Abstract: The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

Abstract: A user experience management system obtains survey results providing insight on the user's experience with provided products and services. Periodically, the user experience management system uses these results in conjunction with the help desk data and the user experience management system monitoring data to identify the set of problems with specific products and services on a granular level. The product management system generates a resolution to address a problem definition generated by the user experience management system. The user experience management system monitors the resolution and updates the database with data pertaining to the resolution. Other embodiments are disclosed herein.

Abstract: Systems, methods, and machine-readable media are disclosed for detecting sensitive personal information in a file. An entity extractor may extract a set of terms and a set of person candidates from a sentence in a file. Each term of the set of terms belongs to a set of sensitive categories. For a verb in the sentence, a relationship builder may determine a relationship between the respective verb, a subject, and an object in the sentence, where the respective verb, the subject, or the object includes a term. An event detector may determine, based on the relationship, that the term relates to a person from the set of set of person candidates and to a sensitive category. The event detector may create an event specifying the term, the sensitive category, and the person in response to determining that the term relates to the person and to the sensitive category.

Abstract: An estimation apparatus clusters a group of voice signals including a voice signal having a speaker attribute to be estimated into a plurality of clusters. Subsequently, the estimation apparatus identifies, from the plurality of clusters, a cluster to which the voice signal to be estimated belongs. Next, the estimation apparatus uses a speaker attribute estimation model to estimate speaker attributes of respective voice signals in the identified cluster. After that, the estimation apparatus estimates an attribute of the entire cluster, by using an estimation result of the speaker attributes of the voice signals in the identified cluster, and outputs an estimation result of the speaker attribute of the entire cluster, as an estimation result of the speaker attribute of the voice signal to be estimated.

Abstract: Methods and systems for receiving a plurality of documents including short text data and determining a plurality of forward similarity values based on the short text data in each of the plurality of documents, determining a plurality of reverse similarity values based on the short text data in each of the plurality of documents, generating a forward and reverse similarity matrix based on the plurality of forward similarity values and the plurality of reverse similarity values, and generating a plurality of short text similarity based clusters to group the short text data of the plurality of documents based on the forward and reverse similarity matrix.

Abstract: A method for migrating course content may include extracting, from a source system, a first portion of a course data at the source system via an application programming interface (API) associated with the source system such as a public application programming interface and/or a private application programming interface. A second portion of the course data may be extracted from the source system by screen scraping. The course data may be transformed and at least a portion of the transformed course data is transferred to the target system while student data associated with the course data is archived. The source system and the target system may be a same or different one of a learning management system, a student information system, a customer relationship management system, an enterprise resource planning system, a financial aid management system, and a human resource management system. Related systems and computer program products are also provided.

Abstract: Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.

Abstract: Methods are provided, such as a method of interacting with a large language model (LLM), including the step of a processing system using a structured, machine-readable representation of data that conforms to a machine-readable language, such as a universal language, to provide new context data for the LLM, in order to improve the output, such as continuation text output, generated by the LLM in response to a prompt; and such as a method of interacting with a LLM, including the step of providing continuation data generated by the LLM to a processing system that uses a structured, machine-readable representation of data that conforms to a machine-readable language, such as a universal language, in which the processing system is configured to analyse the continuation output generated by the LLM in response to a prompt to enable an improved version of that continuation output to be provided to a user. Related computer systems are provided.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for obtaining data items for input to a chatbot. In certain embodiments, a chatbot system includes a component that can be invoked by a chatbot in the chatbot system to obtain data items needed by the chatbot. The component can be invoked based on a reference to the component in a dialog flow definition configured for the chatbot. The reference to the component can indicate a composite entity that the component will use to determine how the data items are obtained from a user. The composite entity acts as a container for the data items and may be configured separately from the dialog flow definition of the chatbot. The data items can be obtained based on rules specified in a composite entity definition configured for the composite entity.

Abstract: Disclosed herein is computer technology that applies natural language processing (NLP) techniques to training data to generate information used to train a natural language generation (NLG) system to produce output that stylistically resembles the training data. In this fashion, the NLG system can be readily trained with training data supplied by a user so that the NLG system is adapted to produce output that stylistically resembles such training data. In an example, an NLP system detects a plurality of linguistic features in the training data. These detected linguistic features are then aggregated into a specification data structure that is arranged for training the NLG system to produce natural language output that stylistically resembles the training data. Parameters in the specification data structure can be linked to objects in an ontology used by the NLG system to facilitate the training of the NLG system based on the detected linguistic features.

