Ontology matching is an essential aspect of the Semantic Web with a goal of finding alignments among the entities of given ontologies. Ontology matching is a necessary step for establishing interoperation and knowledge sharing among Semantic Web applications. In this study we present an algorithm and a tool developed based on this algorithm to find correspondences among entities of input ontolo...

We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three metrics. It uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity. These range from simple character and word n-grams and common subsequences to complex features such as Explicit Sem...

The revised hierarchical model of bilingualism (e.g., Kroll & Stewart, 1994) assumes that second language (L2) words primarily access semantics through their first language (L1) translation equivalents. Consequently, backward translation from L2 to L1 should not imply semantic access but occurs through lexical wordform associations. However, recent research with Dutch-French bilinguals showed t...

Inner speech, that little voice that people often hear inside their heads while thinking, is a form of mental imagery. The properties of inner speech errors can be used to investigate the nature of inner speech, just as overt slips are informative about overt speech production. Overt slips tend to create words (lexical bias) and involve similar exchanging phonemes (phonemic similarity effect), ...

The massive amount of information from the internet has revolutionized field natural language processing. One challenges was estimating similarity between texts. This been an open research problem although various studies have proposed new methods over years. paper surveyed and traced primary in text similarity. aim to give a broad overview existing issues, applications, research. identified fo...

This work investigates the variation in a word’s distributionally nearest neighbours with respect to the similarity measure used. We identify one type of variation as being the relative frequency of the neighbour words with respect to the frequency of the target word. We then demonstrate a three-way connection between relative frequency of similar words, a concept of distributional gnerality an...

Integrative relations, which combine two independent concepts (e.g., ”lake” and ”bird”) into a compound representation (”lake bird”), induce lexical priming. For example, ”lake” speeds lexical decisions to ”bird” even though they are semantically dissimilar, lexically unassociated, and unfamiliar as a phrase. We report three experiments that investigate whether this integrative priming occurs p...

This paper describes the specifications and results of UMCC_DLSI system, which participated in the first Semantic Textual Similarity task (STS) of SemEval-2012. Our supervised system uses different kinds of semantic and lexical features to train classifiers and it uses a voting process to select the correct option. Related to the different features we can highlight the resource ISR-WN used to e...

In this paper we present our approach for assigning degrees of relational similarity to pairs of words in the SemEval-2012 Task 2. To measure relational similarity we employed lexical patterns that can match against word pairs within a large corpus of 12 million documents. Patterns are weighted by obtaining statistically estimated lower bounds on their precision for extracting word pairs from a...