Abstract: This invention comprises a series of steps which transforms one or more natural language expressions into a single, well-formed formal language representation. Each natural language expression is partially parsed into simple fragments, each of which is then associated with one or more short formal expressions. Each formal expression is constructed in such a way as to contain one or more placeholder variables, each of which is associated with one or more attributes to constrain the types of entities that each variable can potentially represent. The resulting plurality of formal expressions is then filtered for relevance within a given context, and the surviving expressions manipulated based upon a plurality of rules, which are cognizant of the attributes associated with each variable contained therein. A user is then presented with the resulting plurality of formal expressions, whereupon the user optionally selects, rejects, adds to, logically connects and otherwise manipulates each member of said plurality.

Abstract: A method, system and computer program product for identifying documents of interest. A profile of a subscriber is created based on information obtained about the subscriber. Subscriber-interest determination rules are used to identify potential topics of interest of the subscriber based on the subscriber's profile as well as based on external knowledge sources. Each potential interest of the subscriber may be represented by a pointer that references a concept. Additionally, concepts in the documents published by the publishers are identified. A comparison may be made between the concepts identified in the documents published by the publishers with those concepts representing the potential topics of interests of the subscriber. Those documents with matching concepts may then be identified as potentially being of interest for the subscriber. In this manner, documents of interest are more accurately identified for the document seeker.

Abstract: A system and method for producing semantically-rich representations of texts to amplify and sharpen the interpretations of texts. The method relies on the fact that there is a substantial amount of semantic content associated with most text strings that is not explicit in those strings, or in the mere statistical co-occurrence of the strings with other strings, but which is nevertheless extremely relevant to the text. This additional information is used to both sharpen the representations derived directly from the text string, and also to augment the representation with content that, while not explicitly mentioned in the string, is implicit in the text and, if made explicit, can be used to support the performance of text processing applications including document indexing and retrieval, document classification, document routing, document summarization, and document tagging.