730 results about "Relationship extraction" patented technology

A messenger-linked service system and method using a social graph based on a human relationship of a messenger are provided. The messenger-linked service system may include a relationship extraction unit to extract a social graph of a friend relationship of the messenger, a selection unit to select data in the messenger-linked service, and an execution unit to either transmit or to execute a sharing request of the selected data to the friend using the social graph.

It is an object to highly accurately perform semantic relationship extraction from text data by performing supervised learning of multiple classes using an existing thesaurus as a correct answer. Concerning any pair of words in a text, a plurality of kinds of similarities are calculated and a feature vector including the similarities as elements is generated. A label indicating a classification of a semantic relationship is given to pairs of words on the basis of the thesaurus. Data for semantic relationship identification is learned as an identification problem of multiple classes from the feature vector and the label. Identification of an inter-semantic relationship of two words is performed according to the data for semantic relationship identification.

The invention belongs to the technical field of computer software, and discloses a method and a system for automatically constructing knowledge maps for mass unstructured texts. The method comprises the steps of: abstracting a named entity recognition problem into a sequence labeling problem by giving a sentence and labeling each word in the sequence of sentences; designing effective features according to the training data, learning various classification models, and using trained classifiers to predict relationships; linking multiple existing knowledge to create a large-scale and unified knowledge network from the top; and capturing and integrating entity information from three online encyclopedias, open websites, related knowledge bases, or search engine logs. According to the method andthe system for automatically constructing knowledge maps for mass unstructured texts, the construction speed of the knowledge maps can be greatly improved, the time efficiency is improved, and the human resource cost is reduced by more than 30%. In addition, the method and the system have better domain portability, and the construction of the knowledge map can be quickly implemented by only optimizing the entities and relationship extraction algorithms in the invention.

A system for extracting information from text, the system including parsing functionality operative to parse a text using a grammar, the parsing functionality including named entity recognition functionality operative to recognize named entities and recognition probabilities associated therewith and relationship extraction functionality operative to utilize the named entities and the probabilities to determine relationships between the named entities, and storage functionality operative to store outputs of the parsing functionality in a database.

The invention relates to an entity relationship joint extraction method and system based on an attention mechanism. The method comprises the following steps of: converting an entity marked in trainingdata and a triple of a relationship into a form that each word corresponds to a predefined type of tag; Mapping each word in the sentences of the training data into a corresponding word vector, inputting the word vectors into a neural network model based on an attention mechanism, and performing training through a back propagation algorithm to obtain a label prediction model; And inputting the sentences needing to be subjected to entity relationship extraction into the trained label prediction model, predicting a label corresponding to each word, and obtaining entity relationship triples existing in the sentences according to the corresponding relationship between the labels and the words in the triples. The system comprises a preprocessing module, a model training module and a result processing module. According to the method, by more effectively utilizing the key information in the sentences, the joint extraction performance of the relational entities is improved, and the method hasgood practicability.

The invention relates to a dependency semantic-based Chinese unsupervised open entity relationship extraction method. The method comprises the following steps of preprocessing an input text: performing Chinese word segmentation, part-of-speech tagging and dependency grammar analysis on the input text; performing named entity identification on the input text; arbitrarily selecting two entities from identified entities to form candidate entity pairs; searching for a dependency path between two entities in the candidate entity pairs; and analyzing whether a syntactic structure mapped by the dependency path is matched with a normal form of a dependency semantic normal form set or not, if yes, extracting words or phrases from the residual part of the input text according to the matched normal form to serve as relational words, forming a relational triple by the extracted relational words and the candidate entity pairs, and if not, performing normal form matching of a next group of the candidate entity pairs; and outputting the relational triple. Compared with the prior art, the method has the advantages that the calculation complexity is low; the extraction efficiency is high; distance position limitation is overcome; a simple sentence also can be extracted and the like.

