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

1) Candidate: graph


Is in goldstandard

1
paper corpusSignosTxtLongLines368 - : We selected the two characters that best represented the internal and external attribution dimension (x-axis in the graph): the main character and the shopkeeper . In order to assess if there are differences of blame between English and Spanish- speaking participants, as well as the four types of agentive wording, a two-way analysis of variance (ANOVA) was conducted to assess level of responsibility for the main character in the scenario presented where a vase was broken. With respect to how much participants held the main character in the scenario responsible, language (Spanish M = 4.56, SD = 2.41, or English M = 4.74, SD = 1.39) did not significantly affect blame, F(1, 112) = 1.92, MSE = 3.72, p = .17. Agentive wording across the four conditions (M = 4.64, SD = 1.99) also did not significantly affect the extent to which the main character in the scenario was blamed, F(3, 112) = 0.72, MSE = 3.72, p = .54. Finally, there was no interaction between language and agentive wording, F(1, 112) =

2
paper corpusSignosTxtLongLines382 - : Raskin is credited with a significant contribution to the notion of ‘script’, a central concept of the Semantic Script Theory of Humour (SSTH). The script is defined as a cognitive structure that "represents the native speaker’s knowledge of a small part of the world" and contains semantic information about a word or information evoked by it. Formally, it can be represented by "a graph with lexical nodes and semantic links between the nodes" (Raskin, 1985: 81 ). The scripts store encyclopaedic information and express approximations of reality.

3
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

4
paper corpusSignosTxtLongLines438 - : Acarturk, C., Habel, C. & Cagiltay, C. (2008). Multimodal comprehension of graphics with textual annotations: The role of graphical means relating annotations and graph lines . En G. Stapleton, J. Howse & J. Lee (Eds.), Diagrammatic Representation and Inference: 5th International Conference, Diagrams (pp. 335-343). Herrsching, Alemania. [ [35]Links ]

5
paper corpusSignosTxtLongLines485 - : Graph 1 Results: Frequency of textual genres in the medical domain .

6
paper corpusSignosTxtLongLines485 - : Graph 2 Results: Difficulty of textual genres in the medical domain .

7
paper corpusSignosTxtLongLines485 - : Graph 3 Results: Overall frequency and difficulty for textual genres in the medical domain .

8
paper corpusSignosTxtLongLines485 - : Graph 4 Results: Frequency of textual genres in the tourism domain .

9
paper corpusSignosTxtLongLines485 - : Graph 5 Results: Difficulty of textual genres in the tourism domain .

10
paper corpusSignosTxtLongLines485 - : Graph 6 Results: Overall frequency and difficulty of textual genres in the tourism domain .

11
paper corpusSignosTxtLongLines485 - : Graph 7 Results: Frequency and difficulty of textual genres in the public administration .

12
paper corpusSignosTxtLongLines485 - : Graph 11 Overall results: Writing difficulties in the three domains studied .

Evaluando al candidato graph:


1) textual: 8 (*)
3) genres: 7 (*)
4) semantic: 7 (*)
5) domain. : 6
6) representation: 5 (*)
7) difficulty: 5
9) conceptual: 4
12) overall: 3
14) wording: 3 (*)
15) tourism: 3 (*)
16) agentive: 3
18) medical: 3
19) scenario: 3

graph
Lengua: eng
Frec: 31
Docs: 11
Nombre propio: 8 / 31 = 25%
Coocurrencias con glosario: 6
Puntaje: 7.155 = (6 + (1+5.93073733756289) / (1+5)));
Candidato aceptado

Referencias bibliográficas encontradas sobre cada término

(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)
graph
: Carpenter, P. & Shah, P. (1998). A model of the perceptual and conceptual processes in graph comprehension. Journal of Experimental Psychology: Applied, 4(2), 75-100.
: Leskovec, J., Grobelnik, M. & Milic-Frayling, N. (2004). Learning semantic graph mapping for document summarization. Ponencia presentada en ECML/PKDD-2004 Workshop on Knowledge Discovery and Ontologies, Pisa, Italia.
: 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.
: Navigli, R. & Lapata, M. (2010). An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE transactions on pattern analysis and machine intelligence, 32(4), 678-692.
: Pinker, S. (1990). A theory of graph comprehension. En R. Freedle (Ed.), Artificial intelligence and the future of testing (pp. 73-126). Hillsdale, NJ: Erlbaum.
: Shah, P. & Freedman, E. G. (2011). Bar and line graph comprehension: An interaction of top‐down and bottom‐up processes. Topics in Cognitive Science, 3(3), 560-578.
: 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.