221 results about "Dependency tree" patented technology

Methods, systems and computer program products are provided for visually indicating relationships among cells in a spreadsheet. Each of a first graphical linking element extending between cells in a first branch of a dependency tree of a root cell and a second graphical linking element extending between cells in a second branch of the dependency tree of the root cell is independently displayed and hidden.

In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.

A processor is connected to a storage device storing an incoming e-mail, a dependency database, and code for a calendar application. The dependency database can be built from an e-mail corpus containing a plurality of natural language e-mails containing scheduling information. The incoming e-mail is parsed by the processor to build a dependency tree containing word pairs from the e-mail that are found in the dependency tree. The word pairs are stored as dependency pairs in a tree structure in the dependency tree. A probability sum for the dependency tree is calculated to determine if the e-mail contains scheduling information. If the probability sum exceeds a predetermined value, the e-mail is assumed to contain scheduling information and the scheduling information is extracted from the dependency tree and exported to the calendar application.

Disclosed is a computer-implemented planning process that aids a system administrator in the task of creating a job schedule. The process treats enterprise computing resources as a grid of resources, which provides greater flexibility in assigning resources to jobs. During the planning process, an administrator or other user, or software, builds a job-dependency tree. Jobs are then ranked according to priority, pickiness, and network centricity. Difficult and problematic jobs then are assigned resources and scheduled first, with less difficult jobs assigned resources and scheduled afterwards. The resources assigned to the most problematic jobs then are changed iteratively to determine if the plan improves. This iterative approach not only increases the efficiency of the original job schedule, but also allows the planning process to react and adapt to new, ad-hoc jobs, as well as unexpected interruptions in resource availability.

In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.

A relation detection model training solution. The relation detection model training solution mines freely available resources from the World Wide Web to train a relationship detection model for use during linguistic processing. The relation detection model training system searches the web for pairs of entities extracted from a knowledge graph that are connected by a specific relation. Performance is enhanced by clipping search snippets to extract patterns that connect the two entities in a dependency tree and refining the annotations of the relations according to other related entities in the knowledge graph. The relation detection model training solution scales to other domains and languages, pushing the burden from natural language semantic parsing to knowledge base population. The relation detection model training solution exhibits performance comparable to supervised solutions, which require design, collection, and manual labeling of natural language data.

A processor is connected to a storage device storing an incoming e-mail, a dependency database, and code for a calendar application. The dependency database can be built from an e-mail corpus containing a plurality of natural language e-mails containing scheduling information. The incoming e-mail is parsed by the processor to build a dependency tree containing word pairs from the e-mail that are found in the dependency tree. The word pairs are stored as dependency pairs in a tree structure in the dependency tree. A probability sum for the dependency tree is calculated to determine if the e-mail contains scheduling information. If the probability sum exceeds a predetermined value, the e-mail is assumed to contain scheduling information and the scheduling information is extracted from the dependency tree and exported to the calendar application.

A method and a system implementing web-based incremental computing. A webpage updates when a data value relevant to displayed information is altered and updates only those affected elements. A watch system monitors relevant data values and in conjunction with a dependency tree, may execute functions to alter only altered elements of a webpage. A framework to automatically track data dependencies and propagate data changes is disclosed.

The invention provides a specific target emotion classification method based on attention coding and a graph convolution network, and the method comprises the steps: obtaining a context and a hidden state vector corresponding to a specific target through a preset bidirectional recurrent neural network model, and carrying out the multi-head self-attention coding of the context and the hidden statevector; extracting a syntax vector in a syntax dependency tree corresponding to the context by combining a point-by-point convolution graph convolutional neural network, and performing multi-head self-attention coding on the syntax vector; then, multi-head interaction attention is used for carrying out interaction fusion on syntactic information codes, context semantic information codes, syntacticinformation codes and specific target semantic information codes; and splicing the fused result with the context semantic information code to obtain a final feature representation, and obtaining an emotion classification result of the specific target based on the feature representation. Compared with the prior art, the relation between the context and the syntax information and the relation between the specific target and the syntax information are fully considered, and the accuracy of sentiment classification is improved.