The invention provides a building method used for a knowledge obtaining model in a knowledge graph. The method comprises the steps of constructing a first training set consisting of multiple text sentences as input data and a relationship between any two entities in each sentence in the knowledge graph, as a classification result, and training a first neural network; constructing a second trainingset consisting of triples in multiple knowledge graphs, and training a second neural network; by taking input data vectors obtained in the second neural network as attention features of the first neural network, building a relationship extraction model; by taking input data vectors obtained in the first neural network as attention features of the second neural network, building a knowledge representation model; and fusing the relationship extraction model and the knowledge representation model to obtain the knowledge obtaining model in the knowledge graph. According to the method provided bythe invention, the two task models of knowledge representation and relationship extraction are integrated at the same time, and the features of the knowledge graph and free texts can be comprehensively extracted, so that the model stability and accuracy are improved.

Presenting event-specific video content that conforms to a user selection of an event type includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of at least one event within the at least one video feed to determine at least one event type, wherein the at least one event type includes an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed, extracting the video content displaying the at least one event and associating the understanding with the video content in a video content data structure. A user interface is configured to permit a user to indicate a preference for at least one event type that is used to retrieve and provide corresponding extracted video content with the data structure in a new video feed.

The invention discloses an event atlas construction system and method based on multi-dimensional feature fusion and dependency syntax. The event graph construction method based on multi-dimensional feature fusion and dependency syntax is realized through joint learning of event extraction, event correction and alignment based on multi-dimensional feature fusion, relationship extraction based on enhanced structured events, causal relationship extraction based on dependency syntax and graph attention network and an event graph generation module. According to the event graph construction method and device, the event graph is constructed through the quintuple information of the enhanced structured events and the relations between the events in four dimensions, and the defects that in the priorart, event representation is simple and depends on an NLP tool, the event relation is single, and the influence of the relations between the events on event graph construction is not considered at the same time are overcome. According to the event atlas construction method provided by the invention, the relationships among the events in four dimensions can be randomly combined according to different downstream tasks, and the structural characteristics of the event atlas are learned to be associated with potential knowledge, so that downstream application is assisted.

The invention discloses a construction system and method of a knowledge map, and belongs to the technical fields of natural language processing (NLP) and computer information processing. The system includes: a crawler module, which carries out crawling and data cleaning on text; a basic labeling module, which is used for carrying out basic labeling work including subject completion operations; a candidate relationship extraction module, which is used for extracting candidate relationships including candidate relationship sentences and / or relationship entity pairs, a feature extraction module,which is used for carrying out feature extraction; a relationship classifier training module, which is used for carrying out model training according to candidate relationship extraction results and feature extraction results to construct a relationship classifier; and a relationship audit module, which is used for carrying out audit determination on candidate sentence relationships obtained by the relationship classifier, and accordingly adjusting the relationship classifier according to results of audit determination. The system realizes higher relationship extraction capability, reduces costs of manual participation, and improves efficiency of constructing the knowledge map.

Open Chinese entity relationship extraction refers to, on the premise of not limiting a corpus field and a relationship category, automatic extraction of relationship information between entities from a Chinese corpus to obtain an entity relationship tuple. The present invention discloses a pattern self-learning based Chinese opening relationship extraction method. The method comprises the following three main steps of: firstly, based on an existing knowledge library, acquiring a high-quality entity relationship tuple and a corresponding sentence as a training corpus, and obtaining a dependent path mode between an entity and a relationship word by a pattern learning method proposed by the present invention; secondly, performing pre-processing of word segmentation, part-of-speech tagging, dependency analysis and the like on a to-be-extracted text, and performing entity relationship extraction by means of a relationship mode obtained by previous learning; and finally, performing quality evaluation on an entity relationship extracted automatically from the Chinese corpus by using a machine learning method, and obtaining the high-quality entity relationship tuple.

The invention discloses an enterprise relationship extraction method, a device and a storage medium. The method includes: extracting enterprise entity pair sentences, of which relationships exist, touse the same as training sample sentences to establish a sample library; extracting all training sample sentences, which contain one enterprise entity pair, from the sample library, carrying out wordsegmentation, mapping each word to a word vector x, and mapping each training sample sentence to a sentence vector S; using LSTM (Long Short-Term Memory) to calculate a first hidden-layer statevector h and a second hidden-layer state vector h' of the word vector x, obtaining a comprehensive hidden-layer state vector by splicing, and then obtaining a feature vector T; substituting the feature vector T into an average-vector expression to calculate an average vector S; substituting the average vector S and relationship types of the enterprise entity pair into a softmax classification function to calculate a weight a of each training sample sentence; and extracting sentences containing two enterprise entities, obtaining a feature vector T through bi-LSTM (Bidirectional Long Short-term Memory), and inputting the same into a trained RNN model to predict a relationship of the two enterprises. Labor costs are reduced, and the relationship between the two enterpriseentities is more accurately predicted.

