We aimed to determine whether semantic relatedness between an incoming word and its preceding context can override expectations based on two types of stored knowledge: real-world knowledge about the specific events and states conveyed by a verb, and the verb's broader selection restrictions on the animacy of its argument. We recorded event-related potentials on post-verbal Agent arguments as pa...

This paper presents a graph-based method for all-word word sense disambiguation of biomedical texts using semantic relatedness as edge weight. Semantic relatedness is derived from a term-topic co-occurrence matrix. The sense inventory is generated by the MetaMap program. Word sense disambiguation is performed on a disambiguation graph via a vertex centrality measure. The proposed method achieve...

Conceptual vectors can be used to represent thematic aspects of text segments, which allow for the computation of semantic relatedness. We study the behavior of conceptual vectors based on an ontology by comparing the results to the Miller-Charles benchmark. We discuss the limits to such an approach due to explicit mapping, as well as the viability of the Miller-Charles dataset as a benchmark f...

Semantic technologies are playing an increasingly popular role as a means for advancing the capabilities of knowledge management systems. Among these advancements, researchers have successfully leveraged semantic technologies, and their accompanying techniques, to improve the representation and search capabilities of knowledge management systems. This paper introduces a further application of s...

This paper explores the hypothesis that semantic relatedness may be more reliably inferred by using a multilingual space, as compared to the typical monolingual representation. Through evaluations using several stateof-the-art semantic relatedness systems, applied on standard datasets, we show that a multilingual approach is better suited for this task, and leads to improvements of up to 47% wi...

Keyword-matching systems based on simple models of semantic relatedness are inadequate at modelling the ambiguities in natural language text, and cannot reliably address the increasingly complex information needs of users. In this paper we propose novel methods for computing semantic relatedness by spreading activation energy over the hyperlink structure of Wikipedia. We demonstrate that our te...