We present an adaptive, hybrid semantic matchmaker for SAWSDL services, called SAWSDLMX2. It determines three kinds of semantic matching of an advertised service with a requested one both of which are described in the standard SAWSDL: Logic-based, text-similarity-based and XML-tree edit-based structural similarity. Before selection, SAWSDL-MX2 learns the optimal aggregation of these different m...

VRep is a system designed for SemEval 2016 Task 1 Semantic Textual Similarity (STS) and Task 2 Interpretable Semantic Textual Similarity (iSTS). STS quantifies the semantic equivalence between two snippets of text, and iSTS provides a reason why those snippets of text are similar. VRep makes extensive use of WordNet for both STS, where the Vector relatedness measure is used, and for iSTS, where...

The class of similarity based methods (SBM) covers most neural models and many other classifiers. Performance of such methods is significantly improved if irrelevant features are removed and feature weights introduced, scaling their influence on calculation of similarity. Several methods for feature selection and weighting are described. As an alternative to the global minimization procedures c...

In this paper we present a fast algorithm and implementation for computing the Spearman rank correlation (SRC) between a query expression profile and each expression profile in a database of profiles. The algorithm is linear in the size of the profile database with a very small constant factor. It is designed to efficiently handle multiple profile platforms and missing values. We show that our ...

We present an empirical study on the use of semantic information for Concept Segmentation and Labeling (CSL), which is an important step for semantic parsing. We represent the alternative analyses output by a state-of-the-art CSL parser with tree structures, which we rerank with a classifier trained on two types of semantic tree kernels: one processing structures built with words, concepts and ...

The symmetric EM algorithm is proposed for probabilistic latent semantic analysis in collaborative filtering. The algorithm allows to reveal the latent interest profiles of both users and items, then to easily construct high-quality similarity measures of all required types: user–user, item–item, and item–user. The advantage of the proposed approach is that different profiles are consistent to ...

This paper presents recent results using statistics generated by a MMl-supervised vector quantizer as a measure of audio similarity. Such a measure has proved successful for talker identification, and the extension from speech to general audio, such as music, is straightforward. A classifier that distinguishes speech from music and non-vocal sounds is presented, as well as experimental results ...

Protein complex prediction approaches are based on the assumptions that complexes have dense protein-protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can...

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words, and generates correction candidates based on morphophonemic similarity. Both ...

We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-sup...