The Enterprise Information Security Management (EISM) system aims to semi-automatically estimate the sensitivity of enterprise data through advanced content analysis and business process mining. We demonstrate a proof-of-concept of EISM that crawls all the files in a personal computer and estimates the sensitivity of individual files and the overall sensitivity level of the computer. The system can identify 11 different personally identifiable information (PII) types and 11 sensitive data categories, and estimate data sensitivity based on the identified sensitive information in the data. Furthermore, the tool produces the evidences of the discovered sensitive information including the surrounding context in the document to help users understand what kinds of sensitive information are stored in their computer. The evidences allow users can easily redact the sensitive information or move it to a more secure location. Thus, this system can be used as a privacy enhancing tool as well as a security tool.

Customer satisfaction is a very important indicator of how successful a contact center is at providing services to the customers. Contact centers typically conduct a manual survey with a randomly selected group of customers to measure customer satisfaction. Manual customer satisfaction surveys, however, provide limited values due to high cost and the time lapse between the service and the survey.

Call type classification and topic classification for contact center calls using automatically generated transcripts is not yet widely available mainly due to the high cost and low accuracy of call-center grade automatic speech transcription. To address these challenges, we examine if using only partial conversations yields accuracy comparable to using the entire customer-agent conversations. We exploit two interesting characteristics of call center calls. First, contact center calls are highly scripted following prescribed steps, and the customers problem or request (i.e., the determinant of the call type) is typically stated in the beginning of a call. Thus, using only the beginning of calls may be sufficient to determine the call type. Second, agents often more clearly repeat or rephrase what customers said, thus it may be sufficient to process only agents’ speech. Our experiments with 1,677 customer calls show that two partial transcripts comprising only the agents utterances and the first 40 speaker turns actually produce slightly higher classification accuracy than a transcript set comprising the entire conversations. In addition, using partial conversations can significantly reduce the cost for speech transcription.

Modern businesses use contact centers as a communication channel with users of their products and services. The largest factor in the expense of running a telephone contact center is the labor cost of its agents. IBM Research has built a new system, Contact-Center Agent Buddies (CAB), which is designed to help reduce the average handle time (AHT) for customer calls, thereby also reducing their cost. In this paper, we focus on the call logging subsystem, which helps agents reduce the time they spend documenting those calls. We built a Template CAB and a Call Logging CAB, using a pipeline consisting of audio capture of a telephone conversation, automatic speech recognition, text analysis, and log generation. We developed techniques for ASR text cleansing, including normalization of expressions and acronyms, domain terms, capitalization, and boundaries for sentences, paragraphs, and call segments. We found that simple heuristics suffice to generate high-quality logs from the normalized sentences. The pipeline yields a candidate call log which the agents can edit in less time than it takes them to generate call logs manually. Evaluation of the Call Logging CAB in an industrial contact center environment shows that it reduces the amount of time agents spend logging calls by at least 50% without compromising the quality of the resulting call documentation.

This paper studies the relationship between word error rate (WER) and keyword error rate (KER) in speech transcripts and their effect on the performance of speech analytics applications. Automatic speech recognition (ASR) systems are increasingly used as input for speech analytics, which raises the question of whether WER or KER is the more suitable performance metric for calibrating the ASR system. ASR systems are typically evaluated in terms of WER. Many speech analytics applications,

In this paper, we describe a system for the construction of taxonomies which yield high accuracies with automated categorization systems, even on Web and intranet documents. In particular, we describe the way in which measurement of five key features of the system can be used to predict when categories are sufficiently well defined to yield high accuracy categorization. We describe the use of this system to construct a large (8800-category) general-purpose taxonomy and categorization system.

We discuss the merits of building text categorization systems by using supervised clustering techniques. Traditional approaches for document classification on a predefined set of classes are often unable to provide sufficient accuracy because of the difficulty of fitting a manually categorized collection of documents in a given classification model. This is especially the case for heterogeneous collections of Web documents which have varying styles, vocabulary, and authorship. Hence, we investigate the use of clustering in order to create the set of categories and its use for classification of documents. Completely unsupervised clustering has the disadvantage that it has difficulty in isolating sufficiently fine-grained classes of documents relating to a coherent subject matter. We use the information from a preexisting taxonomy in order to supervise the creation of a set of related clusters, though with some freedom in defining and creating the classes. We show that the advantage of using partially supervised clustering is that it is possible to have some control over the range of subjects that one would like the categorization system to address, but with a precise mathematical definition of how each category is defined. An extremely effective way then to categorize documents is to use this a priori knowledge of the definition of each category. We also discuss a new technique to help the classifier distinguish better among closely related clusters.

This paper investigates the use of supervised clustering in order to create sets of categories for classification of documents.We use information from a pre-existing taxonomy in order to supervise the creation of a set of related clusters, though with some freedom in defining and creating the classes.We show that the advantage of using supervised clustering is that it is possible to have some control over the range of subjects that one would like the categorization system to address, but with a precise mathematical definition of each category.We then categorize documents using this a priori knowledge of the definition of each category.We also discuss a new technique to help the classifier distinguish better among closely related clusters.Finally, we show empirically that this categorization system utilizing a machine-derived taxonomy performs as well as a manual categorization process, but at a far lower cost.