Abstract: A computing system, computer program product, and computer-implemented method for quantitative comment summarization are provided. The method includes receiving a collection of comments, identifying a set of candidate key points corresponding to the collection of comments, and selecting a subset of key points from the set of candidate key points, wherein the selected subset of key points includes key points that are most salient in the collection of comments. The method also includes automatically mapping each comment within the collection of comments to any corresponding key points within the subset of key points based on a match score between each comment and key point pair, as well as generating a summary including the subset of key points and an absolute number or percentage of the comments mapped to each key point.

Abstract: The present invention relates to a patent document creating device, method, computer program, computer-readable recording medium, server and system.

Abstract: Methods for generating and utilizing a multi-modal discourse tree (MMDT) are provided herein. An extended discourse tree (EDT) may be generated (e.g., from a discourse tree (DT) or a communicative DT (CDT)) from a corpus of text. Data records (e.g., records contained numerical data) may be linked to the extended discourse tree to generate a multi-modal discourse tree. The multi-modal discourse tree may link any suitable text/records from disparate sources. For example, entities identified from elementary discourse units of the EDT may be matched to an entity of a data record. Causal links may be identified between EDTs and/or data records. Rhetorical relationships can be identified for each entity/causal link match to incorporate the data records with the EDT to generate a MMDT. The MMDT may be used to classify subsequent input, to generate answers to subsequent questions, to navigate the corpus of text and/or data records, or the like.

Abstract: The present invention relates to an apparatus and method for automatically generating machine reading comprehension training data, and more particularly, to an apparatus and method for automatically generating and managing machine reading comprehension training data based on text semantic analysis. The apparatus for automatically generating machine reading comprehension training data according to the present invention includes a domain selection text collection unit configured to collect pieces of text data according to domains and subjects, a paragraph selection unit configured to select a paragraph using the pieces of collected text data and determine whether questions and correct answers are generatable, and a question and correct answer generation unit configured to generate questions and correct answers from the selected paragraph.

Abstract: A search assistant method, the method of searching for patent documents material to elements of a target patent document including: receiving the target patent document including the elements, the target patent document being machine-readable; selecting keywords from the elements; identifying synonyms corresponding to the keywords in a synonym database; forming the keywords and the identified synonyms into a search string; identifying patent documents in a patent database based on the search string; highlighting the keywords and the identified synonyms in the identified patent documents; ranking the identified patent documents based on relevance of the highlighted keywords and the highlighted identified synonyms in each of the identified patent documents to the search string; determining citations in at least one of the ranked identified patent documents for each of the highlighted keywords and the highlighted identified synonyms; and mapping the elements of the target patent document to the determined citati

Abstract: This disclosure relates generally to data anonymization using clustering techniques. In Typically, data anonymization using global recoding can overgeneralize the data. However, preservation of information while anonymization the data is of equal importance as obscuring the relevant information that can be used by the attackers. The disclosed method and system utilized attribute taxonomy tree for generalization to optimize the generalization of the records. The disclosed method uses clustering-based approach and after clustering, each cluster is solved independently using ILP model for K-Anonymization. The ILP model is solved by generalizing the value of the attributes. Sometimes, even after clustering the number of possible patterns is large, thus the disclosed method generates patterns on the fly during multiple iterations.

Abstract: A data processing method includes: obtaining a to-be-detected text, and determining a context word set and a candidate substitute word set corresponding to a to-be-detected word in the to-be-detected text to be inputted into a pronoun resolution neural network for feature extraction; performing positive-example iteration processing and negative-example iteration processing on the features corresponding to the context word set and the candidate substitute word set, to obtain a positive-example feature vector length and a negative-example feature vector length, and calculating a substitute probability corresponding to each candidate substitute word in the candidate substitute word set according to the positive-example feature vector length and the negative-example feature vector length; determining a target substitute word according to the substitute probability corresponding to the each candidate substitute word; and inserting the target substitute word into the to-be-detected text according to a position corr

Abstract: Techniques for generating a natural language question template for an artificial intelligence question and answer (QA) system are disclosed. A graph database query relating to a QA system is parsed using a predefined schema. The parsing includes extracting a first plurality of values from the graph database query relating to a where clause in the graph database query, extracting a second plurality of values from the graph database query relating to a return clause in the graph database query, identifying a QA template rule relating to the graph database query, based on a match clause in the graph database query. A natural language question template is generated based on the first plurality of values, the second plurality of values, and the identified QA template rule. The natural language question template is suitable for use by the QA system as part of generating a response to a natural language question.

