Michael Crystal

ALGORITHMS THAT LEARN TO EXTRACT INFORMATION BBN: TIPSTER PHASE III

All of BBNs research under the TIPSTER III program has focused on doing extraction by applying statistical models trained on annotated data, rather than by using programs that execute hand-written rules. Within the context of MUC-7, the SIFT system for extraction of template entities (TE) and template relations (TR) used a novel, integrated syntactic/semantic language model to extract sentence level information, and then synthesized information across sentences using in part a trained model for cross-sentence relations. At the named entity (NE) level as well, in both MET-1 and MUC-7, BBN employed a trained, HMM-based model.The results in these TIPSTER evaluations are evidence that such trained systems, even at their current level of development, can perform roughly on a par with those based on rules hand-tailored by experts. In addition, such trained systems have some significant advantages:• They can be easily ported to new domains by simply annotating fresh data.• The complex interactions that make rule-based systems difficult to develop and maintain can here be learned automatically from the training data.We believe that improved and extended versions of such trained models have the potential for significant further progress toward practical systems for information extraction.

A Methodology for Extrinsically Evaluating Information Extraction Performance

This paper reports a preliminary study addressing two challenges in measuring the effectiveness of information extraction (IE) technology:• Developing a methodology for extrinsic evaluation of IE; and,• Estimating the impact of improving IE technology on the ability to perform an application task.The methodology described can be employed for further controlled experiments regarding information extraction.

CHINESE INFORMATION EXTRACTION AND RETRIEVAL

This paper provides a summary of the following topics:1. what was learned from porting the INQUERY information retrieval engine and the INFINDER term finder to Chinese2. experiments at the University of Massachusetts evaluating INQUERY performance on Chinese newswire (Xinhua),3. what was learned from porting selected components of PLUM to Chinese4. experiments evaluating the POST part of speech tagger and named entity recognition on Chinese.5. program issues in technology development.

Multi-modal prediction of PTSD and stress indicators

Post-traumatic stress disorder (PTSD) is an anxiety disorder that affects a large population and that is currently diagnosed mostly through subject interviews and manual analysis of self-reported symptoms and of subject behavior. However, most PTSD cases are believed to go underdiagnosed and un-dertreated. We present a multi-modal system for computer-aided diagnosis of PTSD and stress that requires no clinician interview and relies principally in the elicitation of multimodal neurophysiological responses to audio-visual stimuli. We conduct a thorough evaluation of the discriminative power of the modalities involved (electro encephalography, galvanic skin-response, electrocardiography, head motion and speech), type of stimuli presented (audio, images, audio-and-images and video), and emotions evoked (positive, negative, and trauma-specific) between PTSD subjects and high and low-stress control groups. Our analysis indicates that the multi-modal prediction from the elicitation of trauma-specific emotions from images and audio is a promising approach to computer-aided diagnosis.

PROGRESS IN INFORMATION EXTRACTION

This paper provides a quick summary of the following topics: enhancements to the PLUM information extraction engine, what we learned from MUC-6 (the Sixth Message Understanding Conference), the results of an experiment on merging templates from two different information extraction engines, a learning technique for named entity recognition, and towards information extraction from speech.