1041 results about "Named entity" patented technology

A computer implemented system and method for processing text are disclosed. Partially processed text, in which named entities have been extracted by a standard named entity system, is processed to identify attributive relations between a named entity or proper noun and a corresponding attribute. A concept for the attribute is identified and, in the case of a named entity, compared with the named entity's context, enabling a confirmation or conflict between the two to be determined. In the case of a proper name, the attribute's context can be associated with the proper name, allowing the proper name to be recognized as a new named entity.

The invention discloses a system and method for filling out a form from a dialog between a caller and a call center agent. The caller and the caller center agent can have the dialog in the form of telephone conversation, instant messaging chat or email exchange. The system and method provides a list of named entities specific to the call center operation and uses a translation and transcription minor to filter relevant elements from the dialog between the caller and the call center agent. The relevant elements filtered from the dialog are subsequently displayed on the call center agent's computer screen to fill out application forms automatically or through drag and drop operations by the call center agent.

A method is described for providing a named entity classification in a computing system having a processor, comprising the steps of the processor reading, from an LOD (Linking Opening Data) set, an LOD node corresponding to a to-be-classified named entity. The processor also determining a type attribute of the LOD node corresponding to the to-be-classified named entity as a tagged type of the to-be-classified named entity and further reading a candidate type. Finally, the processor computing, based on the tagged type, a possibility of the to-be-classified named entity belonging to the candidate type.

A system and method for providing a name service in a peer-to-peer environment is described. A peer group name server may be a standalone name server that may be used in peer-to-peer applications and environments. A network computing platform may be used as a basis for establishing and operating a peer-to-peer network. A peer group name server may cache information about peers, peer groups and other entities. Peers may discover other peers, peer groups and other entities through the peer group name server. A peer group name server may serve as a reverse lookup provider. A peer group name server may act as a registrar for named entities (e.g. peers) in the peer-to-peer networking environment. In one embodiment, peer group name servers may maintain information about other peer group name servers, thus making a network of decentralized peer group name servers.

The invention discloses a query sentence analyzing method based on understanding of natural languages and a system thereof, and belongs to the technical field of information retrieval. The query sentence analyzing method comprises the following steps: (1) automatic segmenting, named entity identification and part-of-speech tagging of an input Chinese query sentence are implemented; (2) syntax structure of the segmented sentence is analyzed so as to obtain a syntax structural tree, and meaning of each word is determined according to the sentence after the part-of-speech tagging; (3) according to the syntax structure and the meaning of each word, semantic roles of predicates in the sentence are tagged; and (4) according to the analyzed result of the sentence from the levels of syntactics, syntax and semantics, keywords are expanded and the keywords that can reflect user information retrieval requirements are extracted. The query sentence analyzing system of the invention comprises a syntactic analyzing module, a syntax analyzing module, a semantic analyzing module and a keyword extracting module. The query sentence analyzing method and system can greatly improve the accuracy of query results and provide desired query results for users.

A structural natural language index is created by segmenting documents within a repository into text portions and extracting named entity, co-reference, lexical entries, structural-semantic relationships, speaker attribution and meronymic derived features. A constituent structure is determined that contains the constituent elements and ordering information sufficient to reconstruct the text portion. A functional structure of the text portions is determined. A set of characterizing predicative triples are formed from the functional structure by applying linearization transfer rules. The constituent structure, the characterizing predicative triples and the derived features are combined to form a canonical form of the text portion. Each canonical form is added to the structural natural language index. A retrieved question is classified to determine question type and a corresponding canonical form for the question is generated. The entries in the structural natural language index are searched for entries matching the canonical form of the question and relevant to the question type. The characterizing predicative triples are used in conjunction with a generation grammar to create an answer. If the generation fails, some or all of the constituent structure of the matching entry is returned as the answer.

A method for named-entity (NE) recognition and verification is provided. The method can extract at least one to-be-tested segments from an article according to a text window, and use a predefined grammar to parse the at least one to-be-tested segments to remove ill-formed ones. Then, a statistical verification model is used to calculate the confidence measurement of each to-be-tested segment to determine where the to-be-tested segment has a named-entity or not. If the confidence measurement is less than a predefined threshold, the to-be-tested segment will be rejected. Otherwise, it will be accepted.

A system and methodology is provided for filtering temporal streams of information such as news stories by statistical measures of information novelty. Various techniques can be applied to custom tailor news feeds or other types of information based on information that a user has already reviewed. Methods for analyzing information novelty are provided along with a system that personalizes and filters information for users by identifying the novelty of stories in the context of stories they have already reviewed. The system employs novelty-analysis algorithms that represent articles as a bag of words and named entities. The algorithms analyze inter- and intra-document dynamics by considering how information evolves over time from article to article, as well as within individual articles.

