In line with previous work by Treisman, many studies have demonstrated effects of perceptual discrimination between visual targets and distractors. Few studies however have investigated semantic discrimination effects. We report the findings of two experiments that were carried out in order to demonstrate the influence of semantic relatedness between a target word and distractor words in intera...

Most studies on document clustering till date use Vector Space Model (VSM) to represent documents in the document space, where documents are denoted by a vector in a word vector space. The standard VSM does not take into account the semantic relatedness between terms. Thus, terms with some semantic similarity are dealt with in the same way as terms with no semantic relatedness. Since this uncon...

We describe Vector Generation from Explicitly-defined Multidimensional semantic Space (VGEM), a method for converting a measure of semantic relatedness (MSR) into vector form. We also describe Best path Length on a Semantic Self-Organizing Map (BLOSSOM), a semantic relatedness technique employing VGEM and a connectionist, nonlinear dimensionality reduction technique. The psychological validity ...

Is morphology a discrete and independent element of lexical structure or does it simply reflect a fine tuning of the system to the statistical correlation that exists among the orthographic and semantic properties of words? Imaging studies in English failed to show unequivocal morphological activation that is distinct from semantic or orthographic activation. Cognitive research in Hebrew has re...

Identifying the semantic relatedness of two words is an important task for the information retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a...

Various semantic relatedness, similarity, and distance measures have been proposed in the past decade and many NLP-applications strongly rely on these semantic measures. Researchers compete for better algorithms and normally only few percentage points seem to suffice in order to prove a new measure outperforms an older one. In this paper we present a metastudy comparing various semantic measure...

The use of lexical semantic knowledge in information retrieval has been a field of active study for a long time. Collaborative knowledge bases like Wikipedia and Wiktionary, which have been applied in computational methods only recently, offer new possibilities to enhance information retrieval. In order to find the most beneficial way to employ these resources, we analyze the lexical semantic r...

Segmenting text into semantically coherent fragments improves readability of text and facilitates tasks like text summarization and passage retrieval. In this paper, we present a novel unsupervised algorithm for linear text segmentation (TS) that exploits word embeddings and a measure of semantic relatedness of short texts to construct a semantic relatedness graph of the document. Semantically ...

This paper summarizes the results of a large-scale evaluation study of bag-ofwords distributional models on behavioral data from three semantic priming experiments. The tasks at issue are (i) identification of consistent primes based on their semantic relatedness to the target and (ii) correlation of semantic relatedness with latency times. We also provide an evaluation of the impact of specifi...

Questions that require answers in the form of a list of entities and the identification of diverse biological entity classes present an interesting challenge that required new approaches not needed for the TREC 2006 Genomics track. We added some components to our automatic question answering system, including (i) a simple algorithm to select which keywords extracted from natural language questi...