentence is used as the input of paraphrase generation model, and the paraphrase generation model outputs several paraphrase sentences;Step 4: the similarity of several paraphrase sentences and original sentence in grammatical structure and semantics is calculated, and the highest similarity paraphrase sentence is used as the optimal paraphrase sentence of original sentence.The application avoids the information dependence of model training phase in template sentence by masking the words of related part-of-speech in template sentence.

This application relates to the field of natural language processing and provides a method and apparatus for clustering fault work orders. The method includes: obtaining N adjectives from the fault description text of at least one fault work order in a target system; arranging and combining M entity names and N adjectives in the target system to obtain M*N keyword groups; determining the fault work order vector of a single fault work order based on the word vectors of the words to be processed in the single fault work order; sequentially matching the fault description text of the single fault work order with the M*N keyword groups, and obtaining keyword groups to be clustered based on the successful matching; clustering the word vectors of the keyword groups to be clustered in each fault work order to obtain X classes of keyword vectors; and matching the keyword vectors with the fault work order vectors in each of the X classes, clustering the successfully matched fault work order vectors. The fault work order clustering method provided in this application can improve the accuracy of fault classification, thereby improving the accuracy of fault work order clustering.