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

1) Candidate: errors


Is in goldstandard

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paper corpusSignosTxtLongLines356 - : Abstract: The current study provides a descriptive analysis of the orthographic performance of Chilean children that attend public schools. In order to address the recurrent errors, a total of 250 narrative texts from students of 3rd -, 5th- and 7th grade were collected. Stories were obtained from a topic-continuing written prompt. To analise the errors, a rubric was created considering the following error categories: omission of accent marks, erroneous use of spelling (b/v ; s/c/z; h), hyposegmentation, and omission or commission of syllables and/or letters. The rubric was based on the linguistic norm and on the phonologic simplification phenomenon. An analysis of frequencies and an Index of errors related to total written words showed that the orthographic errors occurred in 17% of written words. These errors were normally attested in frequently used words such as verbs ‘haber’, ‘hacer’ and ‘estar’ In addition, most recurrent errors relate to the omission of accent marks (mostly in last

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paper corpusSignosTxtLongLines415 - : “To sum up, the statistical grammar checker will fail to capture errors if the errors are not word combination problems or they involve problems of non-adjacent word strings or conflicts across different clause boundaries” (Chen, 2009: 175 ).

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paper corpusSignosTxtLongLines415 - : El usuario no debería olvidar que no se trata de una herramienta infalible, sino que es, más bien, “a flagging tool which brings possible errors to their attention” (Jacobs & Rodgers, 1999: 523 ). De manera que, igual que cuando, por ejemplo, estamos escribiendo en español e introducimos una cita en inglés, el corrector del procesador de textos nos la subraya, y no por ello consideramos que hemos cometido un error; pues, CorrectMe también llamará la atención sobre combinaciones de palabras que, simplemente, son poco frecuentes, pero no por ello, erróneas. Este programa exige que el usuario no sea un mero receptor pasivo de la información, sino que sea capaz de intuir la causa de los avisos que le envía. Además, es conveniente que el usuario sea consciente de la necesidad de complementar la utilización del corrector con el Diccionario de la lengua española y el Diccionario panhispánico de dudas de la RAE, y con otras utilidades como el Corpus del español actual (CREA) o WebCorp (Renouf, Keho

4
paper corpusSignosTxtLongLines416 - : The issues listed above caused errors on both datasets: FactSpaCIC and RawWeb . Below we describe some issues that did not occur in the grammatically correct dataset FactSpaCIC (possibly due to its limited size), yet they occurred on the (larger) RawWeb dataset.

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paper corpusSignosTxtLongLines416 - : Figure 2. Distribution of types of errors by issues for each dataset. R stands for the RawWeb dataset, F for FactSpaCIC. The issues are indicated with numbers: 1: underspecified noun phrase, 2: overspecified verb phrase, 3: non-contiguous verb phrase, 4: N-ary relation, 5: conditional clause, 6: relative clause, 7: coordinate structure, 8: inverse word order, 9: incorrect POS-tagging, 10: grammatical errors, 11: others .

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paper corpusSignosTxtLongLines416 - : A similar situation is observed for grammatical errors (10): although they are expected to affect all components of the extractions, detection of arguments is affected more frequently than that of relation phrases . To verify this, further experiments with a larger dataset are needed. Grammatical errors are inherent to informal communication. A preprocessing stage of intelligent automatic grammar correction could solve this problem. However, this lies far beyond the area of information extraction.

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paper corpusSignosTxtLongLines416 - : We have analyzed in detail the errors typical to the method of Open IE based on heuristic rules over POS-tags. No detailed description or accurate classification of the errors had been reported before, although some types of errors along with some issues were mentioned by Fader et al. (2011), but not distinguished. We have distinguished between errors and their sources. We have classified all information extraction errors into four types based on the component of an extracted fragment where an error occurred: incorrect relation phrase, incorrect arguments, incorrect argument order, and incorrect arguments with correct relation phrase . This classification is complete: it covers all possible errors.

