Applying classical association rule extraction framework to dense SAGE data leads to an unmanageably highly sized association rule sets– compounded with their low precision– that often make the perusal of knowledge ineffective, their exploitation time-consuming, and frustrating for the user. To overcome such drawback, we advocate the extraction and exploitation of compact and informative generi...

This paper introduces two novel approaches for extracting compact grammars for hierarchical phrase-based translation. The first is a combinatorial optimization approach and the second is a Bayesian model over Hiero grammars using Variational Bayes for inference. In contrast to the conventional Hiero (Chiang, 2007) rule extraction algorithm , our methods extract compact models reducing model siz...

DRASiW is an extension of the WiSARD weightless neural model that provides the ability of producing examples/prototypes, called “mental images”, from learnt categories. This work introduces a novel way of performing rule extraction by applying the WiSARD/DRASiW RAMbased neural model upon a well-known machine learning benchmark. A functional exploration is offered in order to demonstrate how the...

This year’s MedNLP-2 [1] has two tasks: Extraction task (Task 1) and Normalization task (Task 2). We tested both machine learning based methods and an ad-hoc rule-based method for the two tasks. For the Extraction Task, a two-stage approach (first, the machine learning based method is applied to identify c tags, and second, the rule-based method is applied to modality features) obtained higher ...

We argue that despite being an actively researched area for nearly a decade, rule-extraction technology has not made as signiicant of an impact as it should have. A connuence of trends, however, has made the ability to extract comprehensible descriptions from complex learned models more important now than ever. We argue that rule-extraction methods can have a signii-cant impact in the overlappi...

We present a rule extractor for SCFG-based MT that generalizes many of the contraints present in existing SCFG extraction algorithms. Our method’s increased rule coverage comes from allowing multiple alignments, virtual nodes, and multiple tree decompositions in the extraction process. At decoding time, we improve automatic metric scores by significantly increasing the number of phrase pairs th...

The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction. Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this system demonstration, we present a tool and a workflow designed to enab...

This paper addresses the important issue of the tradeoff between accuracy and comprehensibility in data mining. The paper presents results which show that it is, to some extent, possible to bridge this gap. A method for rule extraction from opaque models (Genetic Rule EXtraction – G-REX) is used to show the effects on accuracy when forcing the creation of comprehensible representations. In addi...

Knowledge-extraction methods are applied to ML-based predictors attain explainable representations of their operation when the lack interpretable results constitutes a problem. Several algorithms have been proposed for knowledge extraction, mostly focusing on extraction either lists or trees rules. Yet, most them only support supervised learning – and, in particular, classification tasks. Iter ...

opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which can play an important role in making major decisions in such area. in general, opinion mining extracts user reviews at three levels of document, sentence and feature. opinion mining at the feature level is taken into consideration more than the other two levels d...