Abstract: A method (and structure) of extracting information from text, includes parsing an input sample of text to form a parse tree and using user inputs to define a machine-labeled learning pattern from the parse tree.

Abstract: A method (and structure) of extracting information from text, includes parsing an input sample of text to form a parse tree and using user inputs to define a machine-labeled learning pattern from the parse tree.

Abstract: This invention is a business system and method to perform categorization (classification) of multimedia items and to make business decisions based on the categorization of the item. The multimedia items are comprised of a multitude of disparate information sources, in particular, visual information and textual information. Classifiers are induced based on combining textual and visual feature vectors. Textual features are the traditional ones. Visual features include, but are not limited to, color properties of key intervals and motion properties of key intervals. The visual feature vectors are determined in such a fashion that the vectors are sparse. The text and the visual representation vectors are combined in a systematic and coherent fashion. This vector representation of a media item lends itself to well-established learning techniques and can be used for multimedia item categorization. The resulting business system, subject of this invention, can be used for many purposes.

Abstract: Supervised learning is used to develop a computer program that can automatically route or respond to electronic communications assuming the existence of an appropriate formal scheme for categorizing incoming electronic communications. A method is described by which such a categorization scheme for electronic communications can be constructed. The method is based on an analysis of factors having an impact on the categorization scheme from both the business domain and the technology domain. The problem solved by this method is a new one that is only now emerging as automated methods of routing communications based on supervised learning are becoming feasible. Among other uses, this method may be practically employed as a disciplined way of carrying out consulting engagements that call for setting up and maintaining categorization schemes for electronic communications.

Abstract: This invention is a system and method to perform categorization (classification) of multimedia items. These items are comprised of a multitude of disparate information sources, in particular, visual information and textual information. Classifiers are induced based on combining textual and visual feature vectors. Textual features are the traditional ones, such as, word count vectors. Visual features include, but are not limited to, color properties of key intervals and motion properties of key intervals. The visual feature vectors are determined in such a fashion that the vectors are sparse. The vector components are features such as the absence or presence of the color green in spatial regions and the absence or the amount of visual flow in spatial regions of the media items. The text and the visual representation vectors are combined in a systematic and coherent fashion. This vector representation of a media item lends itself to well-established learning techniques.

Abstract: A method and apparatus make keyword selection and/or weighting as a function of a session history of user input in order to answer queries submitted by the user to a computer system by providing answers based on stored documents. The aim is to find the best answers by matching stored natural language documents both to the most recent query and to the most latest query in a context that captures the recent history interaction. To do this, answers are matched against a set of keywords extracted from the most recent query as well as a set of keywords extracted from those queries received since the last topic switch was detected. A central feature of the method is for the computer system implementing this method to maintain a session history for each user session history. Keywords are extracted from each query by a system implementing this method. A graded keyword list is a list of keywords paired with ages, which are indicators of how long ago in the session the user employed this keyword in a query.

Abstract: A method for iteratively drilling-down on a user's textual free-form natural language query uses a session history to interpret successive queries in the context of previous queries on a topic or topics and to detect an implicit switch in topic. By maintaining a session history of the user's free-form natural language input and by automatically determining whether there is a topic or context switch, the search process is substantially simplified and is more effective; that is, more accurate answers to a user's queries are found faster. In addition, as the system operates on free-form natural language input, automatically constructing the actual search expressions, the complexity of constructing successive search expressions is obviated. If the system determines the user is, according to its session history and tests, asking successive questions within a given topic or context, the system keeps searching within a previously determined given set of previous responses on that context or topic.

Abstract: A method to automatically categorize messages or documents containing text. The method of solution fits in the general framework of supervised learning, in which a rule or rules for categorizing data is automatically constructed by a computer on the basis of training data that has been labeled beforehand. More specifically, the method involves the construction of a linear separator: training data is used to construct for each category a weight vector w and a threshold t, and the decision of whether a hitherto unseen document d is in the category will depend on the outcome of the test wTx≧t, where x is a vector derived from the document d. The method also uses a set L of features selected from the training data in order to construct the numerical vector representation x of a document.

Abstract: A computerized system responds, not just to a single query issued by a user, but to a query in the context of a dialog with the user. The system, which is referred to as an interactive automated response system, consists of three principal components or subsystems, which are a text categorizer that assigns categories to text extracted from a dialog, a search system whose purpose is to match text extracted from a dialog with answers, and a dialog manager whose purpose is to maintain a user's session history, to decide what text should be sent to the text categorizer and to the search system, to make use of a partially ordered category scheme to categorize each stage of the dialog based on the results returned by the other components, and to use the results of dialog categorization, as well as the results returned by the other components to create suitable responses to the user's query in the context of his or her earlier queries.

Abstract: This invention is a system and method to perform categorization (classification) of multimedia items. These items are comprised of a multitude of disparate information sources, in particular, visual information and textual information. Classifiers are induced based on combining textual and visual feature vectors. Textual features are the traditional ones, such as, word count vectors. Visual features include, but are not limited to, color properties of key intervals and motion properties of key intervals. The visual feature vectors are determined in such a fashion that the vectors are sparse. The vector components are features such as the absence or presence of the color green in spatial regions and the absence or the amount of visual flow in spatial regions of the media items. The text and the visual representation vectors are combined in a systematic and coherent fashion. This vector representation of a media item lends itself to well-established learning techniques.

Abstract: A method to automatically categorize messages or documents containing text. The method of solution fits in the general framework of supervised learning, in which a rule or rules for categorizing data is automatically constructed by a computer on the basis of training data that has beforehand been categorized, i.e., each training data item has been labeled with the categories to which it belongs. More specifically, the method for rule induction involves the novel combination of (1) inducing from the training data a decision tree for each category, (2) automated construction from each decision tree of a simplified symbolic rule set that is logically equivalent overall to the decision tree, and which is to be used for categorization instead of the decision tree, and (3) determination of a confidence level for each rule.

Abstract: This invention is a business system and method to perform categorization (classification) of multimedia items and to make business decisions based on the categorization of the item. The multimedia items are comprised of a multitude of disparate information sources, in particular, visual information and textual information. Classifiers are induced based on combining textual and visual feature vectors. Textual features are the traditional ones. Visual features include, but are not limited to, color properties of key intervals and motion properties of key intervals. The visual feature vectors are determined in such a fashion that the vectors are sparse. The text and the visual representation vectors are combined in a systematic and coherent fashion. This vector representation of a media item lends itself to well-established learning techniques and can be used for multimedia item categorization. The resulting business system, subject of this invention, can be used for many purposes.

Abstract: A method and apparatus for interacting with incoming text information, e.g., a user query. The method of the present invention categorizes the incoming text information, and may also provide associated confidence levels with the categorization feature. The categorized information is used to query a database having associated category information with associated threshold values. The confidence levels may also be used when querying the database. The categorized information and corresponding confidence levels are then compared to the threshold value. If the categorized information and corresponding confidence levels equals or exceeds the threshold value then a response is provided by the system and method of the present invention.

Abstract: Supervised learning is used to develop a computer program that can automatically route or respond to electronic communications assuming the existence of an appropriate formal scheme for categorizing incoming electronic communications. A method is described by which such a categorization scheme for electronic communications can be constructed. The method is based on an analysis of factors having an impact on the categorization scheme from both the business domain and the technology domain. The problem solved by this method is a new one that is only now emerging as automated methods of routing communications based on supervised learning are becoming feasible. Among other uses, this method may be practically employed as a disciplined way of carrying out consulting engagements that call for setting up and maintaining categorization schemes for electronic communications.