The invention provides a microservice application arrangement and deployment method and device and a computer terminal. The method comprises the steps of reading a dependency tree of a pre-stored microservice application, wherein the microservice application is a root node of the dependency tree; traversing all nodes in the dependency tree according to a preset rule and determining a starting sequence of all nodes; and deploying applications corresponding to all nodes to an operation environment according to the starting sequence, thereby finishing deploying the microservice application. According to the method, the device and the computer terminal, the condition that a user directly writes a complex program to realize the arrangement and deployment of the applications is avoided, a plurality of sets of environments for operating microservice are set up rapidly, the continuous integration, continuous delivery and continuous deployment are facilitated, all applications are deployed through a key, and a technical level of an enterprise is improved.

The present disclosure discloses a method and a device for clarifying questions based on deep question and answer. The method includes: receiving a query sentence; recalling corresponding answer titles and / or history query sentences according to the query sentence; analyzing the answer titles and / or the history query sentences to obtain corresponding dependency trees; clustering the answer titles and / or the history query sentences according to the dependency trees, to generate at least one cluster of questions; generalizing the at least one cluster of questions to generate candidate and clarified questions; and displaying the candidate and clarified questions.

The invention discloses a dependency tree and attention mechanism-based attribute sentiment classification method. The method comprises the steps of selecting a smallest sub-tree part comprising givenattributes based on a dependency tree analysis result of a whole text, and taking clauses of the part as representations of context information of the attributes; then performing modeling on contextsof sentences and contexts of the attributes by utilizing two bidirectional threshold circulation units to obtain two feature representation matrixes fixed in size; obtaining feature representations of the text and specific attributes by utilizing an attention mechanism; and finally performing sentiment polarity classification of the specific attributes by utilizing a multilayer perceptron. The classification method proposed by the invention can extract different attribute feature information for different attributes in the same text, and is high in classification accuracy.

The embodiment of the invention discloses a parallel processing method and device of block chain data, equipment and a storage medium. The method comprises: dependency identification is carried out ona to-be-processed transaction request to construct a directed acyclic graph including at least one dependency tree, wherein the dependency tree is used for recording a dependency relationship betweentransaction requests; and according to the directed acyclic graph, parallel processing is carried out on at least two transaction requests without dependency. Therefore, a problem of processing suspension or error caused by the dependency relationship in the prior art is solved; the parallel processing of the block chain data is realized; and the dependency relationship of the transaction requestis considered.

The invention relates to a relation extraction method and system based on an attention cycle gated graph convolutional network, and the method comprises the steps of carrying out the semantic dependency analysis of a statement, enabling word embedding to be connected with a position feature, and obtaining a final word embedding representation; constructing a BLSTM network layer, and extracting a word context feature vector; applying an attention mechanism to the dependency tree to obtain a soft adjacency matrix of a fully connected graph with weight information; transmitting the word context feature vector and the soft adjacency matrix into a gated graph convolutional network, and extracting a high-order semantic dependence feature to obtain vector representation of a statement; and extracting vector representations of the two marked entities, splicing the extracted vector representations of the two marked entities with the vector representation of the statement, transmitting the spliced vector representation of the statement into a full connection layer of the gated graph convolutional network, calculating the probability of each relationship type and predicting the relationship type, and finally obtaining the relationship type of the statement. According to the invention, key information loss is avoided, and the relationship extraction performance is improved.

A computer system, program, or method may provide for quantifying changes in an individual component among a plurality of components in a software development environment where the components are incorporated in a build. This may include calculating a baseline weight of the individual component prior to a change; effecting, in the software development environment, the change to the individual component; calculating a modified weight of the individual component after the change; determining a threshold for a difference between the baseline weight and the modified weight of the individual component; calculating the difference between the baseline weight and the modified weight of the individual component; and providing on the software development computer a notification if the difference exceeds the threshold. The weights of the individual component may use data such as a count of other components required by the individual component, an install size, a runtime memory usage, a depth of a dependency tree, a component license type, a component category for memory location, a component code type, and / or other types of data.

