122 results about "Document level" patented technology

In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. A Bernoulli Process Topic (BPT) model is provided which models the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of the citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach.

Methods and systems provide for automatically performing actions on or in association with text or data strings that are recognized as belonging to certain semantic categories. Text entered by a user is passed to a recognizer application. If a given text or data string is recognized as belonging to a given semantic category, the recognizer application passes data corresponding to the recognized string back to a host application. In response to recognized text or data, a pointer to the object model of the host application may be passed to the recognizer application to allow the recognizer application to perform any function of the host application in response to the recognized string. Alternatively, after the recognizer application passes data corresponding to the recognized string back to the host application, the host application may fire an application level or document level event for causing an action component to perform desired actions on recognized strings. Alternatively, after a string is recognized by the recognizer application, the recognizer application may set a property associated with a desired action to be performed on or in association with the recognized string. The host application may call an action component identified by the property for automatically performing the desired action on or in association with the recognized string.

A method for entity recognition employs document-level entity tags which correspond to mentions appearing in the document, without specifying their locations. A named entity recognition model is trained on features extracted from text samples tagged with document-level entity tags. A text document to be labeled is received, the text document being tagged with at least one document-level entity tag. A document-specific gazetteer is generated, based on the at least one document-level entity tag. The gazetteer includes a set of entries, one entry for each of a set of entity names. For a text sequence of the document, features for tokens of the text sequence are extracted. The features include document-specific features for tokens matching at least a part of the entity name of one of the gazetteer entries. Entity labels are predicted for the tokens in the text sequence with the named entity recognition model, based on the extracted features.