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: 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.