/LING
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Update: February 24, 2023 The new version of Termout.org is now online, so this web site is now obsolete and will soon be dismantled. |
Lista de candidatos sometidos a examen:
1) frequency (*)
(*) Términos presentes en el nuestro glosario de lingüística
1) Candidate: frequency
Is in goldstandard
1
paper corpusSignosTxtLongLines276 - : Total type frequency: 23 Total token frequency: 66
2
paper corpusSignosTxtLongLines276 - : Total type frequency: 23 Total token frequency: 41
3
paper corpusSignosTxtLongLines276 - : Total type frequency: 3 Total token frequency: 3
4
paper corpusSignosTxtLongLines276 - : Total type frequency: 10 Total token frequency: 37
5
paper corpusSignosTxtLongLines393 - : The ‘tf’ component of the formula is calculated by the normalised frequency of the term, whereas the ‘idf’ is obtained by dividing the number of documents in the corpus by the number of documents which contain the term; and then taking the logarithm of that quotient. Given a corpus D and a document dj (dj D), the ‘tf-idf’ value for a term ti in dj is obtained by the product between the normalised frequency of the term ti in the document dj(tfij) and the inverse document frequency of the term in the corpus (idf(ti)) as follows:
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paper corpusSignosTxtLongLines454 - : Regarding pronouns in the plural, the use of first person forms such as ‘we’ and ‘us’ in online communities may express solidarity with the support group (^[99]Arguello et al., 2006). The lexical analysis of this corpus evidences that in English, however, first person plural forms rank low on the frequency list in both genders: ‘we’ at position 150 and ‘us’ at 901 out of a total of 1,927 . In Spanish, by contrast, and especially in women’s groups, we find the possessive nos, an inclusive engagement marker, in very remarkable positions, at position number 70 out a total of 2,138 words on the frequency list. This may corroborate the findings of other cross-cultural studies which suggest that Spanish nationals use the Spanish language in positive politeness-oriented ways which emphasises in-group involvement and relations (^[100]Mur-Dueñas, 2007; ^[101]Lorenzo-Dus & Bou-Franch, 2013).
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paper corpusSignosTxtLongLines455 - : [135]Table 6 shows that ABWSD[min] is just better than the Gloss-centered algorithm with a 57.28 over 56.45, respectively. The Semcor corpus was used for this comparison. In ^[136]Ramakrishnan et al., (2004) they reported several experimental results under different conditions, but the only result to which the ABWSD could be compared was the one without ‘full content expansion’, without stemming, one-sentence context window, and using the hypernyms instead of the gloss description. It is noteworthy that, due to the Gloss-centered uses a most frequent sense strategy for those cases when it is not able to provide an answer. The ABWSD was adjusted to use the same strategy. However, this adjustment is only for comparison purposes, since if a system uses “sense frequency information that is only obtainable from sense-annotated corpora, it is essentially a supervised system” (^[137]Wang & Hirst, 2014: 534 ).
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paper corpusSignosTxtLongLines471 - : autoras aquí son más cautelosas sobre la intuición de los hablantes: “It seems that it is almost impossible to answer the question of whether or not language users have accurate intuitions about collocation frequency; it all depends on the frequency range in question […]” (^[155]Siyanova & Spina, 2015: 555 ).
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paper corpusSignosTxtLongLines474 - : Another of the analysis variables was the use of modalizers and lexicalized expressions such as por favor ‘please’ and gracias ‘thank you’. It is confirmed that Spanish presents a lower frequency of their usage in comparison with English, for example. In the great majority of the emails the students ask for something: Information, favours, etc. Nonetheless, a high frequency in the use of por favor and gracias was not observed. It was observed that in corpus 1, in 79.7% of the emails, the expression por favor was not used even once. The word gracias normally appears in the signing off formulas, but even so in 39% of emails (corpus 1) it is not used once. It is fitting to add that the use of quantifiers occurs again with much frequency in the signing off: Muchísimas gracias ‘very many thanks’, mil gracias ‘a thousand thank yous’, etc .