The invention relates to a character relationship graph construction method based on integration of an ontology and multiple neural networks. The method comprises the following steps: crawling data related to a character in a certain domain in the Internet; establishing a domain character ontology; extracting data from a structured data table which contains multiple types of entities and has repeated entities to construct a standardized entity table; matching the two class names of the character ontology model with the two entity table names through a semantic mapping algorithm, automaticallyobtaining all entity relationships, and storing the entity relationships in a Neo4j database in a graph structure; for the text data in the structured table, carrying out character entity recognitionand relationship extraction by using a sliding window, entity position characteristics and a bidirectional gating recurrent neural network; and updating the current graph structure of the newly addedrelationship to form a domain character relationship knowledge graph. The character relationship advanced features can be extracted from the original relational data and the text data, manual design is not needed, the recognition effect is improved, and the efficiency of constructing the character relationship graph by the complex webpage text is improved.

The invention discloses a text entity relationship extraction method and a model training method, and can be applied to a natural language processing technology in the field of artificial intelligence; according to the invention, the graph state recurrent neural network and the BERT model are combined, a first vector used for representing semantic features of the text and a second vector used forrepresenting dependency relationship features of the text are extracted from the text; the first vector and the second vector are spliced and then classified; the relationship extraction of the entitypairs is enabled to obtain relatively high accuracy in long sentence and cross-sentence application scenes; the problem of insufficient accuracy in application scenes such as long sentences and crosssentences in the prior art is solved, in addition, in the model training stage, based on the preset rules and the pre-training model, a large amount of annotation data is produced in a remote supervision mode, and a large amount of accurate training data can be obtained at a low cost. Therefore, the method can be widely applied to the natural language processing technology.

The invention discloses an entity relationship joint extraction method. The method comprises the steps of performing data preprocessing on an input sentence; mapping each word in the input sentence into a corresponding word vector; inputting the obtained word vectors into an entity relationship joint extraction model based on a long and short-term memory network and a graph convolutional neural network for training; and adopting the trained LSTM-GCN model to carry out entity extraction and relationship extraction. According to the method, the sequence information and the region information ofthe input sentence can be captured at the same time through the LSTM and the GCN, each word can be better expressed, the performance of entity extraction and relationship extraction is improved, and the method has certain practicability.

The invention provides a text entity relationship extraction method and device, electronic equipment and a storage medium. The method comprises the steps of performing recognition processing on an input text to obtain an entity in the input text and a category to which the entity belongs; traversing the entities based on category constraint conditions so as to construct candidate entity pairs based on the candidate entities meeting the category constraint conditions; according to the category to which the entity in each candidate entity pair belongs, performing tagging processing on the constructed candidate entity pair; on the basis of the candidate entity pairs subjected to tagging processing, replacing entities identified in the input text with tags to obtain a new sample; and carryingout classification processing on the obtained new sample through a classification model to obtain a relationship of the constructed candidate entity pair, and outputting a triple formed by the candidate entity pair and the relationship. The entity relationship extraction efficiency and effect of the text can be improved.

The invention discloses a knowledge network-based text indexing system and method. The text indexing system comprises a single text feature extraction unit, a multi-text word relation extraction unit, a knowledge tree generating unit, a knowledge tree application unit and a knowledge base storage unit. The text indexing method comprises the following steps of: partitioning words in a text input to the text indexing system, and acquiring text feature words in the text; deducing a class word TAG corresponding to the text according to node positions of a knowledge tree corresponding to the text feature words; and judging the validity of the TAG through a judgment type model based on the TAG, then extracting a reliable TAG word set, and repositioning a text feature word set through the reliable TAG word set to form a reliable text feature word set. According to the system and the method, content word extraction, class labeling and phrase extraction are integrated, so that the extraction effects can be mutually promoted; and the semantics of the words are expressed through the nodes of the knowledge network, so that different meanings are reduced.

