Abstract: Performing an operation comprising determining that a parse of an input string comprising a plurality of tokens is incomplete, generating, based on a machine learning (ML) model: (i) a plurality of candidate addition tokens for adding to the input string, and (ii) a plurality of candidate removal tokens for removing from the input string, selecting, from the plurality of candidate addition tokens and the plurality of candidate removal tokens, a first candidate token, and modifying the input string based on the first candidate token to facilitate a complete parse of the modified input string by a parser.

Abstract: A phrase that includes a trigger word that modifies a meaning within the phrase is received. The trigger word is identified. The words of the phrase that are modified by the trigger word are identified by analyzing features of the phrase that link the trigger word to other words. The phrase is interpreted by modifying the second subset of words according to the modification of the trigger word.

Abstract: Techniques for cognitive document quality determination and automated heuristic generation are provided. A plurality of documents is received, where each of the plurality of documents contains natural language text. A plurality of values is determined for a first plurality of predefined attributes of the plurality of documents. A plurality of quality scores is generated for the plurality of documents by processing the plurality of values using a machine learning model, where the plurality of quality scores indicate a suitability of each of the plurality of documents to be processed using a target processing operation. A subset of documents is identified from the plurality of documents having respective quality scores below a predefined threshold. The subset of documents is flagged for further processing. At least one document of the plurality of documents that is not flagged is selectively processed using the target processing operation.

Abstract: A mechanism is provided for improving predicate parses (or logical representations of a passage) using semantic knowledge. In response to encountering an ambiguous decision point during a syntactic analysis of a portion of natural language content, a candidate meaning of the ambiguous decision point is generated. Characteristics of the ambiguous decision point are evaluated based on a semantic knowledge base to determine a semantic meaning associated with the ambiguous decision point. A determination is made as to whether the semantic meaning supports or refutes the candidate meaning. In response to determining that the semantic meaning refutes the candidate meaning, the candidate meaning of the ambiguous decision point is overridden based on the semantic meaning to include the semantic meaning as a final meaning for the ambiguous decision point. The portion of natural language content is then processed based on the final meaning for the ambiguous decision point.

Abstract: A technique of improving input text processing in a data processing system that answers questions includes receiving a question from a user. The question has associated supporting original input text that is utilized in answering the question. Corrective measures are applied to the supporting original input text to create supporting revised input text. The supporting revised input text documents corrective measures applied to the supporting original input text. An answer to the question is generated based on, at least in part, the supporting revised input text. The supporting original input text is recreated from the supporting revised input text based on the documented corrective measures in the supporting revised input text subsequent to generating the answer. The answer and the supporting original input text as recreated are transmitted to the user.

Abstract: Provided are a computer program product, system, and method for classifying text to determine a goal type used to select machine learning algorithm outcomes. Natural language processing of text is performed to determine features in the text and their relationships. A classifier classifies the text based on the relationships and features to determine a goal type. The determined features and relationships from the text are inputted into a plurality of different machine learning algorithms to generate outcomes. For each of the machine learning algorithms, a determination is made of performance measurements resulting from the machine learning algorithms generating the outcomes. A determination is made of at least one machine learning algorithm having performance measurements that are highly correlated to the determined goal type. An outcome is determined from at least one of the outcomes.

Abstract: Provided are a computer program product, system, and method for classifying text to determine a goal type used to select machine learning algorithm outcomes. Natural language processing of text is performed to determine features in the text and their relationships. A classifier classifies the text based on the relationships and features to determine a goal type. The determined features and relationships from the text are inputted into a plurality of different machine learning algorithms to generate outcomes. For each of the machine learning algorithms, a determination is made of performance measurements resulting from the machine learning algorithms generating the outcomes. A determination is made of at least one machine learning algorithm having performance measurements that are highly correlated to the determined goal type. An outcome is determined from at least one of the outcomes.

Abstract: Performing an operation comprising determining that a parse of an input string comprising a plurality of tokens is incomplete, generating, based on a machine learning (ML) model: (i) a plurality of candidate addition tokens for adding to the input string, and (ii) a plurality of candidate removal tokens for removing from the input string, selecting, from the plurality of candidate addition tokens and the plurality of candidate removal tokens, a first candidate token, and modifying the input string based on the first candidate token to facilitate a complete parse of the modified input string by a parser.

Abstract: A mechanism is provided for improving predicate parses (or logical representations of a passage) using semantic knowledge. In response to encountering an ambiguous decision point during a syntactic analysis of a portion of natural language content, a candidate meaning of the ambiguous decision point is generated. Characteristics of the ambiguous decision point are evaluated based on a semantic knowledge base to determine a semantic meaning associated with the ambiguous decision point. A determination is made as to whether the semantic meaning supports or refutes the candidate meaning. In response to determining that the semantic meaning refutes the candidate meaning, the candidate meaning of the ambiguous decision point is overridden based on the semantic meaning to include the semantic meaning as a final meaning for the ambiguous decision point. The portion of natural language content is then processed based on the final meaning for the ambiguous decision point.

