NASA Scientific and Technical Aerospace Reports

We explore the use of Optimal Mixture Models to represent topics. We analyze two broad classes of mixture models:
set-based and weighted. We provide an original proof that estimation of set-based models is NP-hard, and therefore not
feasible. We argue that weighted models are superior to set-based models, and the solution can be estimated by a simple
gradient descent technique. We demonstrate that Optimal Mixture Models can be successfully applied to the task of document
retrieval. Our experiments show that weighted mixtures outperform a simple language modeling baseline. We also observe that
weighted mixtures are more robust than other approaches of estimating topical models.
DTIC
Information Retrieval; Mathematical Models; Optimization
20060001853 Massachusetts Univ., Amherst, MA USA
A Conditional Random Field for Discriminatively-Trained Finite-State String Edit Distance
McCallum, Andrew; Bellare, Kedar; Pereira, Fernando; Jan. 1, 2005; 9 pp.; In English
Report No.(s): AD-A440386; No Copyright; Avail.: Defense Technical Information Center (DTIC)
The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence
analysis, and other domains. This paper presents discriminative string-edit CRF’s a finite-state conditional random field model
for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem,
such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of
complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify
the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive
and negative instances of string pairs. We present positive experimental results on several data sets.
DTIC
Random Variables; Strings
20060001854 Connecticut Univ., Storrs, CT USA
Mapping Flows onto Networks to Optimize Organizational Processes
Levchuk, Georgiy M.; Levchuk, Yuri N.; Pattipati, Krishna R.; Kleinman, David L.; Jan. 1, 2005; 25 pp.; In English; Original
contains color illustrations
Contract(s)/Grant(s): N00014-00-1-0101
Report No.(s): AD-A440387; No Copyright; Avail.: Defense Technical Information Center (DTIC)
Interdependence of tasks in a mission necessitates information flow among the organizational elements (agents) assigned
to these tasks. This information flow introduces communication delays. An effective task schedule that minimizes the total
execution time, including task processing and coordination delays, is an important issue in designing an organization and its
task processing strategy. This paper defines the structure of information-dependent tasks, and describes an approach to map
this structure to a network of organizational elements (agents). Since the general problem of scheduling tasks with
communication is NP-hard, only fast heuristic (e.g., list scheduling and linear clustering) algorithms are discussed. The authors
modify the priority calculation for list scheduling methods, matching the critical path with a network of heterogeneous agents.
They then present their algorithm, termed Heterogeneous Dynamic Bottom Level (HDBL), and compare it with various
list-scheduling heuristics. The results show that HDBL exhibits superior performance to all list scheduling algorithms,
providing an improvement of over 25% in schedule length for communication-intensive task graphs.
DTIC
Command and Control; Information Transfer; Mapping; Networks; Optimization; Organizations; Scheduling
20060001863 Maryland Univ., College Park, MD USA
Searching the Web with SHOE
Heflin, Jeff; Hendler, James; Jan. 1, 2000; 7 pp.; In English; Original contains color illustrations
Contract(s)/Grant(s): DAAL01-97-K-0135
Report No.(s): AD-A440405; No Copyright; Avail.: Defense Technical Information Center (DTIC)
Although search engine technology has improved in recent years, there are still many types of searches that return
unsatisfactory results. This situation can be greatly improved if web pages use a semantic markup language to describe their
content. We have developed SHOE, a language for this purpose, and in this paper describe a scenario for how the language
could be used by search engines of the future. A major challenge to this system is designing a query tool that can exploit the
power of a knowledge base while still being simple enough for the casual user. We present the SHOE Search tool, which
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