Abstract: A platform for developing a virtual personal assistant (“VPA”) application includes an ontology that defines a computerized structure for representing knowledge relating to one or more domains. A domain may refer to a category of information and/or activities in relation to which the VPA application may engage in a conversational natural language dialog with a computing device user. Re-usable VPA components may be linked to or included in the ontology. An ontology populating agent may at least partially automate the process of populating the ontology with domain-specific information. The re-usable VPA components may be linked with the domain-specific information through the ontology. A VPA application created with the platform may include domain-adapted re-usable VPA components that may be called upon by an executable VPA engine to determine a likely intended meaning of conversational natural language input of the user and/or initiate an appropriate system response to the input.

Abstract: A platform for developing a virtual personal assistant (“VPA”) application includes an ontology that defines a computerized structure for representing knowledge relating to one or more domains. A domain may refer to a category of information and/or activities in relation to which the VPA application may engage in a conversational natural language dialog with a computing device user. Re-usable VPA components may be linked to or included in the ontology. An ontology populating agent may at least partially automate the process of populating the ontology with domain-specific information. The re-usable VPA components may be linked with the domain-specific information through the ontology. A VPA application created with the platform may include domain-adapted re-usable VPA components that may be called upon by an executable VPA engine to determine a likely intended meaning of conversational natural language input of the user and/or initiate an appropriate system response to the input.

Abstract: A platform for developing a virtual personal assistant (“VPA”) application includes an ontology that defines a computerized structure for representing knowledge relating to one or more domains. A domain may refer to a category of information and/or activities in relation to which the VPA application may engage in a conversational natural language dialog with a computing device user. Re-usable VPA components may be linked to or included in the ontology. An ontology populating agent may at least partially automate the process of populating the ontology with domain-specific information. The re-usable VPA components may be linked with the domain-specific information through the ontology. A VPA application created with the platform may include domain-adapted re-usable VPA components that may be called upon by an executable VPA engine to determine a likely intended meaning of conversational natural language input of the user and/or initiate an appropriate system response to the input.

Abstract: A platform for developing a virtual personal assistant (“VPA”) application includes an ontology that defines a computerized structure for representing knowledge relating to one or more domains. A domain may refer to a category of information and/or activities in relation to which the VPA application may engage in a conversational natural language dialog with a computing device user. Re-usable VPA components may be linked to or included in the ontology. An ontology populating agent may at least partially automate the process of populating the ontology with domain-specific information. The re-usable VPA components may be linked with the domain-specific information through the ontology. A VPA application created with the platform may include domain-adapted re-usable VPA components that may be called upon by an executable VPA engine to determine a likely intended meaning of conversational natural language input of the user and/or initiate an appropriate system response to the input.

Abstract: Methods and apparatus, including computer program products, related to entity name translation. In general, a set of data records including one or more names of one or more entities in a source language is received and candidate translations are generated for the names of the data records. The candidate translations are strings of characters in a target language that has a different orthography than an orthography of the source language. A data repository is queried for records matching the candidate translations. A translation of at least one of the candidate translations is selected based on a comparison of a combination of candidate translations and properties of the source record comprising of the publication date, entity name co-occurrence and their frequency with the result of the querying the data repository.

Abstract: Methods and apparatus, including computer program products, related to entity name translation. In general, a set of data records including one or more names of one or more entities in a source language is received and candidate translations are generated for the names of the data records. The candidate translations are strings of characters in a target language that has a different orthography than an orthography of the source language. A data repository is queried for records matching the candidate translations. A translation of at least one of the candidate translations is selected based on a comparison of a combination of candidate translations and properties of the source record with the result of the querying the data repository.

Abstract: A Hidden Markov model is used to segment a data sequence. To reduce the potential for error that may result from the Markov assumption, the Viterbi dynamic programming algorithm is modified to apply a multiplicative factor if a particular set of states is re-entered. As a result, structural domain knowledge is incorporated into the algorithm by expanding the state space in the dynamic programming recurrence. In a specific example of segmenting resumes, the factor is used to reward or penalize (even require or prohibit) a segmentation of the resume that results in the re-entry into a section such as Experience or Contact Information. The method may be used to impose global constraints in the processing of an input sequence or to impose constraints to local sub-sequences.

Abstract: A method and apparatus is disclosed for making predictions about entities represented in documents and for information analysis of text documents or the like, from a large number of such documents. Predictive models are executed responsive to variables derived from canonical documents to determine documents containing desired attributes or characteristics. The canonical documents are derived from standardized documents, which, in turn, are derived from original documents.