Abstract: First content containing a plurality of list items in one or more lists can be parsed for conjunctions and implied list indicators. One or more modifications can occur at one or more conjunctions or implied list indicators. The one or more modifications can comprise one or more of expanding text, contracting text, and replacing text. The one or more modifications can generate second content conducive to natural language processing operations.

Abstract: First content containing a plurality of list items in one or more lists can be parsed for conjunctions and implied list indicators. One or more modifications can occur at one or more conjunctions or implied list indicators. The one or more modifications can comprise one or more of expanding text, contracting text, and replacing text. The one or more modifications can generate second content conducive to natural language processing operations.

Abstract: A mechanism is provided for improving predicate parses (or logical representations of a passage) using semantic knowledge. In response to encountering an ambiguous decision point during a syntactic analysis of a portion of natural language content, a candidate meaning of the ambiguous decision point is generated. Characteristics of the ambiguous decision point are evaluated based on a semantic knowledge base to determine a semantic meaning associated with the ambiguous decision point. A determination is made as to whether the semantic meaning supports or refutes the candidate meaning. In response to determining that the semantic meaning refutes the candidate meaning, the candidate meaning of the ambiguous decision point is overridden based on the semantic meaning to include the semantic meaning as a final meaning for the ambiguous decision point. The portion of natural language content is then processed based on the final meaning for the ambiguous decision point.

Abstract: A mechanism is provided for improving predicate parses (or logical representations of a passage) using semantic knowledge. In response to encountering an ambiguous decision point during a syntactic analysis of a portion of natural language content, a candidate meaning of the ambiguous decision point is generated. Characteristics of the ambiguous decision point are evaluated based on a semantic knowledge base to determine a semantic meaning associated with the ambiguous decision point. A determination is made as to whether the semantic meaning supports or refutes the candidate meaning. In response to determining that the semantic meaning refutes the candidate meaning, the candidate meaning of the ambiguous decision point is overridden based on the semantic meaning to include the semantic meaning as a final meaning for the ambiguous decision point. The portion of natural language content is then processed based on the final meaning for the ambiguous decision point.

Abstract: A computer processor may receive medical data including a report and an image. The computer processor may analyze the report using natural language processing to identify a condition and a corresponding criterion. The computer processor may also analyze the image using an image processing model to generate an image analysis. The computer processor may determine whether the report has a potential problem by comparing the image analysis to the criterion.

Abstract: A computer processor may receive medical data including a report and an image. The computer processor may analyze the report using natural language processing to identify a condition and a corresponding criterion. The computer processor may also analyze the image using an image processing model to generate an image analysis. The computer processor may determine whether the report has a potential problem by comparing the image analysis to the criterion.

Abstract: First content containing a plurality of list items in one or more lists can be parsed for conjunctions and implied list indicators. One or more modifications can occur at one or more conjunctions or implied list indicators. The one or more modifications can comprise one or more of expanding text, contracting text, and replacing text. The one or more modifications can generate second content conducive to natural language processing operations.

Abstract: A technique of improving input text processing in a data processing system that answers questions includes receiving a question from a user. The question has associated supporting original input text that is utilized in answering the question. Corrective measures are applied to the supporting original input text to create supporting revised input text. The supporting revised input text documents corrective measures applied to the supporting original input text. An answer to the question is generated based on, at least in part, the supporting revised input text. The supporting original input text is recreated from the supporting revised input text based on the documented corrective measures in the supporting revised input text subsequent to generating the answer. The answer and the supporting original input text as recreated are transmitted to the user.

Abstract: A computer processor may receive medical data including a report and an image. The computer processor may analyze the report using natural language processing to identify a condition and a corresponding criterion. The computer processor may also analyze the image using an image processing model to generate an image analysis. The computer processor may determine whether the report has a potential problem by comparing the image analysis to the criterion.

Abstract: A computer processor may receive medical data including a report and an image. The computer processor may analyze the report using natural language processing to identify a condition and a corresponding criterion. The computer processor may also analyze the image using an image processing model to generate an image analysis. The computer processor may determine whether the report has a potential problem by comparing the image analysis to the criterion.

Abstract: A mechanism is provided in a question answering system for duration similarity weighting in a question answering system. The question answering system receives an input question. The question answering system generates a plurality of candidate answers to the input question using a corpus of information. The question answering system identifies a question-side duration value for the input question and identifies a policy-side duration value for a policy to be applied to an entity identified in the input question. The question answering system determines a duration similarity weight for each of the plurality of candidate answers. The question answering system applies the duration similarity weight to each of the plurality of candidate answers. The question answering system ranks the plurality of candidate answers and outputs one or more of the candidate answers.