39 results about "Lexical semantics" patented technology

A natural language understanding system is described to provide generation of concept codes from free-text medical data. A probabilistic model of lexical semantics, is implemented by means of a Bayesian network, and is used to determine the most probable concept or meaning associated with a sentence or phrase. The inventive method and system includes the steps of checking for synonyms, checking spelling, performing syntactic parsing, transforming text to its "deep" or semantic form, and performing a semantic analysis based on a probabilistic model of lexical semantics.

A lexical semantic structure for modeling semantics of a natural language input on a computer is described. A set of lexical semantic categories is selected to model content of the natural language input. A methodology associates content of the natural language input to one or more categories of the set of lexical semantic categories.

A word semantic embedding apparatus includes: a data storing unit to store a lexical semantic network including word dictionary data and word semantic data; a word list generating unit to extract vocabularies having a predetermined part of speech from the stored word dictionary data and generate a list of words to be learned; a processing data generating unit to bring the generated list of words to be learned and the word semantic data of a word to be learned included in the list of words to be learned from the data storing unit and process the data suitable for word embedding learning to generate processing data; and a word embedding learning unit to learn the word to be learned through the word embedding learning using a learning model formed of an input / output layer and a projection layer with the generated processing data to generate a word vector.

The invention discloses a deep learning text classification method integrating shallow semantic representation vectors. The method includes: first training word embedding vectors on text corpus, and secondly using domain vocabulary dictionary as shallow semantic vocabulary to generate text based on shallow semantic vocabulary A shallow semantic vector representation of each word in the corpus. Next, the weighted splicing of the two word vectors is input into the CNN model as a new word vector for feature extraction and model training to build a text classifier. The invention solves the defect that the big data-driven word vector expression lacks lexical features and knowledge representation, and it is difficult to truly understand the semantic information of the words, so that the model has richer feature expressions and higher classification performance.

The invention discloses a text similarity measuring method based on semantic analysis and a semantic relation network. First, two texts are input and are preprocessed, and a preprocessed result is a collection of vocabularies; second, the lexical semantic similarity of the preprocessed result of the two texts is calculated, and the semantic relation network in constructed according to a calculation result; third, flow medium numerical values of nodes in the semantic relation network are respectively calculated, and characteristic collection of the two texts is obtained; fourth, a bipartite graph is constructed according to the characteristic collection of the two texts, and a path weight number between two parts of the bipartite graph is set; fifth, the similarity between the two texts is calculated by utilizing a bipartite graph optimal matching method. The text similarity measuring method based on the semantic analysis and the semantic relation network can be used for the technical field of data mining and information retrieval technology such as text clustering and information retrieval. Compared with other existing text similarity calculating methods, the accuracy degree of text similarity calculation is greatly improved.

The invention discloses a method and system for mining Chinese event information. The method includes the steps of defining an event sample model based on the mining requirement, instantiating the event sample model to obtain a seed event, taking the seed event as the foundation of an event mined from an original text, conducting preprocessing on the original text to obtain a candidate template set, conducting information labeling on the seed event, processing the seed event on the basis of labeling information to obtain a seed template set, then, processing the candidate template set through an iterative method according to the grade scores of candidate templates and the semantic similarity between the candidate templates and seed templates so that an event mining anchor set can be obtained, and obtaining the event types corresponding to mining event anchors according to the lexical semantic similarity between each event mining anchor in the event mining anchor set and any seed event anchor in the seed event set. The aim of mining the Chinese event information from the original text can be achieved only by labeling a small amount of samples, namely, the seed events, and the mining cost is reduced.

The invention provides a big data processing method based on machine learning, comprising: given a search sentence, using a common stop word list to filter the words in the initial search, and retaining meaningful search terms; using a semantic block model Represent vocabulary with semantic vectors; on the basis of semantic vectors, use cosine similarity for each initial search term to find multiple words with the closest similarity from other vocabulary as extended search terms; use the corresponding The extended search terms in the initial search sentence are replaced, and the newly generated search word sequence is used as the extended search sentence; according to the arrangement and combination of the extended search words, the extended search sentences with different expression forms are obtained. The invention improves the parallel framework of MAPRUDUCE to better meet the needs of text data mining; and aims at the non-standard characteristics of social text, uses semantic vectors to effectively represent and analyze text data, and is suitable for social text mining analysis of various scales and calculate.