Abstract: Described herein are techniques for automatically updating (e.g., augmenting) the content of documents with additional content. Such documents may be generated automatically using a natural language generation (NLG) engine. In some embodiments, the techniques developed by the inventors are configured to update a document with text explaining what caused a certain event or set of events described in the document. The techniques developed by the inventors involve multiple acts. For example, the techniques may identify content of relevance to a document, may identify whether the content includes a text segments having a causal structure, and may separate the cause sub-segment from the cause-segment. The cause sub-segment, and optionally the effect sub-segment, may be used to update the document.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. Narrative analytics that are linked to communication goal statements can employ a conditional outcome framework that allows the content and structure of resulting narratives to intelligently adapt as a function of the nature of the data under consideration. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal.

Abstract: An apparatus and method for dynamic data synthesis wherein the apparatus receives a first textual data set, displays at least an interface field, populates the at least an interface field with at least an element of the first textual data set, generates a destination-formatted query using the populated at least an interface field, transmits the formatted query to a remote device, receives a second textual data set, outputs a user score and a set of score metadata, generates a comprehensive report, and displays the comprehensive report to the user.

Abstract: A method of detecting a pattern in common in two text files, each comprising an ordered sequence of words, is disclosed. The method includes generating groups of words having the same syntactic function, comprising at least one word from each text file such that each word in a group is synonymous with another word in the same group, associating each word in a text file belonging to a group of words with a tag representative of the group, generating, for each text file, at least one dense set of words satisfying a condition of internal proximity in the text file, determining at least one pattern in common in the two text files, a pattern in common including one or more sets of words sharing the same tag and comprising at least one word from a dense set of words in each text file.

Abstract: A system for performing automated speech recognition (ASR) on audio data includes a queue manager to receive a request to perform ASR on audio data, add the request to a queue of incoming requests, and determine a queue depth representing a number of requests in the queue at a given time. The system also includes a load supervisor to receive the request and the queue depth from the queue manager and assign a service level for the request based on the queue depth. In addition, the system includes a speech-to-text converter to receive the assigned service level for the request from the load supervisor, select an ASR model for the request based on the received service level, receive the audio data associated with the request, and perform ASR on the audio data using the selected ASR model.

Abstract: A method, apparatus, system, and computer program code for intelligent data discovery with dynamic ontology are provided. According to one illustrative embodiment, the method using a number of processors to perform the steps of: identifying a set of data items in unstructured content using a dynamic data schema populated from a dynamic ontology; and responsive to identifying a data item that is not recognized in the data schema: storing the data item with labels; generating a weight for the data item; and responsive to the weight exceeding a threshold, updating the schema to include the data item that was not recognized.

Abstract: 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-communication 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-communication 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.

Abstract: Devices and techniques are generally described for a speech processing routing architecture. First input data representing an input request may be received. First data including a semantic interpretation of the input request may be determined. Metadata of the first input data may be determined. The metadata may identify an entity associated with the input request. In some examples, a query may be sent to a first component. The query may include the metadata. In some examples, second data that identifies a first skill associated with the entity may be received from the first component. In various examples, the first skill may be selected for processing the first input data based at least in part on the first data and the second data.

Abstract: Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

Abstract: A clustered metasearch system receives a search query from a user. The system uses Natural Language Processing to identify an object of the search query and descriptors of the search query. The system sorts the search into an applicable realm based on the object of the search query. The system then conducts the search across a variety of search engines and collects root domains from the search results. Root domains within the same realm as the search query are prioritized and additional factors such as the presence of descriptors in the result, the recency of the result, the search engine rank of the result, and the distance from the center of the realm are used to determine the final ranking of the results. The results are then displayed to a user.

Abstract: An embodiment for identifying and replacing logically neutral phrases in natural language queries may include receiving a natural language query. The embodiment may also identify one or more logically neutral or non-logically neutral anchors in the natural language query. The embodiment may also identify boundaries containing one or more logically neutral phrases. The embodiment may further include detecting semantic and logical relations between verbal phrases and functional language between and adjacent to the one or more logically neutral and non-logically neutral anchors to reintroduce non-logically neutral language back into a non-logically neutral portion of the natural language query. The embodiment may also include generating a modified natural language query by automatically removing the boundaries and optionally replacing the one or more logically neutral phrases in the natural language query.

Abstract: A system may determine relevance prompts based on input documents and a relevance prompt template and may transmit the plurality of relevance prompts to a large language model for completion. The system may receive response messages including chunk relevance scores. The system may select a subset of the input documents based on the chunk relevance scores. The system may determine query response prompts including text from the selected input documents the natural language query, and a second set of natural language instructions to address the natural language query. The system may determine a response to the natural language query based on answers determined in response to the query response prompts.

