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Lista de candidatos sometidos a examen:
1) graphs (*)
(*) Términos presentes en el nuestro glosario de lingüística

1) Candidate: graphs


Is in goldstandard

1
paper corpusSignosTxtLongLines389 - : Abstract: In this study, we propose a model for generating single-document abstractive summaries, based on the conceptual representation of the text. Although there are studies that take into account the partial syntactic or semantic representation of the text, so far, a complete semantic representation of texts has not been used for generating summaries. Our model uses a complete semantic representation of text by means of conceptual graph structures. In this context, the task of generating the summary is reduced to summarize the set of corresponding conceptual graphs. In order to do this, a set of operations on graphs is applied: generalization, join or association, ranking, and pruning . Furthermore, a hierarchy of concepts (WordNet) and heuristic rules based on the semantic patterns from VerbNet are used in order to support such operations. The resulting set of graphs depicts the text summary at the conceptual level. The method was evaluated on the DUC 2003 data collection. The results

2
paper corpusSignosTxtLongLines438 - : Acarturk, C., Habel, C., Cagiltay, K. & Alacam, O. (2008a). Learning from text and graphs: The role of annotations and sensory modality . En A. Maes & S. Ainsworth (Eds.), Proceedings of EARLI Special Interest Group Text and Graphics. Exploiting the Opportunities Learning with Textual, Graphical and Multimodal Representations (pp. 13-16). Tilburg, Holanda. [ [36]Links ]

Evaluando al candidato graphs:


2) semantic: 4 (*)
3) representation: 4 (*)
4) conceptual: 4
5) generating: 3

graphs
Lengua: eng
Frec: 24
Docs: 10
Nombre propio: / 24 = 0%
Coocurrencias con glosario: 2
Puntaje: 2.886 = (2 + (1+4) / (1+4.64385618977472)));
Candidato aceptado

Referencias bibliográficas encontradas sobre cada término

(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)
graphs
: Acarturk, C. & Habel, C. (2012). Eye tracking in multimodal comprehension of graphs. En R. Cox & J. P. S. Diego (Eds.), Proceedings of the Diagrams Workshop on Technology-Enhanced Diagrams Research, 887, 11-25.
: Acarturk, C., Habel, C., Cagiltay, K. & Alacam, O. (2008b). Multimodal comprehension of language and graphics: Graphs with and without annotations. Journal of Eye Movement Research, 1(3), 2, 1-15.
: Chein, M. & Mugnier, M. L. (2009). Graph-based knowledge representation: Computational foundations of conceptual graphs. Londres: Springer-Verlag.
: Hensman, S. (2005). Constructing conceptual graphs using linguistic resources. En M. Husak & Mastorakis (Eds.), The 4th WSEAS International Conference on Telecommunications and Informatics (pp. 1-6). Wisconsin, USA:WEAS Press.
: Mueller, D. (2012). Grasping rhetoric and composition by its long tail : What graphs can tell us about the field’s changing shape. College Composition and Communication, 64(1), 195-224.
: Namata, G. M. & Getoor, L. (2009). A pipeline approach to graph identification. Ponencia presentada en el International Workshop on Mining and Learning with Graphs, Leuven, Bélgica.
: Plaza, L. (2010). The use of semantic graphs in automatic summarization: Comparative case studies in Biomedicine, Journalism and Tourism. Tesis doctoral, Universidad Complutense de Madrid, Madrid, España.
: Shah, P., Mayer, R. & Hegarty, M. (1999). Graphs as aids to knowledge construction: Signaling techniques for guiding the process of graph comprehension. Journal of Educational Psychology, 91(4), 690-702.
: Tsatsaronis, G., Varlamis, I. & Nørvåg, K. (2010). SemanticRank: Ranking keywords and sentences using semantic graphs. Ponencia presentada en el 23rd International Conference on Computational Linguistics, Beijing, China.