The embodiment of the invention discloses a named entity identification method, device and equipment and a computer readable storage medium. The method comprises the steps that character vectors and word vectors of a to-be-identified text are acquired, and weighted sum is carried out on the character vectors and the word vectors to obtain a weighted sum result; the weighted sum result is input into a target two-way LSTM model for processing to obtain a text character sequence; and the text character sequence is input into a target CRF model for processing to obtain a named entity identification result of the to-be-identified text. After the character vectors and word vectors of the to-be-identified text are acquired, weighted sum is carried out on the character vectors and the word vectors, dynamic weight information is used better, a relationship among words of a context is fully considered by adopting a two-way LSTM model, two-way information is fully adopted, then processing is carried out combined with a CRF model, thereby improving the accuracy rate of the named entity identification.

The invention discloses an entity information atlas generation method and device. The method comprises the following steps: collecting a text file; according to predefined class names and relation words, independently extracting a naming entity associated with each class name from each text file, and the attribute of the naming entity associated with each relation word; according to the attribute of the naming entity, independently carrying out association processing on the naming entities in each collected text file to obtain the entity relationship among all naming entities; according to a predefined event name, looking up the naming entity associated with the predefined event name, binding the predefined event name with the found naming entity; and taking the predefined event name as a clue, establishing mapping for associated information dispersed in each text file according to the extracted naming entity and the entity relation, and carrying out aggregation on the associated information to form an entity information atlas. The method can convert unstructured text data into structured data to realize multidimensional and complex knowledge atlas.

A method is provided for representing and updating the state of a dialog involving a series of queries and commands to an artificial intelligence system. Each statement within the dialogue may be modeled as a relational tree spanning nodes corresponding to named entities within the statement. A data structure may be used to store each of these trees and to modify them as the dialog progresses. A subsequent statement in the dialog may be parsed and its contents used to update an ongoing search initiated within that dialog. Statements may be used for the update process despite being fragmentary or not corresponding to any predetermined grammar. An algorithm is disclosed for updating the trees within the data structure after a new statement is parsed.

The invention relates to a sensitive information desensitization method and system for data sharing. According to the method, by adopting statistics, a natural language processing technology and a machine learning technology, the protection of sensitive data in the whole process from data publishing to data application and usage, and automatic identification of sensitive information of such as named entities and addresses is put forward on the basis of the establishment of a sensitive information keyword library, named entities, addresses and the like is put forward; a Sigmoid function is used for calculating the correlation degree of sensitive attributes; a desensitization strategy is carried out in a combined mode of establishing a sensitive attribute generation rule library and adopting a named entity desensitization rule and a core desensitization algorithm; deep desensitization calculation is conducted in combination with numerical sensitive attributes and classified sensitive attributes separately, the desensitization degree of a whole data set is obtained, and the controlled output of data and the like are achieved by downloading a link address hash; thus, the safety of data sensitive information and a sensitive information processing strategy which meets analysis and mining requirements to the maximize degree can be ensured, and the method and system have the advantages of being good in desensitization effect, high in reliability and the like.

Named entity recognition is described, for example, to detect an instance of a named entity in a web page and classify the named entity as being an organization or other predefined class. In various examples, named entity recognition results are used to augment text from which the named entity was recognized; the augmentation may comprise information retrieval results about the named entity mention. In various embodiments, labeled training sentences in many different languages and for many different classes, are obtained to train machine learning components of a multi-lingual, multi-class, named entity recognition system. In examples, labeled training sentences are obtained from at least two sources, a first source using a multi-lingual or monolingual corpus of inter-linked documents and a second source using machine translation training data. In examples, labeled training sentences from the two sources are selectively sampled for training the named entity recognition system.

The invention provides a named entity identification method capable of combining an attention mechanism and multi-target cooperative training. The method comprises the following steps that: (1) carrying out a preprocessing operation on training data, and through character hierarchy mapping, obtaining the character vector representation of a sentence; (2) inputting the character vector representation obtained in (1) into a bidirectional LSTM (Long Short Term Memory) network, and obtaining the character vector representation of each word; (3) through word hierarchy mapping, obtaining the word vector representation of each sentence; (4) through the attention mechanism, splitting the word vector representation obtained in (3) with the character vector representation obtained in (1), and transmitting into the bidirectional LSTM network to obtain the semantic characteristic vector of the sentence; and (5) aiming at the semantic characteristic vector obtained in (4), carrying out entity annotation on each word by a conditional random field, and decoding to obtain an entity tag.

A system and method for developing a rule-based named entity extraction system is provided. The method includes analyzing requirements of business users. The method further includes designing the rule-based named entity extraction system based on the requirement analysis. Further, the method includes implementing the design of rule-based named entity extraction system using one or more GUI-based tools. Thereafter, regression testing of the rule-based named entity extraction system is conducted. Finally, rule-based named entity extraction system is deployed.