In many Information Extraction tasks, dictionaries and lexica are powerful building blocks for sophisticated extractions. The success of the Semantic Web in the last 10 years has produced an unprecedented quantity of available structured data that can be leveraged to produce dictionaries on countless concepts in many domains. While being an invaluable resource, these automatically built dictionaries may contain "problematic" items, such as spurious words, which have been included by mistake, or ambiguous words, which appear with multiple different meanings in the target corpus and therefore necessitating an expensive disambiguation task. In this paper, we propose a simple and effective method to identify problematic terms in a given dictionary, which are ambiguous or spurious with respect to a given corpus, with the aim to facilitate subsequent Information Extraction tasks. We prove the effectiveness of the method with a systematic experiment on publicly available concept dictionaries, using a very large Web corpus as target, with an average precision in identifying a problem term above 85%.

Ontologies are dynamic artifacts that evolve both in structure and content. Keeping them up-to-date is a very expensive and critical operation for any application relying on semantic Web technologies. In this paper we focus on evolving the content of an ontology by extracting relevant instances of ontological concepts from text. We propose a novel technique which is (i) completely language independent, (ii) combines statistical methods with human-in-the-loop and (iii) exploits Linked Data as bootstrapping source. Our experiments on a publicly available medical corpus and on a Twitter dataset show that the proposed solution achieves comparable performances regardless of language, domain and style of text. Given that the method relies on a human-in-the-loop, our results can be safely fed directly back into Linked Data resources.

Data analysis tasks often require grouping of information to identify trends and associations. However, as the number of elements rises to the hundreds and thousands the cost of having a person perform the groupings unassisted quickly becomes prohibitive. Previous approaches have combined traditional clustering techniques with manual interaction steps, yielding human-in-the-loop clustering algorithms that incorporate user feedback by reweighting features or adjusting a similarity function. But in the real world, many grouping tasks lack both a feature set and a well-defined (dis)similarity metric, having only a subject matter expert with an implicit understanding of the correct relationships between elements based on the domain and the task at hand. We present a refine-and-lock clustering interaction model and demonstrate its effectiveness for cognitive-assisted human clustering over other interaction models such as split/merge and must-link/can't-link. Our approach offers effective automatic clustering assistance even in the absence of clear features or a definitive similarity metric; ensures that every cluster has final user approval; and exhibits at least a 3.94× improvement over other interactive clustering approaches in time to completion.

Healthcare is a data-rich but information-poor domain. Terabytes of multimedia medical data are being generated on a monthly basis in a typical healthcare organization in order to document patients' health status and care process. Government and health-related organizations are pushing for fully electronic, cross-institution, integrated Electronic Health Records to provide a better, cost effective and more complete access to this data. However, provision of efficient access to the content of such records for timely and decision-enabling information extraction will not be available. Such a capability is essential for providing efficient decision support and objective evidence to clinicians. In addition researchers, medical students, patients, and payers could also benefit from it. We present the idea of concept-based multimedia health records, which aims at organizing the health records at the information level. We will explore the opportunities and possibilities that such an organization will provide, what role the field of multimedia content management could play to materialize this type of health record organization, and what the challenges will be in the quest for realizing the idea.We believe that the field of multimedia can play a very active role in taking healthcare information systems to the next level by facilitating the access to decision-enabling information for different types of users in healthcare. Our goal is to share with the community our thoughts on where the field of multimedia content management research should be focusing its attention to have a fundamental impact on the practice of medicine.

We describe a system for rapidly determining document similarity among a set of documents obtained from an information retrieval (IR) system. We obtain a ranked list of the most important terms in each document using a rapid phrase recognizer system. We store these in a database and compute document similarity using a simple database query. If the number of terms found to not be contained in both documents is less than some predetermined threshold compared to the total number of terms in the document, these documents are determined to be very similar. We compare this to the shingles approach.

Speech is a tantalizing mode of human communication. On the one hand, humans understand speech with ease and use speech to express complex ideas, information, and knowledge. On the other hand, automatic speech recognition with computers is still very hard, and extracting knowledge from speech is even harder. In this paper we motivate the study of speech as a knowledge resource and briefly survey a family of related applications and systems being developed at IBM Research aimed towards the goal of exploiting speech as a knowledge resource.

We present a new technique for question answering called Predictive Annotation. Predictive Annotation identifies potential answers to questions in text, annotates them accordingly and indexes them. This technique, along with a complementary analysis of questions, passage-level ranking and answer selection, produces a system effective at answering natural-language fact-seeking questions posed against large document collections. Experimental results show the effects of different parameter settings and lead to a number of general observations about the question-answering problem.