1. Rutgers Components Phase 2 Principal investigators –Paul Kantor, PI; Design, modelling and analysis –Kwong Bor Ng, Co-PI - Fusion; Experimental design –Nina Wacholder, Co-PI; linguistic foundations for modelling

2. Key Components Adaptive personalization to analyst, task and context Improve effectiveness of information access for question answering -- data fusion of IR methods Improve effectiveness of characterizing document qualities, tuned to specific analyst’s persepctives

3. Model Personalization (1) : Robust Information Access & Data Fusion For a persistent query, improve frame and answer generation through Data Fusion (local fusion with person, task, topic feedback) and Interactive Relevance Feedback. In stage 1, we have demonstrated effective data fusion into HITIQA to optimize the rate of useful paragraph extraction. In stage 2, the emphasis will be on exploiting user judgments over time to adjust fusion parameters chronologically, with a time-sensitive weighting scheme, to fit the evolving perspective of the analyst on the task, topic an context.

4. Model Personalization (2) : Document Quality Aspects Personalization of the automatic document quality aspect assessment algorithm, through advanced statistical analysis and machine learning, to identify (1) global quality aspect predictors, (2) a general formal model of quality aspect assessment, and (3) personal parameters settings for individual preference. At stage1, we have established a effective models for estimation of some document qualities, based on textual features and linguistic patterns in a document. While global models do “better than chance”, for high acuracy models must be personalized. In stage 2, we will expand identification of good predictive variables for quality aspects, with emphasis on a local level: to encapsulate the personal mental model of an analyst.

5. Model Personalization (3): Integration through Experiment We will integrate the personalization and other mechanisms into a single interface, by converting related functionalities into position and iconic information in the user display. At stage 2, focusing on the analyst with a persistent query, we will investigate the impacts of interface options on analyst satisfaction and task effectiveness, to identify the best combination strategy, and to establish effectiveness measures on a personal level.

6. Sophisticated Statistical Techniques Sophisticated statistical methods (Design of experiment, ANOVA, multiple comparisons by Scheffe and Tukey’s method, and orthogonal arrays) will reduce the number of experimental configurations to be studied. Instead of a case-by-case attention to “failure analysis” the design will focus on how to neutralize negative effects to obtain more accurate evaluations and design selection with fewer experiments

7. Language Features for Quality Aspects. Expand a scheme, now being developed, for characterizing “aspects” or “facets” of topics. These will be different for e.g. WMD or Biography. Aspects are signalled by the presence of adjective classes. These classes are being defined now, and will be expanded in the proposed work.

8. Using Language Features With a more refined model of the relation of adjectives to aspects, the system will be better able to “understand” classes that the analyst defines, and to flag further occurences in an incoming message stream.

9. A note on retrieval fusion Retrieval fusion will be made interactive with a small Java display, now under development, that tracks the contribution of each retrieval scheme to providing useful information. An interactive feature permits the analyst to highlight a region in the “fusion space” for further investigation.

10. Mock-up Fusion Interface System 2 System 1 1. HITIQA’s Initial retrieval uses both systems. [The occupied region here represents the LOGICAL OR rule. Each document is represented by a small circle. As a passage is marked relevant by the users, the document it came from is flagged (here shown in yellow). 2. The analyst perceives that many of the useful passages came from documents that are clustered near the inner corner, and using the interface tool, draws an extended retrieval region (shown here by the dotted orange box) which HITIQA now explores. =not relevant = relevant 2.5 inches