The invention discloses a man-machine interaction question-answering method and system based on complex intention intelligent recognition, and the method comprises the steps: obtaining an original question sentence of a user, carrying out the sentence segmentation and part-of-speech tagging, and obtaining the part-of-speech information of each component word of the question sentence; performing dependency syntax analysis on the question sentence to obtain a dependency syntax tree; carrying out industry entity identification to obtain industry entities and the number, and extracting a core dependency tree to simplify questions; carrying out industry question relation classification on the questions, carrying out Chinese multi-intention question rewriting, and then carrying out knowledge retrieval on the questions; and selecting and generating answers for knowledge retrieval results, and returning the answers to the user. According to the method and system, multi-intention complex questions can be effectively simplified in any industrial scene, the intention of the user can be accurately understood, the industrial knowledge can be more naturally fed back to the user, the user can more accurately and quickly obtain the required industrial knowledge, the user experience is improved, and the method and system are particularly suitable for man-machine interaction intelligent questions and answers in the medical industry.

Disclosed is a computer-implemented planning process that aids a system administrator in the task of creating a job schedule. The process treats enterprise computing resources as a grid of resources, which provides greater flexibility in assigning resources to jobs. During the planning process, an administrator or other user, or software, builds a job-dependency tree. Jobs are then ranked according to priority, pickiness, and network centricity. Difficult and problematic jobs then are assigned resources and scheduled first, with less difficult jobs assigned resources and scheduled afterwards. The resources assigned to the most problematic jobs then are changed iteratively to determine if the plan improves. This iterative approach not only increases the efficiency of the original job schedule, but also allows the planning process to react and adapt to new, ad-hoc jobs, as well as unexpected interruptions in resource availability.

A system for automatically segmenting the compilation of a large Java project under a controller node to a grid of slave nodes so as to reduce build time. A build tree scanner at the controller node sizes the number of files in the project and the length of each source file. Each slave node has a complete and synchronized set of all compilable units in the project. The controller node automatically segments the source files into a number of filesets commensurate with the number and resource capacities of slave nodes available for the build, and allocates the filesets to the slave nodes. Each slave node scans each source file in its allocated fileset or filesets, builds a dependency tree comprising dependency graphs for each source file, and sends that tree to the controller node, from which the controller node then constructs a master dependency tree. The controller node uses the master dependency tree to automatically generate build filesets, which are then equitably allocated to the slave nodes, which track the time required to build each file in each allocated build fileset. Then the targets are reaped and placed on the controller node, which performs post-compilation tasks.

Visualization frameworks may include solvers. The solvers may be used to determine the properties of view components of view compositions. In some instances, the solvers may be explicitly composed using a relational structure, such as a dependency tree. In some instances, the solvers may be implicitly composed based on property-setters having solvers invoking other property-setters having solvers.

A system and method for determining and displaying return-on-investment (ROI) of components on a network is described. The system includes one or more processes that initiate and receive data streams directly or indirectly from components on the network or from other databases on the network. A dependency tree is created for each component relating each component to other components and to information characterizing the component. A portal is provided to export data feeds to other systems on the network and to generate reports and display usage information on each component on the network. Usage information may be grouped or summarized according to business function or by top level manager.

The present invention relates to a method for constructing a Vietnamese dependency tree bank on the basis of Chinese-Vietnamese vocabulary alignment corpora and belongs to the technical field of natural language processing. According to the present invention, firstly, a Chinese-Vietnamese vocabulary alignment sentence pair library is constructed; then a Chinese dependency tree corpus is constructed; and according to the constructed Chinese-Vietnamese vocabulary alignment sentence pair library and Chinese dependency tree corpus, a Vietnamese dependency tree corpus is constructed. The Vietnamese dependency tree bank constructed by the method can provide powerful support for upper layer applications of syntactic analysis, machine translation, information acquisition and the like; a bilingual parallel dependency tree corpus is constructed; according to the method for constructing a dependency tree, which is disclosed by the present invention, the process of manually collecting and labeling the Vietnamese dependency tree bank is simplified and labor and time of constructing the tree bank are saved; and compared with a method adopting a machine to carry out learning, the method for constructing a dependency tree, which is disclosed by the present invention, is obviously improved in accuracy.

Methods, systems and computer program products are provided for visually indicating relationships among cells in a spreadsheet. Each of a first graphical linking element extending between cells in a first branch of a dependency tree of a root cell and a second graphical linking element extending between cells in a second branch of the dependency tree of the root cell is independently displayed and hidden.

