54,738 research outputs found
Connecting Dream Networks Across Cultures
Many species dream, yet there remain many open research questions in the
study of dreams. The symbolism of dreams and their interpretation is present in
cultures throughout history. Analysis of online data sources for dream
interpretation using network science leads to understanding symbolism in dreams
and their associated meaning. In this study, we introduce dream interpretation
networks for English, Chinese and Arabic that represent different cultures from
various parts of the world. We analyze communities in these networks, finding
that symbols within a community are semantically related. The central nodes in
communities give insight about cultures and symbols in dreams. The community
structure of different networks highlights cultural similarities and
differences. Interconnections between different networks are also identified by
translating symbols from different languages into English. Structural
correlations across networks point out relationships between cultures.
Similarities between network communities are also investigated by analysis of
sentiment in symbol interpretations. We find that interpretations within a
community tend to have similar sentiment. Furthermore, we cluster communities
based on their sentiment, yielding three main categories of positive, negative,
and neutral dream symbols.Comment: 6 pages, 3 figure
Can We Say an Ear of Cabbage : On Translating Wordplay in Xi Xi’s Poetry
This article reflects on the translation of wordplay in the poetry of Hong Kong author Xi Xi. Xi Xi is a highly imaginative poet: much of her poetry hinges upon specificities of the Chinese language, and one might well ask if this makes her work “untranslatable.” This article identifies various techniques for translating Xi Xi’s wordplay, detailing how I mine the potential of English for ways to recreate Xi Xi’s puns, puzzles, and playful subversion of language in a new linguistic and cultural environment. It encourages readers and translators to become unshackled from rules, assumptions, and conventions as they reflect on the malleability and potential of poetry and of language itself
CEAI: CCM based Email Authorship Identification Model
In this paper we present a model for email authorship identification (EAI) by
employing a Cluster-based Classification (CCM) technique. Traditionally,
stylometric features have been successfully employed in various authorship
analysis tasks; we extend the traditional feature-set to include some more
interesting and effective features for email authorship identification (e.g.
the last punctuation mark used in an email, the tendency of an author to use
capitalization at the start of an email, or the punctuation after a greeting or
farewell). We also included Info Gain feature selection based content features.
It is observed that the use of such features in the authorship identification
process has a positive impact on the accuracy of the authorship identification
task. We performed experiments to justify our arguments and compared the
results with other base line models. Experimental results reveal that the
proposed CCM-based email authorship identification model, along with the
proposed feature set, outperforms the state-of-the-art support vector machine
(SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The
proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25
authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5%
accuracy has been achieved on authors' constructed real email dataset. The
results on Enron dataset have been achieved on quite a large number of authors
as compared to the models proposed by Iqbal et al. [1, 2]
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