We develop a new static index pruning criterion based on the notion of information preservation. This idea is motivated by the fact that model degeneration, as does static index pruning, inevitably reduces the predictive power of the resulting model. We model this loss in predictive power using conditional entropy and show that the decision in static index pruning can therefore be optimized to preserve information as much as possible. We evaluated the proposed approach on three different test corpora, and the result shows that our approach is comparable in retrieval performance to state-of-the-art methods. When efficiency is of concern, our method has some advantages over the reference methods and is therefore suggested in Web retrieval settings.

BACKGROUND: Text mining in the biomedical domain is receiving increasing attention. A key component of this process is named entity recognition (NER). Generally speaking, two annotated corpora, GENIA and GENETAG, are most frequently used for training and testing biomedical named entity recognition (Bio-NER) systems. JNLPBA and BioCreAtIvE are two major Bio-NER tasks using these corpora. Both tasks take different approaches to corpus annotation and use different matching criteria to evaluate system performance. This paper details these differences and describes alternative criteria. We then examine the impact of different criteria and annotation schemes on system performance by retesting systems participated in the above two tasks. RESULTS: To analyze the difference between JNLPBA's and BioCreAtIvE's evaluation, we conduct Experiment 1 to evaluate the top four JNLPBA systems using BioCreAtIvE's classification scheme. We then compare them with the top four BioCreAtIvE systems. Among them, three systems participated in both tasks, and each has an F-score lower on JNLPBA than on BioCreAtIvE. In Experiment 2, we apply hypothesis testing and correlation coefficient to find alternatives to BioCreAtIvE's evaluation scheme. It shows that right-match and left-match criteria have no significant difference with BioCreAtIvE. In Experiment 3, we propose a customized relaxed-match criterion that uses right match and merges JNLPBA's five NE classes into two, which achieves an F-score of 81.5%. In Experiment 4, we evaluate a range of five matching criteria from loose to strict on the top JNLPBA system and examine the percentage of false negatives. Our experiment gives the relative change in precision, recall and F-score as matching criteria are relaxed. CONCLUSION: In many applications, biomedical NEs could have several acceptable tags, which might just differ in their left or right boundaries. However, most corpora annotate only one of them. In our experiment, we found that right match and left match can be appropriate alternatives to JNLPBA and BioCreAtIvE's matching criteria. In addition, our relaxed-match criterion demonstrates that users can define their own relaxed criteria that correspond more realistically to their application requirements.

Fuzziness is an intrinsic property of knowledge retrieval. For instance, the relationship similarity is fuzzy by nature. The degree of truth of some transitive relations naturally degrades as transitivity is applied repeatedly. In this paper we propose a formal model of knowledge retrieval based on Goguen Style Fuzzy Horn Logic, a subset of Goguen's fuzzy logic system [The Logic of Inexact Concepts]. This model provides a mathematical (logical) foundation for knowledge retrieval that naturally incorporates fuzziness. By using only a subset of Goguen's logic, we have a simple but useful and efficient reasoning framework.

We propose an image retrieval methodology for a collection of similar images. By similar, we mean that one can define, for the collection, a set of dimensions, and for each of which a set of features. The dimensions are used to capture the essential characteristics of the images in the collection, and the features are for describing each image to a certain degree. We call this strategy fine-grained image retrieval to differentiate it from the more common coarse-grained retrieval, which does not assume any semantic properties on the image collection. The effectiveness of our methodology is demonstrated through an icon-based interactive retrieval system on a collection of butterfly images. This system provides the user with a friendly initial query-by-feature (QBF) interface. The user can then use query-by-example (QBE) to refine the query. In addition to presenting an outline of the methodology and the implementation on butterfly images, we also present some experimental results.

We present here an effective proof theory that allows one to reason within algebras of algebraic logic in a purely syntactic, algebraic fashion. We demonstrate the effectiveness of the method by discussing our automated proofs of problems and theorem

This paper describes a methodology for parallel theorem proving in a distributed environment, called deduction by Clause-Diffusion. This methodology utilizes parallelism at the search level, by having concurrent, asynchronous deductive processes sear

In this paper, a proof method based on a notion of transfinite semantic trees is presented and it is shown how to apply it to prove the completeness of refutational theorem proving methods for first order predicate calculus with equality.To demonstrate how this method is used, the completeness of two theorem-proving strategies, both refinements of resolution and paramodulation, are proved.Neither of the strategies need the functionally reflexive axioms nor paramodulating into variables.Therefore the Wos-Robinson conjecture follows as a corollary.Another strategy for Horn logic with equality is also presented.