Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: Text is intelligently annotated by first creating a topic map summarizing topics of interest of the user. A data structure is created. The topic map is used to create two linked user dictionaries, a topic dictionary reflecting topic names and a traversal dictionary reflecting the knowledge structure of a topic. Actions may be linked with topic types. When the text to be annotated is being read, the topic data structure of the topics found in the text are automatically instantiated using the dictionaries and any actions previously linked to topic types. Instantiated topic data structures are automatically attached to the text being annotated. A user GUI may be created to allow the user to access and interact with the text annotations.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: The present specification relates to Ontology modeling, and, more specifically, to systems and methods for populating a triple store (RDF Graph) data structure from a parse tree diagram and producing a measurable increased degree of confidence in the reliability of the inferences based on the matched axioms derived from the ontology model. The steps of populating and producing can be performed automatically.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A method for processing a bidirectional text is described. The method includes: dividing the text into a set of words; determining a first parameter representing a number of non-bidirectional words in the text, a second parameter representing a number of bidirectional words in the text, a third parameter representing a number of non-bidirectional words in reverse letter order in the text, and a fourth parameter representing a number of bidirectional words in reverse letter order in the text; and determining a text type attribute and/or a text orientation attribute of the bi-directional text from the values of the first parameter, of the second parameter, of the third parameter and the fourth parameters.

Abstract: A method for processing a bidirectional text is described. The method includes: dividing the text into a set of words; determining a first parameter representing a number of non-bidirectional words in the text, a second parameter representing a number of bidirectional words in the text, a third parameter representing a number of non-bidirectional words in reverse letter order in the text, and a fourth parameter representing a number of bidirectional words in reverse letter order in the text; and determining a text type attribute and/or a text orientation attribute of the bi-directional text from the values of the first parameter, of the second parameter, of the third parameter and the fourth parameters.

Abstract: Text is intelligently annotated by first creating a topic map summarizing topics of interest of the user. A data structure is created. The topic map is used to create two linked user dictionaries, a topic dictionary reflecting topic names and a traversal dictionary reflecting the knowledge structure of a topic. Actions may be linked with topic types. When the text to be annotated is being read, the topic data structure of the topics found in the text are automatically instantiated using the dictionaries and any actions previously linked to topic types. Instantiated topic data structures are automatically attached to the text being annotated. A user GUI may be created to allow the user to access and interact with the text annotations.