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paper corpusSignosTxtLongLines451 - : Traditional language courses teach pronunciation and auditory recognition of L2 phonemes commonly using four basic steps:(1) presentation/explanation, (2) imitation, (3) adjustment, and (4) recognition (^[28]Celce-Murcia, Brinton & Goodwin, 2010). First, the instructor describes what position the articulatory organs must take and how they must move in order to produce the target sound or sound combination; second, the learner listens to words with the target sound and repeats them; third, the teacher provides feedback and identifies, explains, and corrects errors with relevant exercises until production of the target sound is appropriate depending on the orientation of the course and the learner’s level ; fourthly and finally, the learner listens to input and discriminates between a word with the target sound and a word without it.

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paper corpusSignosTxtLongLines451 - : Basically, all phoneme errors can be classified into three types which we present in the following three subsections, respectively, (1) substitution of an AE phoneme by an MS phoneme, (2) insertion of an MS phoneme in an AE word, and (3) deletion of an AE phoneme. There are two main reasons which explain why pronunciation errors are made: the first reason is phonetic, that is, a given AE sound does not exist in MS or if it exists, it differs in some way ; the second reason is orthographic, when the MS reading rules are applied to AE words. For example, ‘haste’ may be read as ENT[eɪstENT] instead of ENT[heɪstENT] because the letter h is not pronounced in all contexts in Spanish. However, knowing that the English h must be pronounced, an MS learner may read it as voiceless velar /x/ instead of AE voiceless glottal /h/ since /x/ is the MS consonant most similar to the AE /h/.

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paper corpusSignosTxtLongLines485 - : competence. ^[77]Fregoso (2008) detects six types of difficulties in writing: graphematic, verbal, lexical, syntactic, semantic and orthographic difficulties. ^[78]Sabaj (2009) focuses on errors contained in participants’ compositions: he found writing errors (orthography, punctuation and vocabulary ) and genre-specific errors (formal aspects, hierarchy of information and structure). On the other hand, there are also works about writing difficulties in textual genres of specific specialized fields, such as ^[79]Reimenerink (2003) and ^[80]Moreno, Rodríguez, Vázquez, Ricardo and Rodríguez (2016) in medicine; ^[81]Rodríguez de Benítez (2014) in tourism, and ^[82]González Salgado (2009) in the judicial field. Some other works are especially focused on the study of language and genres of a specialized field, such as ^[83]Calvi (2006) for the language of tourism.

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paper corpusSignosTxtLongLines529 - : In terms of grammatical errors observed in learners’ (N = 19) written service-learning submissions, one common grammatical mistake noted across all learners is the lack of written accents in nouns, interrogative words, and verbs. A few of these errors include the following: ‘ingles’ (correct form: inglés, which signifies ‘English’), ‘sabia’ (correct form: sabía, which signifies ‘knew’), ‘disfrute’ (correct form: disfruté, which signifies ‘I enjoyed’), and ‘traduccion’ (correct form: traducción, which signifies ‘translation’ ). All participants used traducción in its correct form by the end of the service-learning program, but not at all of them wrote inglés accurately. Since this concept was not used in any of the service-learning written work, this could have been a potential factor in learners’ not utilizing and developing the correct form.

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paper corpusSignosTxtLongLines552 - : El FC se define como “the feedback that learners receive on the linguistic errors they make in their oral or written production in a second language (L2)” (^[35]Sheen & Ellis, 2011: 593 ). ^[36]Lyster y Ranta (1997) distinguen seis categorías de FC, clasificadas a su vez en FC explícito y FC implícito.

