The present disclosure provides a sentence generation method as well as a sentence generation apparatus and a smart device. The method includes: obtaining an input sentence; searching for structurally similar sentence(s) of each input sentence, where the structurally similar sentence(s) are structurally similar to the input sentence; finding semantically similar sentence(s) of the structurally similar sentence(s); parsing the input sentence and the structurally similar sentence(s) to obtain a subject block, a predicate block, and an object block to rewrite the semantically similar sentences to generate a new sentence; filtering the new sentence based on a preset filtering condition; and labeling the filtered new sentence as a semantically similar sentence of the input sentence. In this manner, a plurality of new sentences with different sentence patterns can be generated based on the same input sentence, which improves the controllability in generating the sentences and saves the labor cost therein.

A digital data processor includes an instruction memory storing instructions specifying data processing operations and a data operand field, an instruction decoder coupled to the instruction memory for recalling instructions from the instruction memory and determining the operation and the data operand, and an operational unit coupled to a data register file and an instruction decoder to perform an operation upon an operand corresponding to an instruction decoded by the instruction decoder and storing results of the operation. The operational unit is configured to perform a table recall in response to a look up table read instruction by recalling data elements from a specified location and adjacent location to the specified location, in a specified number of at least one table and storing the recalled data elements in successive slots in a destination register. Recalled data elements include at least one interpolated data element in the adjacent location.

A method and apparatus for generating information are disclosed. An implementation of the method includes: receiving a target text, the target text including an objective and descriptive information of the objective; performing a dependency syntax parsing on the target text to generate a dependency tree of the target text; matching predetermined syntactic structure tree with the dependency tree to obtain at least one triple, a triple including a subject, a predicate, and an object; and determining, based on words contained in a triple among the at least one triple and a predetermined weight of the syntactic structure tree matched to obtain the triple, a target triple among the at least one triple.

Systems, devices, and methods of the present invention detect rhetoric agreement between texts. In an example, a rhetoric agreement application accesses a multi-part initial query and generates a question communicative discourse tree that represents rhetorical relationships between fragments of the query. The application identifies a sub-discourse tree from the question communicative discourse tree. The application generates a candidate answer communicative discourse tree for each candidate answer of a set of candidate answers. The application computes a level of complementarity between the sub-discourse tree and each candidate answer discourse tree by applying a classification model to the sub-discourse tree and candidate answer communicative discourse trees. The application selects an answer from the candidate answers based on the computed complementarity, thereby building a dialogue structure of an interactive session.

Embodiments of method and system are directed to a best effort method to reconstruct the corrupt files from available nodes or tiers and heal or repair the corrupt files. The method looks for the existence of fingerprints of the lost segments in available tiers on the nodes or across nodes within a cluster. The method discovers the copies of these missing segments, creates an Lp tree referring these discovered segments and creates a virtual file in the namespace. The file is then moved to the destination tier where the corruption exists, thus healing the corruption.

An embodiment of the present invention is a prime representation data structure in a computer architecture. The prime representation data structure has a plurality of records where each record contains a prime representation and where the prime representation is a product of two or more selected prime factors. Each of the selected prime factor associated with an n-gram of a domain representation of a domain string. The domain representation of the domain string is a domain string of ordered, contiguous domain characters. The n-gram being a subset of n number of the ordered, contiguous domain characters in the domain string. The computer architecture performs string searching and includes one or more central processing units (CPUs) with one or more operating systems, one or more input/output device interfaces, one or more memories, and one or more input/output devices. The architecture further includes the prime representation data structure, one or more prime target query data structures and a search process performed by one or more of the CPUs. The CPUs can be organized in a hierarchical structure. The prime target query data structure has one or more target prime queries. Each target prime query is the product of one or more target selected prime factors. Each target selected factor is associated with a target n-gram of a target domain representation of a target domain string. The search process, performed by one or more of the CPUs, determines whether one or more of the target selected prime factors is common with one of the selected prime factors. By performing this efficient testing, the computer system can determine if one or more small strings are included in one or more large strings.

A system and method for automatically generating organization level ontology for knowledge retrieval, are provided. An input/output unit receives a plurality of documents from document sources and an ontology generation system generates the organization level ontology based on the documents. The ontology generation system extracts one or more nodes and directed relationships from each document and generates an intermediate document ontology for each document. A combination of syntactic, semantic, and pragmatic assessment of intermediate document ontology is performed to assess at least structure and adaptability of the ontology. The ontology generation system further generates a refined document ontology, based on assessment, to satisfy one or more quality metrics. Each of the refined document ontologies is integrated together to generate the organization level ontology. Further, a knowledge retrieval system is operatively coupled to the ontology generation system and processes one or more search queries using the generated organization level ontology.

Automatic expansion of a knowledge information dictionary for speech semantic analysis and generation of responses for a dialogue agent are performed in a favorable manner. Category tags are assigned to each term in input speech for all categories when terms are registered in the knowledge information dictionary. A domain of speech content intended by the input speech is estimated, and terms pertaining to the estimated domain are extracted from the input speech as a phrase of a predetermined entity. A response is generated on the basis of the domain of the speech content intended by the input speech and the phrase of the predetermined entity. When a category tag is not assigned to the phrase of a predetermined entity, the phrase of the predetermined entity is registered for the category corresponding to the predetermined entity in the knowledge information dictionary. The knowledge information dictionary has a hierarchical structure, and the application unit generates the response using the hierarchical structure.

Processor-implemented systems and methods are provided for generating clusters of technical documents. A method includes analyzing degrees of similarity among the technical documents using a hierarchical taxonomy code similarity model and a text clustering model. Clusters of the technical documents are generated based upon the analyzed degrees of similarity from the models.

Aspects of the invention include generating a vector representation of a root node of the error based on a hierarchical topology of a computing system; generating a respective vector representations of each subject matter expert of a plurality of subject matter experts based at least in part on the hierarchical topology; selecting a subject matter expert based at least in part on the vector representation of root cause of the error; and uploading a diagnostic software to the computing system.