Abstract: User feedback may be analyzed with semantic hierarchies. In some instances, the user feedback includes free-form text. The user feedback may be mapped to one or more semantic hierarchies that include multiple levels of nodes, where each node corresponds to a class. Information of the one or more semantic hierarchies may be mapped to an ontology model. The mapped information of the ontology model may be used to identify an actionable item for the user feedback, such as a problem, suggestion, question, or other issue. Information regarding the actionable item may be made available to an individual for evaluation of the actionable item.

Abstract: Free-form text in a document can be analyzed using natural-language processing to determine actionable items specified by users in the text or to provide recommendations, e.g., by automatically analyzing texts from multiple users. Words or phrases of the text can be mapped to classes of a model. An actionable item can be determined using the mapped words or phrases that match a selected grammar pattern. Items can be ranked, e.g., based on frequency across multiple documents. In some examples, the classes can include a suggestion-indicator class or a modal-indicator class, and the selected grammar pattern can include one of those classes. In some examples, the mapping can use a dictionary. A new term not in the dictionary can be automatically associated with classes based on attributes of the new term and of terms in the dictionary, e.g., the new term's part of speech or neighboring terms.

Abstract: User feedback may be analyzed with semantic hierarchies. In some instances, the user feedback includes free-form text. The user feedback may be mapped to one or more semantic hierarchies that include multiple levels of nodes, where each node corresponds to a class. Information of the one or more semantic hierarchies may be mapped to an ontology model. The mapped information of the ontology model may be used to identify an actionable item for the user feedback, such as a problem, suggestion, question, or other issue. Information regarding the actionable item may be made available to an individual for evaluation of the actionable item.

Abstract: Free-form text in a document can be analyzed using natural-language processing to determine actionable items specified by users in the text or to provide recommendations, e.g., by automatically analyzing texts from multiple users. Words or phrases of the text can be mapped to classes of a model. An actionable item can be determined using the mapped words or phrases that match a selected grammar pattern. Items can be ranked, e.g., based on frequency across multiple documents. In some examples, the classes can include a suggestion-indicator class or a modal-indicator class, and the selected grammar pattern can include one of those classes. In some examples, the mapping can use a dictionary. A new term not in the dictionary can be automatically associated with classes based on attributes of the new term and of terms in the dictionary, e.g., the new term's part of speech or neighboring terms.