35 results about "Name disambiguation" patented technology

Name disambiguation by using private / global directories and communication contexts. Exemplary embodiments include a name disambiguation method, including identifying a plurality of names of persons in text received in the computer system, each of the plurality of names including a start and an end position, a family name and a given name, for each of the plurality of names of persons in the text, retrieving personal identification information from a global directory as a candidate for a name match, retrieving personal identification information from a private directory as a candidate for the name match, comparing the private directory personal identification information with the global directory personal identification information, eliminating the personal identification information from the private directory, retrieving frequencies of communications associated with the personal identification information from the private directory and displaying related information for each of the plurality of names of persons in the text.

A “Name Disambiguator” provides various techniques for implementing an interactive framework for resolving or disambiguating entity names (associated with objects such as publications) for entity searches where two or more same or similar names may refer to different entities. More specifically, the Name Disambiguator uses a combination of user input and automatic models to address the disambiguation problem. In various embodiments, the Name Disambiguator uses a two part process, including: 1) a global SVM trained from large sets of documents or objects in a simulated interactive mode, and 2) further personalization of local SVM models (associated with individual names or groups of names such as, for example, a group of coauthors) derived from the global SVM model. The result of this process is that large sets of documents or objects are rapidly and accurately condensed or clustered into ordered sets by that are organized by entity names.

The invention provides a device and a method for name disambiguation clustering. The device for data processing on a name training set comprises the following units: a representative similarity determination unit for determining the representative similarity of the name training set, wherein the representative similarity is a representative value of the inter-textual similarity in the name training set; a preferable similarity threshold selection unit for clustering the name training set by using different similarity thresholds so as to select the similarity threshold which makes the clustering effect better as the preferable similarity threshold; and a function fitting unit for fitting a function which represents the corresponding relation between the representative similarity and the preferable similarity threshold according to the representative similarity and the preferable similarity threshold of each name training set in at least two name training sets.

The present invention discloses a natural person name disambiguation method, device and medium based on enterprise association relationship, the method includes: obtaining basic training data, using enterprise and personnel as two node types, and constructing a basic heterogeneous graph of enterprise-personnel ;According to the preset splitting rules, split some personnel nodes with multiple edges in the basic heterogeneous graph to obtain a derivative graph; according to the derivative graph, train the preset heterogeneous graph neural network model , to obtain a node vector representation model; add the personnel to be merged into the basic heterogeneous graph as a personnel node, and according to the node vector representation model, judge that the personnel node to be merged and other nodes with the same name in the basic heterogeneous graph Whether to merge. Compared with the prior art, the method of the present invention graphs the enterprise data through the enterprise association relationship, and then disambiguates the nodes with the same name through the trained graph neural network model, which greatly improves the accuracy of the disambiguation results.

The invention provides a name disambiguation method and apparatus. The method comprises the following steps: preprocessing full-text information of names to be disambiguated so as to extract semantic features of the full-text information; according to the semantic features, generating semantic fingerprints of the full-text information of the names to be disambiguated, including mail fingerprints, coauthor fingerprints, mechanism fingerprints and text fingerprints; through comparing the full-text information of the names to be disambiguated with semantic fingerprints having same-name full-text information as the names to be disambiguated in a preset semantic fingerprint database, determining similarity between the full-text information of the names to be disambiguated and the semantic fingerprints having the same-name full-text information as the names to be disambiguated in the preset semantic fingerprint database; and according to the semantic fingerprint similarity, determining a name group after disambiguation which the semantic fingerprints of the full-text information of the names to be disambiguated belongs to. By using such a method, while name disambiguation accuracy is ensured, the name disambiguation speed is improved, and increment name disambiguation is supported.

The embodiment of the present application relates to the field of information retrieval technology, and discloses a name disambiguation method, device, electronic equipment, and computer-readable storage medium, wherein the name disambiguation method includes: according to the word sparse distributed representation generated in advance based on the training corpus SDR, determine the document information of at least two documents in at least two language categories to be disambiguated, one document corresponds to one language category; then, based on the pre-built document author classification model for at least two language categories, According to the document information of each document in at least two language types, classify each document according to the author of the document to obtain the first author category corresponding to each document, and the document author classification model of one language type corresponds to the processing Documents of corresponding language categories; Next, the first author categories under each language category are merged, so as to disambiguate the names of the document authors of each document in each language category.

The present invention discloses a method based on keyword extraction methods, including: text pre -processing and negative word filtration phase, word marking and analysis phase, keyword extraction combination comparison, the purpose is to scattered and heterogeneous heterogeneous qualityFind a target text that is related to the entity and the entity occupies an important position in the text in the severe Internet text; the keywords and entity names in the extract text can be combined to determine whether it is related text.The matching of names when the name appears in the same text; the present invention combines multiple stages of processing steps, which greatly improves the accuracy of matching text with the physical name.

The invention relates to a name disambiguation method orienting Chinese authors in English literature. The method mainly comprises the following steps of (1) extracting personnel information of authors in bibliographical reference information of the English literature, and building the collaboration relationship, the reference relationship and the like between the authors; (2) comparing e-mail addresses of the authors with the same names; (3) calculating the similarity of the affiliated units and subjects of the authors with the same names; (4) calculating the similarity of the collaboration relationship of the authors with the same names; (5) calculating the similarity of the reference relationship of the authors with the same names; (6) performing the name disambiguation on the basis of the three-similarity clustering calculated in the step (3) to the step (5).