The invention discloses a medical entity relationship extraction method based on feature fusion, and the method comprises the steps: enabling entities in a knowledge base to be aligned to medical corpora through a remote supervision and rule combination method, and constructing an entity pair sentence set; performing word-level vector coding on the sentences based on a convolutional neural networkmodel to obtain overall feature vector representation of the sentences; extracting features in left and right subtree directions on the shortest dependency path of the sentences by using a recurrentneural network respectively, and performing splicing operation; and fusing the sentence overall features and the dependency syntax features which are extracted from the two parts respectively, and performing final relation extraction on the obtained fusion features. According to the method, on the premise that a dependency syntax structure is utilized; entity type characteristics capable of expressing entity relationship types among entities are introduced; the position features and the overall features of the sentences are integrated with the dependency syntactic features, the semantic relationship between the sentences is better learned, the interference of noise data on medical entity relationship extraction is reduced, and the accuracy of medical entity relationship extraction can be improved to a certain extent.

The invention relates to an extraction system of affective characteristic words. The extraction system is characterized by comprising a characteristic selecting module, a characteristic verification module, a relation extraction module, a generalized affective characteristic word list and a narrow affective characteristic word list; the characteristic selecting module utilizes article content in an article set pointed by comment and comment content in a comment set to respectively extract all candidate affective characteristic words and sorted candidate affective characteristic words in the comment content; the relation extraction module constructs a sense relation diagram between words by a template according to the article content, establishes the generalized affective characteristic word list by the all candidate affective characteristic words and the sense relation diagram; and establishes the narrow affective characteristic word list by the sorted candidate affective characteristic words and the sense relation diagram. The method for obtaining affective characteristic words not only is applicable to universal sentiment analysis with larger subject, but also can carry out deeper sentiment analysis in detailed subject. The extraction system of affective characteristic words can be widely applied to sentiment analysis of comment of news, forums, blogs and the like.

The invention discloses a text relation extraction method based on a double-layer attention mechanism and a bidirectional GRU. The text relation extraction method comprises: carrying out entity labeling and relation labeling text corpora; preprocessing the annotation data to generate a training set and a test set of an entity extraction model and a relationship extraction model; constructing a relationship extraction network; respectively carrying out entity extraction model training and relationship extraction model training; inputting the test set data into an entity extraction model to obtain an entity identification result; and inputting the entity identification result and the test set data into a relationship extraction model to obtain a relationship extraction result. According to the invention, entity position information and entity label information are utilized to expand word vector characteristics; vectorization of text information is realized, more feature information is provided for relationship identification, the correlation between input information and output information of the bidirectional GRU model is improved, the influence of keywords on output is enhanced, the anti-noise capability is improved, and the Chinese text relationship extraction accuracy can be effectively improved.

The invention relates to a field entity attribute relation extraction method based on distance supervision, belonging to the technical field of natural language processing and depth learning. The method inlcudes constructing a domain knowledge base of Chinese tourist attractions, through the Chinese encyclopedia website and tourism website to obtain a large number of tourism domain text collections, using the constructed tourism domain knowledge base of entity pairs to obtain the relational instance text collections from the tourism domain text collection; using the theme model keyword similarity calculation and keyword pattern matching to denoise; finally, using the training corpus which is composed of positive and negative data under each relationship, the part-of-speech feature, dependency feature and short syntax tree feature of the training corpus are extracted, and the three features are fused into a larger feature with more abundant semantic information, and then the relationship extraction model is trained. Experiments show that the F value of the fusion of the three features extracted from the de-noising training corpus is the highest and the extraction performance is thebest.

The invention discloses a universal knowledge graph visualizing device and method based on an artificial intelligence technology. The device includes a data management module, a graph visualizing module, a statistics analyzing and visualizing module, a user management module and a chart management module. The data management module is used for importing, storing and managing knowledge data, setting visualizing chart patterns, conducting cleaning and clustering on the imported data through deep learning, processing the knowledge data through relation extraction and a model calculation technology and then storing the data; the graph visualizing module is used for directly and visually displaying knowledge graphs; the statistics analyzing and visualizing module is used for quantizing data ofthe knowledge graphs to generate corresponding statistics charts, and conducting visual statistics analysis; the user management module is used for protecting the privacy of user data; the chart management module is used for managing generated statistics charts of users. The device can meet demands of employees with more service backgrounds and achieve more efficient visualization, and through intersection of more service domains, fusion of knowledge is facilitated.

