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  • × author_ss:"Ananiadou, S."
  1. Olivier, P.; Ananiadou, S.; Maeda, T.; Tsujii, J.: Visualisation: mediating the interchange of information from the verbal to the visual domain (1992) 0.01 
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    Source
    Mensch und Maschine: Informationelle Schnittstellen der Kommunikation. Proc. des 3. Int. Symposiums für Informationswissenschaft (ISI'92), 5.-7.11.1992 in Saarbrücken. Hrsg.: H.H. Zimmermann, H.-D. Luckhardt u. A. Schulz
  2. Ananiadou, S.; McNaught, J.: Terms are not alone : term choice and choice terms (1995) 0.00 
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    Abstract
    Assesses the degree to which established practices in terminology can provide the translation industry with the lexical means to support mediation of information between languages, especially where such mediation involves modification. The effects of term variation, collocation and sublanguage phraseology present problems of term choice to the translator. Current term resources cannot help much with these problems; however, tools and techniques are discussed which, in the near future, will offer translators the means to make appropriate choices of terminology
  3. Mu, T.; Goulermas, J.Y.; Korkontzelos, I.; Ananiadou, S.: Descriptive document clustering via discriminant learning in a co-embedded space of multilevel similarities (2016) 0.00 
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    Abstract
    Descriptive document clustering aims at discovering clusters of semantically interrelated documents together with meaningful labels to summarize the content of each document cluster. In this work, we propose a novel descriptive clustering framework, referred to as CEDL. It relies on the formulation and generation of 2 types of heterogeneous objects, which correspond to documents and candidate phrases, using multilevel similarity information. CEDL is composed of 5 main processing stages. First, it simultaneously maps the documents and candidate phrases into a common co-embedded space that preserves higher-order, neighbor-based proximities between the combined sets of documents and phrases. Then, it discovers an approximate cluster structure of documents in the common space. The third stage extracts promising topic phrases by constructing a discriminant model where documents along with their cluster memberships are used as training instances. Subsequently, the final cluster labels are selected from the topic phrases using a ranking scheme using multiple scores based on the extracted co-embedding information and the discriminant output. The final stage polishes the initial clusters to reduce noise and accommodate the multitopic nature of documents. The effectiveness and competitiveness of CEDL is demonstrated qualitatively and quantitatively with experiments using document databases from different application fields.

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