About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
Content driven enrichment of formal text using concept definitions and applications
Abstract
Formal text is objective, unambiguous and tends to have complex sentence construction intended to be understood by the target demographic. However, in the absence of domain knowledge it is imperative to define key concepts and their relationship in the text for correct interpretation for general readers. To address this, we propose a text enrichment framework that identifies the key concepts from input text, highlights definitions and fetches the definition from external data sources in case the concept is undefined. Beyond concept definitions, the system enriches the input text with concept applications and a pre-requisite concept graph that showcases the inter-dependency within the extracted concepts. While the problem of learning definition statements is attempted in literature, the task of learning application statements is novel. We manually annotated a dataset for training a deep learning network for identifying application statements in text. We quantitatively compared the results of both application and definition identification models with standard baselines. To validate the utility of the proposed framework for general readers, we report enrichment accuracy and show promising results.