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 computer-implemented method, a computer system and a computer program product create rules for a rule-based natural language interface for databases (NLIDB). The method may include receiving a natural language query from a user. The method may also include generating a first explanation for the natural language query using a deep learning model and a second explanation for the natural language query using the rule-based NLIDB and validating whether the first and second explanations correctly represent the natural language query. The method may further include identifying the database value in the first explanation in response to the first explanation correctly representing the natural language query and the second explanation not correctly representing the natural language query. Lastly, the method may include creating a rule in a table for the rule-based natural language interface for databases that associates the database value with the original word of the natural language query.

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 computer-implemented method, a computer system and a computer program product create rules for a rule-based natural language interface for databases (NLIDB). The method may include receiving a natural language query from a user. The method may also include generating a first explanation for the natural language query using a deep learning model and a second explanation for the natural language query using the rule-based NLIDB and validating whether the first and second explanations correctly represent the natural language query. The method may further include identifying the database value in the first explanation in response to the first explanation correctly representing the natural language query and the second explanation not correctly representing the natural language query. Lastly, the method may include creating a rule in a table for the rule-based natural language interface for databases that associates the database value with the original word of the natural language query.

Abstract: A natural language processor and applicable method receives an input sentence of natural language from a natural language corpus. The input sentence comprises sentence clauses that include a conditional clause. The processor performs natural language processing (NLP), using an NLP model, on the input sentence. The processing comprises using a set of rules determining the sentence clauses and which of the sentence clause is the conditional clause, determining one or more logical connections between the sentence clauses, and determining a role of the sentence clauses based upon the one or more identified logical connections. The sentence clauses are tagged to produce a labeled sentence that is output to an entity that is one or more of a storage device, a network interface, a storage device, and an input of a further language processor or application.

Abstract: A computer system extracts clauses using machine translation. An input sentence in a source language is translated into a translated sentence in a target language using a trained machine translation model, wherein the trained machine translation model inserts a grammatical indicator into a position of the translated sentence that identifies a dependent clause. The input sentence and the translated sentence are aligned to determine a position in the input sentence that corresponds to the position of the grammatical indicator in the translated sentence. The dependent clause is extracted, in the source language, from the input sentence based on the determined position in the input sentence. Embodiments of the present invention further include a method and program product for clause extraction using machine translation in substantially the same manner described above.

Abstract: A method for categorizing an issue includes, for each of a plurality of categories of issue, providing at least one discourse pattern for identify text sequences that meet the discourse pattern. At least one of the discourse patterns specifies that an instance of a domain term in a domain term vocabulary be present in the text sequence for the pattern to be met. An issue is received which includes a text sequence. The text sequence is categorized based on which, if any, of the discourse patterns are met by the text sequence of the received issue. Information based on the categorization of the text sequence is output.

Abstract: An apparatus and a method increase data exploration and facilitate changing between exploratory and iterative searching. A virtual widget is movable on a display device in response to detected user gestures. Graphic objects are displayed on the display device, representing respective documents in a search document collection. The virtual widget is populated with a first query term, which can be used for an iterative search. Semantic terms that are predicted to be semantically related to it are identified, based on a computed similarity between multidimensional representations of terms in a training document collection. The multidimensional representations are output by a semantic model which takes into account context of the respective terms in the training document collection. A user selects one of the set of semantic terms for generating a semantic query for an exploratory search. Documents in the search document collection that are responsive to the semantic query are identified.

Abstract: A method for categorizing an issue includes, for each of a plurality of categories of issue, providing at least one discourse pattern for identify text sequences that meet the discourse pattern. At least one of the discourse patterns specifies that an instance of a domain term in a domain term vocabulary be present in the text sequence for the pattern to be met. An issue is received which includes a text sequence. The text sequence is categorized based on which, if any, of the discourse patterns are met by the text sequence of the received issue. Information based on the categorization of the text sequence is output.