35,573 research outputs found
Positive words carry less information than negative words
We show that the frequency of word use is not only determined by the word
length \cite{Zipf1935} and the average information content
\cite{Piantadosi2011}, but also by its emotional content. We have analyzed
three established lexica of affective word usage in English, German, and
Spanish, to verify that these lexica have a neutral, unbiased, emotional
content. Taking into account the frequency of word usage, we find that words
with a positive emotional content are more frequently used. This lends support
to Pollyanna hypothesis \cite{Boucher1969} that there should be a positive bias
in human expression. We also find that negative words contain more information
than positive words, as the informativeness of a word increases uniformly with
its valence decrease. Our findings support earlier conjectures about (i) the
relation between word frequency and information content, and (ii) the impact of
positive emotions on communication and social links.Comment: 16 pages, 3 figures, 3 table
Interchanging lexical resources on the Semantic Web
Lexica and terminology databases play a vital role in many NLP applications, but currently most such resources are published in application-specific formats, or with custom access interfaces, leading to the problem that much of this data is in ââdata silosââ and hence difficult to access. The Semantic Web and in particular the Linked Data initiative provide effective solutions to this problem, as well as possibilities for data reuse by inter-lexicon linking, and incorporation of data categories by dereferencable URIs. The Semantic Web focuses on the use of ontologies to describe semantics on the Web, but currently there is no standard for providing complex lexical information for such ontologies and for describing the relationship between the lexicon and the ontology. We present our model, lemon, which aims to address these gap
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