Abstract: Techniques for using enhanced logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system and inputting the utterance into a machine-learning model including a series of network layers. A final network layer of the series of network layers can include a logit function. The machine-learning model can map a first probability for a resolvable class to a first logit value using the logit function. The machine-learning model can map a second probability for a unresolvable class to an enhanced logit value. The method can also include the chatbot system classifying the utterance as the resolvable class or the unresolvable class based on the first logit value and the enhanced logit value.

Abstract: Various embodiments of the disclosure provide an electronic device and a method of operating the same, the electronic device including: a speech recognition module; a memory configured to store information corresponding to a plurality of domains related to a collaborative task; and a processor operatively connected to the speech recognition module or the memory, wherein the processor is configured to receive a user voice from a user, analyze the received user voice using the speech recognition module to determine whether or not to perform a collaborative task, if the collaborative task is determined to be performed, select at least one participant related to the collaborative task, collect information related to the collaborative task from the user or an electronic device of the selected participant, and perform the collaborative task, based on the collected information. Various embodiments may be provided.

Abstract: A system, method and computer-readable storage device provides an improved speech processing approach in which hyper parameters used for speech recognition are modified dynamically or in batch mode rather than fixed statically. The method includes estimating, via a model trained on audio data and/or metadata, a set of parameters useful for performing automatic speech recognition, receiving speech at an automatic speech recognition system, applying, by the automatic speech recognition system, the set of parameters to processing the speech to yield text and outputting the text from the automatic speech recognition system.

Abstract: Methods, systems, and computer program products for active learning for natural language question answering are provided herein. A computer-implemented method includes generating a semantic signature of a natural language query; generating a SQL signature for a SQL query corresponding to the natural language query; determining whether a set of mappings includes a semantic signature matching the generated semantic signature, wherein each mapping in the set is between (i) a given semantic signature and (ii) a SQL signature representing a class of SQL queries corresponding to the given semantic signature; and in response to determining that the set of mappings does not include the generated semantic signature, adding a mapping between the generated semantic signature and the generated SQL signature to the set of mappings.

Abstract: Certain aspects of the present disclosure provide techniques for rendering visual artifacts in virtual worlds using machine learning models. An example method generally includes identifying, based on a machine learning model and a streaming natural language input, an intent associated with the streaming natural language input; generating, based on the identified intent associated with the streaming natural language input, one or more virtual objects for rendering in a virtual environment displayed on one or more displays of an electronic device; and rendering the generated one or more virtual objects in the virtual environment.

Abstract: Various embodiments are provided for intelligent application of operational rules to operational data in a computing environment by a processor. One or more operational rules may be extracted and formalized from a knowledge graph, a domain knowledge, or a combination thereof describing one or more operational policies and conditions. The one or more operational rules may be applied to operational data to identify and filter non-compliant operational data.

Abstract: Embodiments of the present disclosure relate to a solution for translation verification and correction. According to the solution, a neural network is trained to determine an association degree among a group of words in a source or target language. The neural network can be used for translation verification and correction. According to the solution, a group of words in a source language and translations of the group of words in a target language are obtained. An association degree among the group of words and an association degree among the translations can be determined by using the trained neural network. Then, whether there is a wrong translation can be determined based on the association degrees. In some embodiments, corresponding methods, systems and computer program products are provided.

Abstract: Systems and methods for using a generative artificial intelligence (AI) model to generate a suggested draft reply to a selected message. A message generation system and method are described that use inferred context to improve the suggested draft reply message for the user. Various message data and additional context are obtained and included in a prompt provided to the AI model to improve suggested content. In some examples, the message data and additional context include a message thread history and previously sent messages, profile information of the sender and recipient(s) of the selected message, known relationship information between the sender and the user, etc. For instance, the user's preferred communication style and talking points can be inferred based on the profile data, relationship data, and the user's past communications with similar participants and used to tailor the suggested draft reply to the user.

Abstract: Computing units provided at local sites or edge locations are programmed to execute conversational tools that generate pertinent, domain-specific responses to queries received from workers at such sites or locations. The conversational tools are large language models that are trained with domain-specific knowledge documents. Data representing queries are received from workers at such sites or locations and provided as inputs to the conversational tools along with text representing nearest knowledge documents from a knowledge base associated with the domain, as well as contextual data. Responses identified based on outputs received from the conversational tools in response to the inputs are provided to the workers that generated the queries.

