Abstract: Systems and methods for domain classification in natural language processing based on domains are disclosed. The method includes generating a trigram corpus for the purpose of classification based on a trigram analysis of a domain model containing a hierarchical ontology and semantic construction that maps patterns of semantic tokens to syntactic patterns. An input string is parsed within each domain, tokenized in each domain. The resulting trigrams for the input text in each domain are looked up in the corresponding trigram corpus to determine the relevancy of each domain to the input text. The input string is thus classified based on the relevancy determination. The systems and methods avoids having to rely on existing annotated domain corpora for classification and allows for fast regeneration of the classifier when domain models are under frequent update and development.

Abstract: Systems and methods of natural language processing in an environment with no existing corpus are disclosed. The method includes defining an input grammar specific to a chosen domain, the input grammar having a domain specific knowledge and general grammatical knowledge. Groups of tokens are identified within the input grammar having syntactic and semantic equivalence. The identified groups are assembled into hypernyms, wherein the hypernyms include a semantic output for each token in the hypernyms. A list of fields is then combined with the hypernyms for combination with the hypernyms. A corpus of possible combinations of hypernyms and fields is created. A data structure mapping each possible combination to a partial semantic output is generated and the data structure is saved for use in later processing.

Abstract: A system and method for processing a natural language input. The natural language input is initially processed through a strict parser, which results in an initial parse. The initial parse is evaluated to determine if it exceeds a completeness threshold. If the initial parse does not pass the completeness threshold, any open slots are filled using type based slot filling. The final parse is then output for further processing by another system, such as a search engine.

Abstract: Systems and methods of natural language processing in an environment with no existing corpus are disclosed. The method includes defining an input grammar specific to a chosen domain, the input grammar having a domain specific knowledge and general grammatical knowledge. Groups of tokens are identified within the input grammar having syntactic and semantic equivalence. The identified groups are assembled into hypernyms, wherein the hypernyms include a semantic output for each token in the hypernyms. A list of fields is then combined with the hypernyms for combination with the hypernyms. A corpus of possible combinations of hypernyms and fields is created. A data structure mapping each possible combination to a partial semantic output is generated and the data structure is saved for use in later processing.

Abstract: A system and method for processing a natural language input. The natural language input is initially processed through a strict parser, which results in an initial parse. The initial parse is evaluated to determine if it exceeds a completeness threshold. If the initial parse does not pass the completeness threshold, any open slots are filled using type based slot filling. The final parse is then output for further processing by another system, such as a search engine.

Abstract: A system and method for processing a natural language input. The natural language input is initially processed through a strict parser, which results in an initial parse. The initial parse is evaluated to determine if it exceeds a completeness threshold. If the initial parse does not pass the completeness threshold, any open slots are filled using type based slot filling. The final parse is then output for further processing by another system, such as a search engine.

Abstract: Systems and methods for domain classification in natural language processing based on domains are disclosed. The method includes generating a trigram corpus for the purpose of classification based on a trigram analysis of a domain model containing a hierarchical ontology and semantic construction that maps patterns of semantic tokens to syntactic patterns. An input string is parsed within each domain, tokenized in each domain. The resulting trigrams for the input text in each domain are looked up in the corresponding trigram corpus to determine the relevancy of each domain to the input text. The input string is thus classified based on the relevancy determination. The systems and methods avoids having to rely on existing annotated domain corpora for classification and allows for fast regeneration of the classifier when domain models are under frequent update and development.

Abstract: A system and method for processing a natural language input. The natural language input is initially processed through a strict parser, which results in an initial parse. The initial parse is evaluated to determine if it exceeds a completeness threshold. If the initial parse does not pass the completeness threshold, any open slots are filled using type based slot filling. The final parse is then output for further processing by another system, such as a search engine.

Abstract: Systems and methods for domain classification in natural language processing based on domains are disclosed. The method includes generating a trigram corpus for the purpose of classification based on a trigram analysis of a domain model containing a hierarchical ontology and semantic construction that maps patterns of semantic tokens to syntactic patterns. An input string is parsed within each domain, tokenized in each domain. The resulting trigrams for the input text in each domain are looked up in the corresponding trigram corpus to determine the relevancy of each domain to the input text. The input string is thus classified based on the relevancy determination. The systems and methods avoids having to rely on existing annotated domain corpora for classification and allows for fast regeneration of the classifier when domain models are under frequent update and development.

Abstract: A system and method for processing a natural language input. The natural language input is initially processed through a strict parser, which results in an initial parse. The initial parse is evaluated to determine if it exceeds a completeness threshold. If the initial parse does not pass the completeness threshold, any open slots are filled using type based slot filling. The final parse is then output for further processing by another system, such as a search engine.

Abstract: Systems and methods for domain classification in natural language processing based on domains are disclosed. The method includes generating a trigram corpus for the purpose of classification based on a trigram analysis of a domain model containing a hierarchical ontology and semantic construction that maps patterns of semantic tokens to syntactic patterns. An input string is parsed within each domain, tokenized in each domain. The resulting trigrams for the input text in each domain are looked up in the corresponding trigram corpus to determine the relevancy of each domain to the input text. The input string is thus classified based on the relevancy determination. The systems and methods avoids having to rely on existing annotated domain corpora for classification and allows for fast regeneration of the classifier when domain models are under frequent update and development.

Abstract: A system and method for processing a natural language input. The natural language input is initially processed through a strict parser, which results in an initial parse. The initial parse is evaluated to determine if it exceeds a completeness threshold. If the initial parse does not pass the completeness threshold, any open slots are filled using type based slot filling. The final parse is then output for further processing by another system, such as a search engine.