In recent years a number of methods have been proposed for the automatic acquisition of feature-based conceptual representations from text corpora. Such methods could offer valuable support for theoretical research on conceptual representation. However, existing methods do not target the full range of concept-relation-feature triples occurring in human-generated norms (e.g. flute produce sound) but rather focus on concept-feature pairs (e.g. flute -sound) or triples involving specific relations only (e.g. is-a or part-of relations). In this article we investigate the challenges that need to be met in both methodology and evaluation when moving towards the acquisition of more comprehensive conceptual representations from corpora. In particular, we investigate the usefulness of three types of knowledge in guiding the extraction process: encyclopedic, syntactic and semantic. We present first a semantic analysis of existing, human-generated feature production norms, which reveals information about co-occurring concept and feature classes. We introduce then a novel method for large-scale feature extraction which uses the class-based information to guide the acquisition process. The method involves extracting candidate triples consisting of concepts, relations and features (e.g. deer have antlers, flute produce sound) from corpus data parsed for grammatical dependencies, and re-weighting the triples on the basis of conditional probabilities calculated from our semantic analysis. We apply this method to an automatically parsed Wikipedia corpus which includes encyclopedic information and evaluate its accuracy using a number of different methods: direct evaluation against the McRae norms in terms of feature types and frequencies, human evaluation, and novel evaluation in terms of conceptual structure variables. Our investigation highlights a number of issues which require addressing in both methodology and evaluation when aiming to improve the accuracy of unconstrained feature extraction further.
MotivationProtein–protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.ResultsPPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi—a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations.Availability and implementation https://github.com/ima23/maxent-ppi Supplementary information Supplementary data are available at Bioinformatics online.
The support vector machine (SVM) methodology has become a popular and well-used component of present chemometric analysis. We assess a relatively recent development of the algorithm, multiple kernel learning (MKL), on published structure-property relationship (SPR) data. The MKL algorithm learns a weighting across multiple kernel-based representations of the data during supervised classifier creation and, thereby, may be used to describe the influence of distinct groups of structural descriptors upon a single structure-property classifier without explicitly omitting any of them. We observe a statistically significant performance improvement over a conventional, single kernel SVM on all three SPR data sets analysed. Furthermore, MKL output is observed to provide useful information regarding the relative influence of five distinct descriptor subsets present in each data set.
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