Abstract: Aspects discussed herein may relate to using machine learning models as part of methods and techniques for ingesting, creating, storing, editing, and managing a document. The document may be a legal contract that includes one or more clauses. Among other things, one or more machine learning models may be configured to recognize clauses and/or classifications, or types, of clauses. For example, the one or more generative language models may be used to generate one or more recommended edits to a clause, generate one or more suggested clauses that are missing from the contract, and/or generate one or more suggested locations where a clause may be inserted into or moved within the contract.

Abstract: Systems and methods for pre-fetching results from large language models (LLMs) are provided. The method includes acquiring a context of an interaction between a user and an Artificial Intelligence (AI) character; predicting, based on the context, one or more anticipated words to be uttered by the user; generating, based on the one or more anticipated words, at least one query to an LLM; providing the at least one query to the LLM; generating, based on at least one response obtained from the LLM, an anticipated reply of the AI character model to the one or more anticipated words to be pronounced by the user; receiving one or more words uttered by the user; determining that a level of a discrepancy between the one or more words and the one or more anticipated words is below a predetermined threshold; and providing the anticipated reply to the user.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

Abstract: Systems and methods are presented for inferring an embedding vector of an item of a first type into the embedding space. Upon receiving a first time for which there is no embedding vector, documents of a document corpus that include (co-occurrence) both the received item and other items of the same type are identified. Of those other items that have embedding vectors, those embedding vectors are retrieved and averaged. The resulting averaged embedding vector is established as an inferred embedding vector for the received item.

Abstract: To improve prediction accuracy of utterance types in a dialog. A learning data generation device (10) according to the present invention comprises: a sort unit (11) configured to perform, based on information appended to utterances in a dialog amongst more than one speaker and that is indicative of a dialogue scene that is a scene in which the utterances in the dialog were made, sorting regarding whether the utterances are to be targets for generation of the learning data, wherein the sorter (11) is configured to exclude utterances of a dialogue scene that includes utterances similar to utterance of the particular type from the targets for generation of learning data.

Abstract: Systems and methods for observation-based training of an Artificial Intelligence (AI) character model are provided. An example method includes receiving log data including interactions of a user and a first AI character model, receiving internal parameters of a second AI character model including a first plurality of heuristic machine learning models and a second plurality of primary machine learning models, pre-processing the log data to obtain one or more data streams including behavioral characteristics of the user, running the one or more data streams through the first plurality of heuristic machine learning models to produce intermediate outputs, composing the intermediate outputs into templated formats, and providing the templated formats to the second plurality of primary machine learning models. The internal parameters of the second AI character model are adjusted based on the templated formats such that the second AI character model mimics the behavioral characteristics of the user.

Abstract: A method, apparatus and computer program product for establishing a logical connection between an indirect utterance and a transaction is described. An indirect utterance is received from a user as an input to a conversational system. The indirect utterance is parsed to a first logical form. A first set of predicates and terms is mapped from the first logical form to a first subgraph in a knowledge graph. A second set of predicates and terms is mapped from a second logical form belonging to a transaction to a second subgraph of the knowledge graph. A best path in the knowledge graph between the first subgraph and the second subgraph is searched for while transforming the first logical form using the node and edge labels along the best path to generate an intermediate logical form. A system action is performed for a transaction if a graph structure of the intermediate logical form matches the graph structure of the logical form of the transaction above a threshold.

Abstract: A method for intelligent editing of legal documents is described. The method may include receiving a rule, accessing a plurality of documents, and generating a list of tokens as a function of the rule and the plurality of documents. The method may also include ranking each token of the list of tokens, wherein a ranking of each token is a function of a frequency of occurrence in the plurality of documents, accessing a user inputted legal text, and suggesting one or more alterations to the user inputted legal text as a function of the ranked list of tokens. An apparatus for intelligent editing of legal documents is also described.

Abstract: By formulizing a specific company's internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. The ontology is built on the premise that meaningful terms are detected in the corpus and then classified according to specific semantic concepts, or entities. Once the main terms are defined, direct relations or linkages can be formed between these terms and their associated entities. Then, the relations are grouped into themes, which are groups or abstracts that contain synonymous relations. The disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. The ontology programming automatically trains itself to understand the domain or environment of the communication data by processing and analyzing a defined corpus of communication data.

Abstract: The invention concerns linguistic analysis. In particular the invention involves a method of operating a computer to perform linguistic analysis. In another aspect the invention is a computer system which implements the method, and in a further aspect the invention is software for programming a computer to perform the method.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines using “explanation” communication goals in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired explanation communication goal such that the narratives will express various ideas that are deemed relevant to a given explanation communication goal.