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paper corpusSignosTxtLongLines599 - : One of the most interesting of Peacock’s findings was the identification of booster clusters, the use of which resulted in a notable increase in the persuasive force of boosting. Regarding the cross-disciplinary variation in the frequency of booster use, writers in the two sciences (Physics and Materials Science, as well as Environmental Science) used a much higher proportion of boosters of the “evidential or implicit truth” type (^[92]Peacock, 2006: 73 ), such as ‘show’, ‘demonstrate’, ‘find’ or ‘establish’ than authors in other disciplines. Peacock suggests that writers in sciences seek to minimise their personal involvement in their findings and appear more objective, which is in accordance with ^[93]Hyland’s (1998a) line of argumentation. In addition, however, they present their claims as evidential, that is, fully based on convincing data rather than being attributable to the writer’s persuasive skills. The author argues that the choice of the verbs mentioned may reflect a disti
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paper corpusSignosTxtLongLines599 - : As can be seen in [108]Table 4, the highest frequency (4.363) was registered in the Linguistics corpus, while the lowest (3.083) in Medicine. Engineering registered the medium normalised frequency of 3.428 occurrences per 1,000 words. In comparison to the frequency data in the previous studies, the average frequency obtained here for Linguistics is similar to the figure reported by ^[109]Hyland (1998): 4 .6 per 1,000 words, but considerably lower than that given in ^[110]Peacock (2006): 10.58 occurrences per 1,000 words. As has already been pointed out, many of the corpus analysis results depend on the corpus design and the method of analysis. This study has used the smallest number of boosters (57), compared to ^[111]Hyland (1998a) with 61 and ^[112]Peacock (2006) with 116 items, and this is clearly reflected in the range of the frequencies obtained, especially for Linguistics (10.98 in ^[113]Peacock, 2006). The differences regarding Engineering (3.428) are not so notable, as the frequency
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paper corpusSignosTxtLongLines599 - : Another significant finding is the three most frequent verb boosters, which were found to be the same in the three corpora: ‘show’, ‘determine’ and ‘demonstrate’. This pattern is in line with the previous finding of the high number of overlapping verb boosters in the three corpora. In addition, if a mean frequency of verb boosters is calculated for each corpus , the following values are given: 0 .090 for Engineering, 0.061 for Medicine and 0.101 for Linguistics. This was used to identify the verb boosters with a significant normalised frequency, that is, a frequency above the mean value (highlighted in bold in [118]Table 4). There are five verbs like that in the Engineering corpus (‘show’, ‘determine’, ‘demonstrate’, ‘prove’ and ‘hold’) and four verbs in the Medicine and Linguistics corpora (‘show’, ‘determine’, ‘demonstrate’ and ‘establish’). As can be seen, a few different verbs appear on these two lists: ‘prove’ and ‘hold’ in Engineering and ‘establ
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paper corpusSignosTxtLongLines600 - : [2]vol.54 número106 [3]A cross-disciplinary study of verb boosters in research articles from Engineering, Medicine and Linguistics: Frequency and co-text variations [4]English L2 connectives in academic bilingual discourse: A longitudinal computerised analysis of a learner corpus [5] índice de autores [6]índice de materia [7]búsqueda de artículos [8]Home Page [9]lista alfabética de revistas
Evaluando al candidato frequency:
1) corpus: 13 (*)
3) boosters: 7
4) engineering: 6
5) peacock: 6
7) verb: 5 (*)
8) linguistics: 5 (*)
12) demonstrate: 4
13) normalised: 4
14) verbs: 4 (*)
15) obtained: 4
18) show: 4
19) token: 4 (*)
frequency
Lengua: eng
Frec: 265
Docs: 84
Nombre propio: 1 / 265 = 0%
Coocurrencias con glosario: 5
Frec. en corpus ref. en eng: 211
Puntaje: 5.780 = (5 + (1+6.06608919045777) / (1+8.05528243550119)));
Rechazado: muy común;
Referencias bibliográficas encontradas sobre cada término
(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)| frequency |
