Unlike common nouns, person names refer to unique entities and generally have a referring function. We used event-related potentials to investigate the time course of identifying the emotional meaning of nouns and names. The emotional valence of names and nouns were manipulated separately. The results show early N1 effects in response to emotional valence only for nouns. This might reflect automatic attention directed towards emotional stimuli. The absence of such an effect for names supports the notion that the emotional meaning carried by names is accessed after word recognition and person identification. In addition, both names with negative valence and emotional nouns elicited late positive effects, which have been associated with evaluation of emotional significance. This positive effect started earlier for nouns than for names, but with similar durations. Our results suggest that distinct neural systems are involved in the retrieval of names' and nouns' emotional meaning.
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The presentation of management information on screens and paper is aimed at the initiation of control actions in order to bring about predefinied goals. The terms and concepts used in this control information can be interptreted in different ways. It is of vital importance that adequate definitions for these terms and concepts are provided, because of the area of tension betrween those that control and those being controlled. The creation of a common conceptual framework and the maintenance of concepts and definitions can be supported by the construction of an organization-specific lexicon and the use of modern IT tools.
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A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite satisfactory results, this measure does not take (semantic) relations between words into account. In this paper we study some alternative relevance measures that do use relations between words. They are computed by defining co-occurrence distributions for words and comparing these distributions with the document and the corpus distribution. We then evaluate keyword extraction algorithms defined by selecting different relevance measures. For two corpora of abstracts with manually assigned keywords, we compare manually extracted keywords with different automatically extracted ones. The results show that using word co-occurrence information can improve precision and recall over tf.idf.
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