International Business Machines Corporation

David E Johnson

Frank J Oles

Frederick J Damerau

Pnina A Vortman

Thomas A Hampp-Bahnmueller

Martin C Buskirk, Jr

Tong Zhang

Thilo W Goetz

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.

Method and system for extracting information from unstructured text using symbolic machine learning

A procedure automates the process of setting up an instance of a conversational natural language interface for a Web site. By automating the process of setting up a new Web site, the process enables a new interface to be created by anyone. Subsequent manual tuning of the interface is possible and much easier to do than creating an interface from scratch. In order to set up an instance of a natural language conversational interface, it is necessary to define a hierarchy of topics into which individual documents or Web pages can be classified, provide a keyword index for those documents for an associated search engine, and for each node in the hierarchy, specify a mechanism for associating an input natural language (NL) query to the node.

Automated set up of web-based natural language interface

A method and apparatus for providing summaries of documents belonging to a class of documents in a classified document collection. A sample set of documents belonging to one or more classes is processed via a machine learning system in order to induce a set of rules associated with the sample set of documents. The vocabulary in the rules are extracted and compared to words, terms or phrases of an incoming document. Any matches between the extracted rules and the words, terms or phrases of the incoming document are used as a summary for the incoming document. By using the method and apparatus, each document does not have to be processed to find most important words and the like in order to provide a summary for that document and then repeating the same process for additional documents.

Rule induction for summarizing documents in a classified document collection

A method of automatically labeling of unlabeled text data can be practiced independent of human intervention, but that does not preclude manual intervention. The method can be used to extract relevant features of unlabeled text data for a keyword search. The method of automated labeling of unlabeled text data uses a document collection as a reference answer set. Members of the answer set are converted to vectors representing centroids of unknown groups of unlabeled text data. Unlabeled text data are clustered relative to the centroids by a nearest neighbor algorithm and the ID of the relevant answer is assigned to all documents in the cluster. At this point in the process, a supervised machine learning algorithm is trained on labeled data, and a classifier for assigning labels to new text data is output. Alternatively, a feature extraction algorithm may be run on classes generated by the step of clustering, and search features output which index the unlabeled text data.

Automatic labeling of unlabeled text data

A rules based configurable system efficiently and effectively determines for a given electronically represented text document which linguistic analysis and extraction processes and which application specific processes should be invoked to provide more accurate answers to a user's query. In a rules based classifier, where each category or topic is represented by a set of rules, in an application such as routing, the categorization effecting the routing can be effectively combined with processes extracting other information. This may be in the form of a prompt for the user to input additional information.

Categorization based text processing

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. Graded keyword lists are maintained in the session history so the system can assign weights to keywords, with more recently received keywords being assigned higher weights than keywords with comparatively greater ages. The weights assigned to keywords are used in computing scores that indicate closeness of match of a document to a list of keywords.

Session-history-based recency-biased natural language document search

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. This effectively narrows the documents found allowing the user to quickly and accurately find just the documents of interest. If the system determines the user has implicitly changed context or topic, based on its session history and tests, it searches all the information at its disposal; i.e., all of the collections of documents.

Automatic topic identification and switch for natural language search of textual document collections

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.

Interactive automated response system

A framework for information extraction from natural language documents is application independent and provides a high degree of reusability. The framework integrates different Natural Language/Machine Learning techniques, such as parsing and classification. The architecture of the framework is integrated in an easy to use access layer. The framework performs general information extraction, classification/categorization of natural language documents, automated electronic data transmission (e.g., E-mail and facsimile) processing and routing, and plain parsing. Inside the framework, requests for information extraction are passed to the actual extractors. The framework can handle both pre- and post processing of the application data, control of the extractors, enrich the information extracted by the extractors. The framework can also suggest necessary actions the application should take on the data. To achieve the goal of easy integration and extension, the framework provides an integration (outside) application program interface (API) and an extractor (inside) API.

Architecture of a framework for information extraction from natural language documents

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. The method covers both decision-tree-based symbolic rule induction and the use for the purpose of document categorization of rules in the logical format of those generated by the rule induction procedure described herein.

Decision-tree-based symbolic rule induction system for text categorization

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. If the categorized information and corresponding confidence levels does not equal or exceed the threshold value then the system of the present invention will request further information of the user, and the method will repeat until (i) the categorized information and corresponding confidence levels equals or exceeds the threshold value or (ii) the system of the present invention times out.

Interactive electronic messaging system

A two stage electronic messaging system and method of use that automatically and accurately responds to user queries (e.g., input documents) using a two stage searching and retrieval system. The two stages system includes fast document-matching techniques (e.g., &ldquo;weak&rdquo; search techniques) in combination with more advanced categorization and text-search techniques (e.g., machine learning and other semi-automated techniques). The two stage searching and retrieval system may be used via the Internet, an intranet, LAN or other similar system, and may be used for providing requested information to a user (e.g., customer, employee, customer representative and the like) via a content rich web site, a propriety database or any computer related help system.

Growing community of inventors

Cortlandt Manor, NY, United States of America

David E Johnson