: Adelman, J. S., Brown, G. D. & Quesada, J. F. (2006). Contextual diversity, not word frequency, determines word-naming and lexical decision times. Psychological Science, 17(9), 814-823. : Balota, D. & Chumbley, J. (1984). Are lexical decisions a good measures of lexical access? The role of word frequency in the neglected decision stage. Journal of Experimental Psychology: Human perception and performance, 10, 340-357. : Carreiras, M. & Perea, M. (2004). Naming pseudowords in Spanish: Effects of syllable frequency. Brain and Language, 90(1-3), 393-400. : Colé, P., Beauvillain, C. & Segui, J. (1989). On the representation and processing of prefixed and suffixed derived words. A differential frequency effect. Journal of Memory and Language, 28, 1-13. : Crossley, S. A., Salsbury, T. & McNamara, D. S. (2010a). The development of polysemy and frequency use in English second language speakers. Language Learning, 60, 573-605. : Davies, M. (2006). A frequency Dictionary of Spanish. Core vocabulary for learners. Nueva York: Routledge. : Davis, M. & Gardner, D. (2010). A frequency dictionary of contemporary American English word sketches, collacates, and thematic lists. London: Routledge. : Dufour, S. & Peereman R. (2004). Phonological priming in auditory word recognition: Initial overlap facilitation effect varies as a function of target word frequency [en línea]. Disponible en línea: [33]http://cpl.revues.org/document437.html : En la investigación realizada por el ^[78]Alfaro y Allende (2011) se presentaron resultados preliminares de la representación Relevance frequency for a label, tf-rfl. Esta representación se describe en la siguiente ecuación, como una nueva representación para problemas multi-etiqueta. : Figure 2. Frequency of el tablet vs. frequency of la tablet on Twitter, 16 May-15 June 2015 (Topsy). : Franco, P., Pino, M. & Rodríguez, B. (2009). Tipología y frecuencia del uso de estrategias en el aprendizaje del inglés como lengua extranjera Types and frequency of use of strategies in the learning. Enseñanza & Teaching, 27(2), 171-191. : Goh, O. & Foong, K. (1997). Chinese ESL students’ learning strategies: A look at frequency, proficiency, and gender. Hong Kong Journal of Applied Linguistics. 2(1), 39-53. : Hintzman, D. (1988). Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95, 528-551. : In order to start answering the main research question [95]Table 1 provides the total number of CSs produced by the learners in each task, the total amount of language generated in each task, and the normalised frequency of strategies per 1000 words. : Krauss, R. M. & Weinheimer, S. (1964). Changes in reference phrases as a function of frequency of usage in social interaction: A preliminary study. Psychonomic Science, 1(1-12), 113-114. : Matthiessen, C. (2006). Frequency profiles of some basic grammatical systems: An interim report. En G. Thompson & S. Hunston (Eds.), System and Corpus. Exploring Connections (pp. 103-142) Londres: Equinox. : McGee, I. D. (2008). Word frequency estimates revisited - A response to Alderson (2007). Applied Linguistics, 29(3), 509-514. : Partington, A. & Morley, J. (2002). From frequency to ideology: Investigating word and cluster/bundle frequency in political debate. Ponencia presentada en Teaching & Language Corpora (TALC, 2002) en The University Residential Centre, Bertinoro, Italy. : Rayner, K. & Duffy, S. A. (1986). Lexical complexity and fixation times in reading: Effects of word frequency, verb complexity, and lexical ambiguity. Memory and Cognition, 14,191-201. : Rayner, K., Ashby, J., Pollatsek, A. & Reichle, E. (2004). The effects of frequency and predictability on eye fixations in reading: Implications for the E-Z Reader Model. Journal of Experimental Psychology & Human Perception Performance, 30(4), 720-32. : Reichle, E. D. & Perfetti, C. A. (2003). Morphology in word identification: A word- experience model that accounts for morpheme frequency effects. Scientific Studies of Reading, 7(3), 219-237. : Rott, S. (1999). The effect of exposure frequency on intermediate language learners’ incidental vocabulary acquisition and retention through reading. Studies in Second Language Acquisition, 21(4), 589-619. : Saviciüté, E., Ambridge, B. & Pine, J. M. (2018). The roles of word-form frequency and phonological neighbourhood density in the acquisition of Lithuanian noun morphology. Journal of Child Language, 45, 641-672. : Scheibman, J. (2001). Local patterns of subjectivity in person and verb type in American English conversation. En J. Bybee & P. Hopper (Eds.), Frequency and the Emergence of Linguistic Structure (pp.61-89). Ámsterdam: John Benjamins. : Siyanova, A. & Spina, S. (2015). Investigation of native speaker and second language learner intuition of Collocation frequency. Language Learning, 65(3), 533-562. : Sun, Q., Shaw, D. & Davis, Ch. (1999). A model for estimating the occurrence of same-frequency words and the boundary between high- and low frequency words in texts. Journal of the American Society for Information Science, 50(3), 280-286. : Trueswell, J. (1996). The role of lexical frequency in syntactic ambiguity resolution. Journal of Memory and Language, 35, 566-585. : Vincze, O. & Alonso Ramos, M. (2013b). Incorporating frequency information in a Collocation dictionary: Establishing a methodology. Procedia -Social and Behavioral Sciences, 95, 241-248. : Wilbur, W. & Kim, W. (2009). The ineffectiveness of within-document term frequency in text classification. Journal of Information Retrieval, 12(5), 509-525. : Zipf, G. (1932). Selected studies of the principle of relative frequency in language. Cambridge, MA: Cambridge University Press. |