Abstract: A system and method for providing an adaptive Interaction Logging functionality to help agents reduce the time spent documenting contact center interactions. In a preferred embodiment the system uses a pipeline comprising audio capture of a telephone conversation, automatic speech transcription, text normalization, transcript generation and candidate call log generation based on Real-time and Global Models. The contact center agent edits the candidate call log to create the final call log. The models are updated based on analysis of user feedback in the form of the editing of the candidate call log done by the contact center agents or supervisors. The pipeline yields a candidate call log which the agents can edit in less time than it would take them to generate a call log manually.

Abstract: A system and method for providing an adaptive Interaction Logging functionality to help agents reduce the time spent documenting contact center interactions. In a preferred embodiment the system uses a pipeline comprising audio capture of a telephone conversation, automatic speech transcription, text normalization, transcript generation and candidate call log generation based on Real-time and Global Models. The contact center agent edits the candidate call log to create the final call log. The models are updated based on analysis of user feedback in the form of the editing of the candidate call log done by the contact center agents or supervisors. The pipeline yields a candidate call log which the agents can edit in less time than it would take them to generate a call log manually.

Abstract: Topicality scores are determined for a number of phrasal expressions in documents. Phrasal expressions may be noun phrases, with or without corresponding prepositional phrases, subject-verb pairs, and verb-object pairs. The documents describe some topic or multiple topics. Techniques can be used to determined how the phrasal expression compares with the topic or topics being described in the documents. Specificities are determined for the phrasal expressions. Techniques may be used to determine whether phrasal expressions are more or less specific than other phrasal expressions. An order is determined for the phrasal expressions by using the topicality scores and the specificities. The order may be represented as a phrasal expression tree, for example. The phrasal expression tree may be displayed to a user, and the user can navigate through the phrasal expression tree, and therefore through the one or more documents.

Abstract: To identify and describe one or more topics in one or more documents in a document set, a term set process creates a basic term set from the document set where the term set comprises one or more basic terms of one or more words in the document. A document vector process then creates a document vector for each document. The document vector has a document vector direction representing what the document is about. A topic vector process then creates one or more topic vectors from the document vectors. Each topic vector has a topic vector direction representing a topic in the document set. A topic term set process creates a topic term set for each topic vector that comprises one or more of the basic terms describing the topic represented by the topic vector. Each of the basic terms in the topic term set associated with the relevancy of the basic term. A topic-document relevance process creates a topic-document relevance for each topic vector and each document vector.

Abstract: Two or more client users, connected by one or more networks to the server, cooperatively search a database. The server has a data structure that has two or more cooperative user identifiers. Each cooperative user identifier represents one of the clients that has indicated a desired to establish a cooperative search. The data structure further has a session identifier that associates two or more of the cooperative user identifiers as session participants in an established a cooperative session. A command process, executing on the server, receives a query from one of the session participants (clients), accesses results of the query from a search engine, and distributes the results to all of the session participants. Queries of the cooperative session are related to indexed terms so that future uses will find this relationship when using similar queries. These are applications in searching for sales and service information.

Abstract: Descriptive canonical forms of entity types are created by scanning one or more documents in a database of a computer system to identify one or more proper names that appear in the documents as raw names. Each of the raw names has zero or more proper names, zero or more medial substrings, zero or more leading substrings, and zero or more trailing substrings. The raw names of one or more documents are "cleaned" and "split" until certain "cleaning and splitting conditions" are no longer met to obtain a list of clean and split candidate names. Anchor names are selected from the list that unambiguously represent an entity type. The anchor names have one or more entity-type attribute values. Variant names, clean and split candidate names having one or more shared attribute (values) with the anchor name, are combined with the anchor name to create an equivalence group of names that refer to the same entity. A canonical form is generated for the group from a subset of the anchor name attributes.

Abstract: In an information retrieval system, a query issued by the user is analyzed by a query engine into query elements. After the query has been evaluated against the document collections, a resulting hit list is presented to the user, e.g., as a table. The presented hit list displays not only an overall rank of a document but also a contribution of each query element to the rank of the document. The user can reorder the hit list by prioritizing the contribution of individual query elements to override the overall rank and by assigning additional weight(s) to those contributions.

Abstract: A method is described for a computerized search for words in an electronic database with a large number of documents stored in memory. With this method, a Boolean retrieval method is used to determine in which of a large number of documents an initial word meets a Boolean condition. A probabilistic retrieval method is then used to determine in which of the documents fulfilling the Boolean condition, the relevance of appearance of a second word exceeds a specified value. The two retrieval methods use different indexes for this. The disadvantages normally found with this are avoided by the two different indexes having a common element that can be processed by both retrieval methods.