Evaluando al candidato errors:


1) correct: 8
5) grammatical: 5 (*)
8) incorrect: 5
9) phoneme: 4 (*)
10) target: 4
11) learner: 4 (*)
14) phrase: 4 (*)
15) orthographic: 4 (*)
16) learners: 4 (*)
17) signifies: 4
19) relation: 4

errors
Lengua: eng
Frec: 149
Docs: 40
Nombre propio: / 149 = 0%
Coocurrencias con glosario: 6
Puntaje: 6.811 = (6 + (1+5.6724253419715) / (1+7.22881869049588)));
Candidato aceptado

Referencias bibliográficas encontradas sobre cada término

(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)
errors
: “difficult mistakes such as *informes conteniendo (instead of informes que contenían […]) or *máscaras antigases (instead of máscaras antigás […]), which are errors that were not detected by MS Word” (Nazar & Renau, 2012: 32).
: Bohannon, J. & Stanowicz, L. (1988). The issue of negative evidence: Adult responses to children’s language errors. Developmental Psychology, 34(5), 684-689.
: Cathcart, R. & Olsen, J. (1976). Teachers’ and students’ preferences for correction of classroom conversation errors. En J. Fanselow & R. Crymes (Eds.), On TESOL ‘76 (pp. 41-53). Washington, DC.: TESOL.
: Chaudron, C. (1977). A descriptive model of discourse in the corrective treatment of learner`s errors. Language Learning, 27, 29-46.
: Chaudron, C. (1986). Teacher’s priorities in correcting learners’ errors in French immersion classes. En M. Day (Ed.), Talking to learn (pp. 64-84). Rowley, MA: Newbury House.
: Chodorow, M. & Leacock, C. (2000). An unsupervised method for detecting grammatical errors. Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Association for Computational Linguistics, 140-147 [en línea]. Disponible en:
: Chun, A. & Day, R. (1982). Errors, interaction, and correction: A study of native/non-native conversations. TESOL Quarterly, 16, 537-547.
: Corder, S. P. (1967). The significance of learners´ errors. IRAL, 5, 161-170.
: Corder, S. P. (1967). The significance of learner’s errors. International Review of Applied Linguistics in Language Teaching, 5(1-4), 161-170.
: Frazier, L. & Rayner, K. (1982). Making and correcting errors during sentence comprehension: Eye movements in the analysis of structurally ambiguous sentences. Cognitive Psychology, 14(2), 178-210.
: Gamon, M., Leacock, C., Brockett, C., Dolan, W. B., Gao, J., Belenko, D. & Klementiev, A. (2009). Using statistical techniques and web search to correct ESL errors.
: Goldstein, B. A. & Pollock, K. E. (2000). Vowel errors in Spanish-speaking children with phonological disorders: A retrospective, comparative study. Clinical Linguistics and Phonetics, 14, 217-225.
: Heift, T. & Schulze, M. (2007). Errors and intelligence in computer-assisted language learning. Parsers and Pedagogues. Nueva York: Routledge.
: Heift, T. (2003). Multiple learner errors and feedback: A challenge for ICALL systems. CALICO Journal, 20(3), 549-560.
: However, compared to human judgment, automatic erroneous sound detection is not at all satisfactory (^[53]Strik et al., 2009). We believe that error detection rate can be improved by using error patterns as guidelines for predicting errors in learner’s speech.
: Islam, A. & Inkpen, D. (2011). Correcting different types of errors in texts. En C. Butz & P. Lingras (Eds.), Advances in Artificial Intelligence (pp. 192-203)
: Salehi, M., Mohsen, R. & Ghasisin, L. (2017). Lexical retrieval or semantic knowledge? Which one causes naming errors in patients with mild and moderate Alzheimer’s Disease? Dementia and Geriatric Cognitive Disorders Extra, 7(3), 419-429.
: These results allowed to initially verify the description of the grammatical features of the DLI as a higher rate of errors, consistent with Leonard’s (2014) proposal.
: Willers, I. F., Feldman, M. L. & Allegri, R. F. (2008). Subclinical naming errors in mild cognitive impairment. A semantic deficit? Dementia & Nueropsychologia, 2(3), 217-222.