Improving Recall of Regular Expressions for Information Extraction

Learning or writing regular expressions to identify instances of a specific
concept within text documents with a high precision and recall is challenging.
It is relatively easy to improve the precision of an initial regular expression
by identifying false positives covered and tweaking the expression to avoid the
false positives. However, modifying the expression to improve recall is difficult
since false negatives can only be identified by manually analyzing all documents,
in the absence of any tools to identify the missing instances. We focus on partially
automating the discovery of missing instances by soliciting minimal user
feedback. We present a technique to identify good generalizations of a regular
expression that have improved recall while retaining high precision. We empirically
demonstrate the effectiveness of the proposed technique as compared to
existing methods and show results for a variety of tasks such as identification of
dates, phone numbers, product names, and course numbers on real world datasets

NFA decomposition and multiprocessing architecture for parallel regular expression processing

This work presents a novel algorithm for decomposing NFA automata into one-state-active modules for parallel execution on Multiprocessor Systems on Chip (MP-SoC). Furthermore, performance related studies based on a 16-PE system for Snort, Bro and Linux-L7 regular expressions are presented. ©2009 IEEE.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.

HDL composition and HDL antioxidant capacity in patients on regular haemodialysis

Recent evidence suggests that HDL can directly inhibit LDL oxidation, a key early stage in atherogenesis. Patients with chronic renal failure are at increased cardiovascular risk, have reduced HDL levels and altered HDL composition. We have therefore investigated whether compositional changes in HDL lead to decreased HDL antioxidant capacity in these patients. In comparison to control subject HDL, patient HDL contained less total cholesterol, cholesterol esters, phospholipids and alpha-tocopherol. LDL, HDL and LDL + HDL were standardised for protein and oxidised in the presence of Cu2+. The rate of propagation during HDL oxidation was reduced in the patient group (3.28 +/- 0.65 x 10(-5) vs. 4.60 +/- 0.97 x 10(-5) abs. U/min, P