Abstract: Example aspects of the present disclosure are directed to systems and methods that employ a machine-learned opinion classification model to classify portions (e.g., sentences, phrases, paragraphs, etc.) of documents (e.g., news articles, web pages, etc.) as being opinions or not opinions. Further, in some implementations, portions classified as opinions can be considered for inclusion in an informational display. For example, document portions can be ranked according to importance and selected for inclusion in an informational display based on their ranking. Additionally or alternatively, for systems which access and consider multiple documents, the portions of a document that are classified as opinion can be compared to similarly-classified portions of other documents to perform document clustering, to ensure diversity within a presentation, and/or other tasks.

Abstract: Example aspects of the present disclosure are directed to systems and methods that employ a machine-learned opinion classification model to classify portions (e.g., sentences, phrases, paragraphs, etc.) of documents (e.g., news articles, web pages, etc.) as being opinions or not opinions. Further, in some implementations, portions classified as opinions can be considered for inclusion in an informational display. For example, document portions can be ranked according to importance and selected for inclusion in an informational display based on their ranking. Additionally or alternatively, for systems which access and consider multiple documents, the portions of a document that are classified as opinion can be compared to similarly-classified portions of other documents to perform document clustering, to ensure diversity within a presentation, and/or other tasks.

Abstract: Examples associated with identifying documents are disclosed. One example includes identifying at least one document in a corpus of documents that contains at least one token. The token is identified from a search query. Relevance of the search query to each identified document is determined according to a Helmholtz score for each respective identified token.

Abstract: Examples relate to providing keywords to generate policy conditions. Examples include a computing device to remove, from a corpus of documents, words that are common among classes in the corpus to create a reduced corpus. In some examples, the computing device is to identify a set of keywords for a particular one of the classes in the reduced corpus by identifying keywords that are common among documents in the particular class, and provide the set of keywords to generate a policy condition.