This paper presents an application of PageRank, a random-walk model originally devised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess a given semantic property. The semantic properties we use for exemplifying the approach are positivity and negativity, two properties of central importance in sentiment analysis. The idea derives from the observat...

Studies in linguistics define lexico-syntactic patterns to characterize the linguistic utterances that can be interpreted with semantic relations. Because patterns are assumed to reflect linguistic regularities that have a stable interpretation, several software implement such patterns to extract semantic relations from text. Nevertheless, a thorough analysis of pattern occurrences in various c...

Web-scale relation extraction is a means for building and extending large repositories of formalized knowledge. This type of automated knowledge building requires a decent level of precision, which is hard to achieve with automatically acquired rule sets learned from unlabeled data by means of distant or minimal supervision. This paper shows how precision of relation extraction can be considera...

Three experiments tested whether a modified version of the Clustered Conceptual Span task (H. J. Haarmann, E. J. Davelaar, & M. Usher, 2003), which ostensibly requires active maintenance of semantic representations, predicted individual differences in higher-order cognitive abilities better than short-term memory (STM) span tasks or non-semantic versions of the “Conceptual” task did. Non-semant...

This paper addresses the problem of semantic relation identification for a set of relations difficult to differentiate: near-misses and overlaps. Based on empirical observations on a fairly large dataset of such examples we provide an analysis and a taxonomy of such cases. Using this taxonomy we create various contingency sets of relations. These semantic categories are automatically identified...

The current event-related fMRI study specifies the neuroanatomical correlates of semantic priming and differences in semantic relation types using an auditory primed lexical decision task (LDT). Word pairs consisted of different relation types, associations (key-chain), pure categorical relations (cow-dog), and unrelated words (table-window), as well as word-pseudoword (way-tinne) and pseudowor...

This paper presents an approach for detecting semantic relations in noun phrases. A learning algorithm, called semantic scattering, is used to automatically label complex nominals, genitives and adjectival noun phrases with the corresponding semantic relation. 1 Problem description This paper is about the automatic labeling of semantic relations in noun phrases (NPs). The semantic relations are...

In this paper, we describe the first tool that detects the semantic relation between Arabic named entities, henceforth RelANE. We use various supervised learning techniques to predict the word or the sequence of terms that can highlight one or more semantic relationship between two Arabic named entities. For each word in the sentence, we use its morphological, contextual and semantic features o...

Semantic relation clustering for unsupervised information extraction Most studies in unsupervised information extraction concentrate on the relation extraction and few work has been proposed on the organization of the extracted relations. We present in this paper a two-step clustering procedure to group semantically equivalent relations : a first step clusters relations with similar expressions...

I propose to solve the hard problem in the philosophy of mind by means of Brandom‟s notion of the pragmatically mediated semantic relation. The explanatory gap between a phenomenal concept and the corresponding theoretical concept is a gap in the pragmatically mediated semantic relation between them. It is closed if we do not neglect the pragmatics.