The invention discloses an aspect-level text sentiment classification method and system, and the method comprises the steps: extracting the long-distance dependence features of a sentence text according to the obtained local feature vectors of the sentence text, and obtaining the context feature representation of the sentence text; constructing a syntactic dependency relationship among words in the sentence text according to the context feature representation of the sentence text to obtain aspect-level feature representation of the sentence text; and constructing a dependency tree-based graphattention neural network, and obtaining aspect-level emotion categories of the text according to aspect-level feature representation of the sentence text. The method comprises the steps of extractinglocal feature information in a sentence by adopting a convolutional neural network, learning pooled features of the convolutional neural network by utilizing a bidirectional long-short-term memory network, obtaining context information of the sentence, constructing a dependency tree-based graph attention network model, and modeling a sentence dependency relationship by utilizing syntactic information of a dependency tree, thereby improving the performance of sentiment classification.

A computer system for optimizing dependencies of batch jobs includes a scheduling server and a computer terminal. The scheduling server schedules an execution order of batch jobs on at least one batch server. The computer terminal receives a user input and queries the scheduling server for precedent batch jobs of a target batch job. The viewing application receives a query result from the scheduling server and depicts, on an electronic display, a dependency tree that includes one or more precedent batch jobs and the target batch job. The viewing application further depicts a critical path that includes critical precedent batch jobs that process data required by the target batch job. The viewing application provides for optimizations that optimize the execution order of the batch jobs such that the target batch job finishes executing earlier than the target batch job would have finished executing without the one or more optimizations.

The invention provides a conflict-based method for checking dependency of a software package. The method comprises the following steps of: reading dependence information from a software package dependency description file and generating a dependency tree of the software package; extracting a dependence package set of the software package, and carrying out existence check; acquiring a conflict package set according to the dependence package set; sequentially processing conflicts in the conflict package set; and judging that the dependence of the software package can be satisfied if all the conflicts can be solved, and otherwise, judging that the dependence of the software package cannot be satisfied. The method provided by the invention is oriented to the dependency management of software package at a Linux distribution end, and the conflict associated with the software package is removed from the aspect of conflict to check the dependence completeness of the software package at the Linux distribution end.

The invention provides a method and a system for extracting an entity relationship by using structural information. The method comprises the following steps: acquiring a text which comprises a plurality of sentences with marked relationships; acquiring a group of dependency tree modes related to sentence structures in the text; extracting the characteristic of each sentence in the text by referring to the dependency tree modes, wherein the characteristic comprises the structural characteristic of the sentence; collecting the extracted characteristics to train a relationship marking model; and applying the relationship marking model to an unmarked sentence to extract a relationship example. Furthermore, the invention also discloses a process for automatically extracting the dependency tree modes. Compared with the prior art, the relationship extracting system and the relationship extracting method of the invention can realize high performance.

A relation detection model training solution. The relation detection model training solution mines freely available resources from the World Wide Web to train a relationship detection model for use during linguistic processing. The relation detection model training system searches the web for pairs of entities extracted from a knowledge graph that are connected by a specific relation. Performance is enhanced by clipping search snippets to extract patterns that connect the two entities in a dependency tree and refining the annotations of the relations according to other related entities in the knowledge graph. The relation detection model training solution scales to other domains and languages, pushing the burden from natural language semantic parsing to knowledge base population. The relation detection model training solution exhibits performance comparable to supervised solutions, which require design, collection, and manual labeling of natural language data.

The invention belongs to the field of processing for natural languages of computational linguistics, and discloses a method for processing unknown words in Chinese-language dependency tree banks. The method includes steps of A, searching all synonyms of the unknown words by the aid of synonym forests; B, computing character pattern similarity degrees among the unknown words and all the synonyms of the unknown words according to character pattern features of Chinese characters; C, extracting mapped words and information quantities of word classes of the mapped words when the character pattern similarity degrees among the unknown words and the multiple synonyms are high, and improving character pattern similarity degree computation models; D, extracting the words with the maximum character pattern similarity degrees as the optimal mapped words of the unknown words and using the extracted words as explanation for the unknown words in the tree banks. The method has the advantages that unit pairs (word classes, word classes) in dependency syntactic analysis can be recovered to unit pairs (word classes, words) or unit pairs (words, word classes) on the premise that the scales of the tree banks are no longer expanded, accordingly, the information granularity can be refined, the problem of data sparseness can be solved, and the dependency syntactic analysis performance can be improved.