The invention discloses an electronic medical record entity relationship extraction method based on a convolutional recurrent neural network. The method comprises the steps of using a data constructorto reconstruct natural statements to obtain a multi-dimensional hierarchical sequence; Mapping the multi-dimensional hierarchical sequence into an input feature vector by adopting a vector representation technology; capturing local and global semantic information of the statement simultaneously by adopting a convolutional recurrent neural network ConvLSTM to obtain an upper sentence vector; adopting a two-stage attention mechanism to capture text content closely associated with the semantic relation, and obtaining a high-stage sentence vector, so as to solve the problem that multiple instances are mistakenly labeled; and performing relation judgment according to the obtained high-level sentence vector to obtain a prediction label. The method does not depend on any external resource characteristics, and the entity relationship extraction performance is improved only by means of data reconstruction and improvement of a network model framework. Meanwhile, the method can be extended to other tasks with the problems of insufficient feature extraction, unbalanced samples and the like.

The invention relates to a domain event graph construction method and device fusing multiple types of facts and entity knowledge. The method comprises the following steps: performing cause relationship extraction and instance cause element extraction on a domain corpus to form an instance cause logic knowledge base; constructing an abstract concept knowledge base with hierarchy; performing entityword abstraction and predicate abstraction on instance events in the instance cause logic knowledge base by utilizing the abstract concept knowledge base to form an abstract cause graph; carrying outentity linking on instance events in the instance cause logic knowledge base by utilizing the entity knowledge graph and adopting an entity linking technology, and fusing event knowledge and entity knowledge to form a cause knowledge graph; and combining the abstract factorial map with the factorial knowledge map to form a domain event map. The advantages of static entity knowledge and action event knowledge can be integrated, the application range of knowledge questions and answers can be widened, and the domain event graph can be used as a common knowledge base to expand domain language resources.

The invention discloses a knowledge graph relational data extraction method based on a semantic syntax interaction network. The method mainly comprises the following steps: collecting a design document of a complex equipment design process, and establishing a design document corpus according to text data of the design document; performing text preprocessing on the design document text data; establishing a relation extraction model based on a semantic syntax multi-round interaction deep neural network; inputting the preprocessed text data and the relationship type label into a relationship extraction model for offline training; and preprocessing the text data of the entity relationship to be predicted, and inputting the preprocessed text data into the trained relationship extraction model to obtain a predicted relationship category. Through multiple rounds of interaction of the semantic information and the syntactic information, the utilization rate of the semantic information and the syntactic information is improved, the semantic information and the syntactic information beneficial to knowledge graph relation data extraction are dynamically and deeply mined, and the flexibility, generalization and accuracy of the model are improved.

The invention provides an entity relationship extraction method and system integrated with dynamic word vector technology. According to the system, an existing knowledge base corresponds to rich unstructured data by using a remote supervision method so as to generate a large amount of training data, so that the problem of insufficient manual annotation corpora is relieved, and the system can reduce the dependence on annotation data, thereby effectively reducing the labor cost. In order to obtain feature information between entities as much as possible, the basic architecture of the model adopts a segmented convolutional neural network; and the semantic information of the example sentences is further extracted by integrating a dynamic word vector technology.

The invention relates to a text statement processing method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a sample text statement containing an entity pair and a relationship label of the entity pair; extracting a positive example statement pair and a negative example statement pair from the sample text statement according to the relationship label, and performing positive and negative example sampling processing to obtain a training set; inputting the training set into a to-be-trained relationship extraction model, and generating a loss valuecomprising a comparison loss value; wherein the comparison loss value is used for representing the difference between the similarity of the statements in the positive example statement pair and the similarity of the statements in the negative example statement pair; adjusting parameters of the relation extraction model according to the loss value, and returning to the step of extracting the positive example statement pair and the negative example statement pair from the sample text statement according to the relation label, so as to carry out iterative training until a training stop conditionis met, and obtaining the relation extraction model; wherein the relationship extraction model is used for identifying the entity relationship of the entity pair in the text statement. By adopting the method, the entity relationship extraction accuracy can be effectively improved.