Abstract: Techniques for generating natural language text with a natural language generation (NLG) system using a plurality of semantic objects including a first semantic object. The techniques include: obtaining a first specification of the first semantic object, the first specification specifying a first set of one or more data variables, first attributes, a first vocabulary, and a first document structure configuration; obtaining, from at least one data store, first data related to the first set of data variables; determining values of at least some of the first set of data variables using the first data; generating the natural language text including first natural language text, using the first specification, the values of at least some of the first set of data variables; and outputting the generated natural language text.

Abstract: Techniques for determining a degree to which a document can be generated using a natural language generation (NLG) system, the NLG system being configured to generate natural language text. The techniques include using at least one computer hardware processor to perform: obtaining a document including text segments; determining a degree to which at least some of the text segments can be generated using the NLG system; generating a report indicating the degree to which the at least some of the text segments can be generated using the NLG system; and outputting the report.

Abstract: Techniques for generating natural language text with a natural language generation (NLG) system using a plurality of semantic objects including a first semantic object. The techniques include: obtaining a first specification of the first semantic object, the first specification specifying a first set of one or more data variables, first attributes, a first vocabulary, and a first document structure configuration; obtaining, from at least one data store, first data related to the first set of data variables; determining values of at least some of the first set of data variables using the first data; generating the natural language text including first natural language text, using the first specification, the values of at least some of the first set of data variables; and outputting the generated natural language text.

Abstract: Described herein are techniques for generating natural language text customized to linguistic preferences of a user. Customizing the generation of natural language text to the linguistic preference of a user can significantly improve the overall user experience. Some embodiments relate to techniques for learning the linguistic preferences of a user, and for assisting NLG systems to generate natural text that reflects more closely the linguistic preferences of the user. A linguistic preference learner can present different natural language options to a user, and can ask the user to select the option that or appears to reflect more closely the user's personal linguistic preferences. The Linguistic preference learner may determine, based on the user selection, information relating to what linguistic characteristics the user appears to prefer.

Abstract: Techniques for generating natural language text with a natural language generation (NLG) system using a plurality of semantic objects including a first semantic object. The techniques include: obtaining a first specification of the first semantic object, the first specification specifying a first set of one or more data variables, first attributes, a first vocabulary, and a first document structure configuration; obtaining, from at least one data store, first data related to the first set of data variables; determining values of at least some of the first set of data variables using the first data; generating the natural language text including first natural language text, using the first specification, the values of at least some of the first set of data variables; and outputting the generated natural language text.

Abstract: Techniques for identifying vocabulary associated with semantic objects used for generating natural language text with a natural language generation (NLG) system, the semantic objects including a first semantic object having a first set of ordered attributes. The techniques include: obtaining text segments; identifying, from among the text segments and using at least one first machine learning classifier, groups of text segments corresponding to respective semantic objects in the plurality of semantic objects; identifying, from the groups of text segments and using at least one second machine learning classifier, a plurality of vocabularies for the plurality of semantic objects; and generating natural language text using the NLG system, the plurality of vocabularies, and the plurality of semantic objects; and outputting the generated natural language text.

Abstract: Methods and apparatus for summarizing a chatbot interaction with a user are provided. The method comprises using at least one computer hardware processor to perform generating an initial summary based, at least in part, on user data, receiving first input from the user during the chatbot interaction, processing the first input with a natural language processing engine, updating the initial summary based, at least in part, on an output of the processing by the natural language processing engine to generate an updated summary, wherein the updating is performed prior to completion of the chatbot interaction, and outputting a final summary of the chatbot interaction, wherein the final summary of the chatbot interaction is based, at least in part, on the updated summary.

Abstract: Techniques for creating a template to be used in connection with automatically generating text. Techniques include creating a template to include human language text and at least a first tag that serves as a placeholder for a text portion referring to at least one referent; and allowing a user to specify multiple options to be used in place of the first tag when generating output text using the created template, the options comprising at least a first referential expression for the at least one referent and at least a first anaphoric expression for the at least one referent.

Abstract: Techniques for use in connection with a system for automatically generating text. Techniques include accessing information specifying at least one referential expression for at least a first referent and at least one anaphoric expression for at least the first referent; accessing a template that includes human-language text and a first tag that serves as a placeholder for a first text portion including a reference to at least the first referent; automatically identifying, using at least one system rule and at least one processor, text to use for the first text portion at least in part by determining whether to use as the text for the first text portion the at least one referential expression or the at least one anaphoric expression; and automatically generating output text including the human-language text and the identified text for the first text portion.

Abstract: Techniques for creating a template to be used in connection with automatically generating text. Techniques include creating a template to include human language text and at least a first tag that serves as a placeholder for a text portion referring to at least one referent; and allowing a user to specify multiple options to be used in place of the first tag when generating output text using the created template, the options comprising at least a first referential expression for the at least one referent and at least a first anaphoric expression for the at least one referent.

Abstract: Techniques for creating a template to be used in connection with automatically generating text. Techniques include creating a template to include human language text and at least a first tag that serves as a placeholder for a text portion referring to at least one referent; and allowing a user to specify multiple options to be used in place of the first tag when generating output text using the created template, the options comprising at least a first referential expression for the at least one referent and at least a first anaphoric expression for the at least one referent.

Abstract: Techniques for use in connection with generating text, the techniques comprise: obtaining a plurality of items of content and associated metadata; and generating a document plan comprising a plurality of rhetorical relations among items of content in the plurality of items of content, the plurality of rhetorical relations comprising a first set of one or more rhetorical relations and a second set of one or more rhetorical relations different from the first set, the generating comprising: obtaining a schema specifying the first set of one or more rhetorical relations; and identifying the second set of rhetorical relations based, at least in part, on the associated metadata, wherein the second set of rhetorical relations is not in the schema.

Abstract: Techniques for use in connection with a system for automatically generating text. Techniques include accessing information specifying at least one referential expression for at least a first referent and at least one anaphoric expression for at least the first referent; accessing a template that includes human-language text and a first tag that serves as a placeholder for a first text portion including a reference to at least the first referent; automatically identifying, using at least one system rule and at least one processor, text to use for the first text portion at least in part by determining whether to use as the text for the first text portion the at least one referential expression or the at least one anaphoric expression; and automatically generating output text including the human-language text and the identified text for the first text portion.

Abstract: Some embodiments relate to identifying grammatical errors in text that has been automatically generated from a template. The text may be scanned to identify, for example, errors in punctuation, spacing, and capitalization. When an error in the text is identified, it may be corrected. Some embodiments relate to automatically formatting lists in text that is generated from a template. For example, a template may include a tag that specifies a list of elements. A formatting parameter may be determined that specifies whether to format the list as an enumerated list or a textual sentence. Output text may then be generated that includes the list in the proper format.

Abstract: Some embodiments relate to identifying grammatical errors in text that has been automatically generated from a template. The text may be scanned to identify, for example, errors in punctuation, spacing, and capitalization. When an error in the text is identified, it may be corrected. Some embodiments relate to automatically formatting lists in text that is generated from a template. For example, a template may include a tag that specifies a list of elements. A formatting parameter may be determined that specifies whether to format the list as an enumerated list or a textual sentence. Output text may then be generated that includes the list in the proper format.

Abstract: Method and apparatus for automatically generating text in a human language using a template. The template may include at least one grammatical tag that implicates at least one actor in a sentence in the template. Human-language text may be determined to fill in the tag based on a characteristic of the actor, such as, for example, the actor's gender, whether the actor is singular or plural, or some other characteristic of the actor.