A system includes a content extraction engine comprising at least one processor and configured to receive a content page for a target product including product data for the target product and noise content unrelated to the target product, identify noise content pertaining to data unrelated to the target product, remove noise content from the content page, thereby generating a remainder content page containing target product data usable to enable product comparison between multiple sources.

A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot. The flow generator and enhancer may generate an auto-generated conversational dialog flow for upgrading the executable chatbot.

Solution automation & interface analysis components can be implemented in many ways, such as by specifying input/outputs & training a learning (generate, test & update) algorithm on the input/output data to generate a prediction function, to replace code connecting input & outputs.

Alternatively, additional specific example structure (like code/configuration/data) implementations to connect input/outputs of sub-tasks like core interaction functions & problem-solving intents to implement solution automation & interface analysis are included in the specification of this invention.

Solution automation & interface analysis components can be implemented in many ways, such as by specifying input/outputs & training a learning (generate, test & update) algorithm on the input/output data to generate a prediction function, to replace code connecting input & outputs.

Alternatively, additional specific example structure (like code/configuration/data) implementations to connect input/outputs of sub-tasks like core interaction functions & problem-solving intents to implement solution automation & interface analysis are included in the specification of this invention.

A method implemented in a data processing system includes receiving a plurality of text strings. A plurality of rules are applied to the text strings. If a condition specified in one of the rules exists in a given text string, one or more attributes are associated to that text string as metadata. One or more of the text strings are selected, using the metadata, as a potential title for the content. A final title is prepared based on the potential title, and the content is published online under the final title.

A method is provided for creating a lexical tree from a statement in a natural language. The method is implemented by a natural-language processing module. The method includes: receiving a statement in natural language in the form of a string of characters; iteratively processing the statement as a function of at least one processing parameter and one ontological dictionary, delivering at least one relational graph corresponding to at least one lexical item included in the statement in natural language; and creating a data structure at output having all possible combinations of the lexical items of the statement in natural language on the basis of the at least one relational graph.

A mechanism is provided in a data processing system having a processor and a memory storing instructions for implementing a natural language processing engine, a store of semantic types, and a store of units, conversions among units, and variants of unit names, for generating semantically equivalent variants of a natural language term. The mechanism receives an input term for variant analysis. The natural language processing engine executing on the data processing system identifies a semantic type of the input term based on the store of semantic types. The natural language processing engine extracts a quantity and a unit from the input term based on the store of units, conversions among units, and variants of unit names. The natural language processing engine populates type-specific templates at a level of specificity based on the input term based on the identified semantic type of the input term and the extracted quantity and unit to form a set of semantically equivalent variants of the input term. The natural language processing engine performs a natural language processing operation using the input term and the set of semantically equivalent variants of the input term.

A mechanism is provided in a data processing system having a processor and a memory storing a store of semantic types and instructions for implementing a natural language processing engine for generating semantically equivalent variants of a natural language term. The mechanism receives an input term for variant analysis. The natural language processing engine executing on the data processing system identifies a semantic type of the input term based on a store of semantic types. The natural language processing engine performs a type-specific series of rule-based expansions of the input term based on the identified semantic type of the input term to form a set of semantically equivalent variants of the input term. The natural language processing engine performs a natural language processing operation using the input term and the set of semantically equivalent variants of the input term.

Systems and methods that offer significant improvements to current chatbot conversational experiences are disclosed. The proposed systems and methods are configured to manage conversations in real-time with human customers based on a dynamic and unscripted conversation flow with a virtual assistant. In one embodiment, a knowledge graph or domain model represents the sole or primary source of information for the virtual assistant, thereby removing the reliance on any form of conversational modelling. Based on the information provided by the knowledge graph, the virtual agent chatbot will be equipped to answer customer queries, as well as demonstrate reasoning, offering customers a more natural and efficacious dialogue experience.

An information search system, including: a database (12); a query sentence acceptance unit (26) that accepts a query sentence; an inputted search keyword extractor (44) that extracts an inputted search keyword from the query sentence; a shared keyword dictionary (30) in which relevant keywords are registered in association with each other; a local keyword dictionary (102) in which district keywords used in particular districts are registered; a candidate search keyword reader (32) that reads out a keyword that is relevant to the inputted search keyword; and a retrieval executor (40) that executes retrieval processing from the database using the inputted search keyword, wherein, in a case in which the inputted search keyword is not registered in the local keyword dictionary, the candidate search keyword reader refers to the shared keyword dictionary, so as to read out a keyword that is relevant to the inputted search keyword.