/LING
|
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) automatic (*)
(*) Términos presentes en el nuestro glosario de lingüística
1) Candidate: automatic
Is in goldstandard
1
paper corpusSignosTxtLongLines336 - : Before formulating the linguistic point we are going to test via computer experiments, we will first localize it within the vast realm of linguistics. Our statement is concerned with the concept of collocation, one of contemporary controversial issues in theoretical and applied linguistics. Knowledge of collocation is very important in lexicology (Herbst & Mittmann, 2008), translation (Boonyasaquan, 2006), language acquisition (Handl, 2008), and in various tasks of automated processing of natural language (e.g., in automatic word sense disambiguation: Jin, Sun, Wu & Yu, 2007 ; in machine translation: Wehrli, Seretan, Nerima & Russo, 2009; in text classification: Williams, 2002, etc.).
2
paper corpusSignosTxtLongLines374 - : Ronquillo, F., Pérez de Celis, C., Sierra, G., da Cunha, I. & Torres-Moreno, J. (2011). Automatic classification of biomedical texts: Experiments with a hearing loss corpus . En Y. Ding, Y. Peng, R. R. Shi, K. Hao & L. Wang (Eds.), 4th International Conference on Biomedical Engineering and Informatics (pp. 1674-1679). Shanghai, China: IEEE. [ [58]Links ]
3
paper corpusSignosTxtLongLines455 - : Current approaches for automatic WSD can be classified into four groups according to the methodology employed for selecting the correct sense of the word to be disambiguated: supervised, unsupervised, semi-supervised, and knowledge-based approaches (^[52]Borah et al ., 2014; ^[53]Nandanwar & Mamulkar, 2015).
4
paper corpusSignosTxtLongLines507 - : Koza, W., Filippo, D., Cotik, V., Stricker, V., Muñoz, M., Rivas, N., Godoy, N. & Martínez, R. (2018). Automatic detection of negated findings in radiological reports for Spanish language: Methodology based on Lexicon-Grammatical information processing . Journal of Digital Imaging. First Online. [ [172]Links ]
5
paper corpusSignosTxtLongLines559 - : TAALES2.0 (Tool for the Automatic Assessment of Lexical Sophistication) by ^[41]Kile, Crossley and Berger, (2018), is an AWE tool that computes numerous indices related to: word frequency (less frequent words are considered more sophisticated or complex ), word range (number of documents containing particular elements), n-gram frequency (set of infrequent terms that relate to the quality of the text), n-gram range, n-gram strength of association, contextual distinctiveness (measures the diversity of the context in which a word occurs), semantic network and word neighbors (words that share phonological, phonographic and orthographic similarities). This tool has been applied to L1 and L2 (Second Language) students to predict holistically the lexical proficiency. The authors discarded variables that did not comply with a minimum correlation, in addition to variables that presented multicollinearity. The final model included ten variables, which explain the 58% variance in the lexical
Evaluando al candidato automatic:
3) variables: 3 (*)
4) tool: 3 (*)
5) n-gram: 3 (*)
automatic
Lengua: eng
Frec: 139
Docs: 47
Nombre propio: 3 / 139 = 2%
Coocurrencias con glosario: 3
Puntaje: 3.532 = (3 + (1+3.32192809488736) / (1+7.12928301694497)));
Candidato aceptado
Referencias bibliográficas encontradas sobre cada término
(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)| automatic |
: Alfonseca, E. & Pérez, D. (2004). Automatic assessment of short questions with a Bleu-inspired algorithm and shallow NLP. Ponencia presentada en the 4th International Conference, EsTAL 2004, Alicante, España. : Ananiadou, S. (1994). A methodology for automatic term recognition. Ponencia presentada en el 15th International Conference on Computational Linguistics, Kyoto, Japón. : Athanaselis, T., Bakamidis, S. & Dologlou, I. (2006). An automatic method for revising ill-formed sentences based on : Bordag, S. (2006). Word sense induction: Triplet-based clustering and automatic evaluation. Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL) (pp. 137-144). : Cabré, M. T., Estopà, R. & Vivaldi., J. (2001). Automatic term detection: A review of current systems. En D. Bourigault, C. Jacquemin & M. C. L´Homme (Eds.), Recent Advances in Computational Terminology (pp. 1-28). Amsterdam: John Benjamins. : Cerviño, U., García, J., Calvo, R. & Ceccatto, A. (2004). Automatic classification of news articles in Spanish [en línea]. Disponible en: [34]http://citeseer.ist.psu.edu/beresi04automatic.html : Fink, E. (2004). Automatic evaluation and selection of problem-solving methods: Theory and experiments. Journal of Experimental and Theoretical Artificial Intelligence, 16(2), 73-105. : Frantzi, K. & Ananiadou, S. (1997). Automatic term recognition using contextual cues. Ponencia presentada en el IJCAI Workshop on Multilinguality in Software Industry, AIC, Japón. : Frantzi, K., Ananiadou, S. & Hideki, M. (2000). Automatic recognition of multi-word terms: The C-value/NC-value method. International Journal of Digital Libraries, 3(2), 115-130. : Ghiasinejad, S. & Golden, R. (2002). An empirical evaluation of the AUTOCODER system for automatic semantic coding of children summarization data. Poster presentado en the 12th Annual Meeting of the Society for Text and Discourse, Chicago, Estados Unidos de Norteamérica. : Gildea, D. & Jurafsky, D. (2002). Automatic labeling of semantic roles. Computational Linguistics, 28, 245-288. : Girju, R., Badulescu, A. & Moldovan, D. (2006). Automatic discovery of part-whole relations. Computational Linguistics, 32(1), 83-135. : Grefenstette, G. (1994). Explorations in automatic thesaurus discovery. Norwell, MA: Kluwer Acad. : Hasting, A. S., Kotz, S. A. & Friederici, A. D. (2007). Setting the stage for automatic syntax processing: The mismatch negativity as an indicator of syntactic priming. Journal of Cognitive neuroscience, 19(3), 386-400. : Hearts, M. (1992). Automatic acquisition of hyponyms from large text corpora. En Proceedings of Conference COLING. Nantes: Association for Computational Linguistics. : Hovy, E. & Chin-Yew, L. (1999). Automating text summarization in SUMMARIST. En I. Mani & M. T. Maybury (Eds.), Advances in Automatic Text Summarization (pp. 81-94). Cambridge, MA: MIT Press. : 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. : Ide, N., Erjavec, T. & Tufis, D. (2001). Automatic sense tagging using parallel corpora. In Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium -NLPRS (pp. 83-90). : Kageura, K. & Umino, B. (1996). Methods of automatic term recognition. Terminology, 3(2), 259-290. : Kile, K., Crossley, S. & Berger, C. (2018). The tool for the automatic analysis of lexical sophistication (TAALES): Version 2.0. Behavior Research Methods, 50(3), 1030-1046. : Kolesnikova, O. (2011). Automatic extraction of lexical functions. Tesis doctoral, Instituto Politécnico Nacional, Ciudad de México, México. : Laberge, D. & Samuels, S. J. (1994). Toward a theory of automatic information processing of reading. En H. Singer & B. Rudell (Eds.) Theoretical models and processes of reading (pp. 689-718). Newark, Delaware: IRA & Erlbaum. : Lan, M., Tan, C. L., Su, J. & Lu, Y. (2009). Supervised and traditional term weighting methods for automatic text categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), 721-735. : Larkey, L. (1998). Automatic essay grading using text categorization techniques. Proceedings of the 21^st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, U.S.A. : Lesk, M. (1986). Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. Proceedings of the 5th annual international conference on Systems documentation (pp. 24-26). ACM. : Lin, Ch. & Hovy, E. (2003). Automatic evaluation of summaries using n-gram co-occurrence statistics. En Actas del 2003 Language Technology Conference, Edmonton, Canadá . : Lin, Ch. (2004). ROUGE: A Package for Automatic Evaluation of Summaries. En actas del Workshop on Text Summarization Branches Out, Post-Conference Workshop of ACL 2004, Barcelona, España. : Lu, X. (2010). Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics, 15(4), 474-496. : Lucero, C., Pinto, D. & Jiménez, H. (2004). A tool for automatic detection of antonymy relations. Ponencia presentada en el IX Ibero-American Conference on Artificial Intelligence, Puebla, México. : Luhn, H. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2), 159-165. : López-de-Ipiña, K., Alonso, J. B., Travieso, C. M., Solé-Casals, J., Egiraun, H., Faundez-Zanuy, M. & Lizardui, U. M. D. (2013). On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors, 13(5), 6730-6745. : Mani, I. & Maybury, M. (2001). Advances in automatic text summarization. Massachusetts: Massachusetts Institute of Technology. : Maynard, D. & Ananiadou, S. (2000). TRUCKS: A model for automatic multi-word term recognition. Journal of Natural Language Processing, 8(1), 101-125. : Menzel, W., Herron, D., Bonaventura, P. & Morton, R. (2000). Automatic detection and correction of non-native English pronunciations. Proceedings of INSTILL, 49-56. : Nenkova, A. & McKeown, K. (2011). Automatic summarization. Foundations and Trends in Information Retrieval, 5(2-3), 103-233. : Nenkova, A. (2005). Automatic text summarization of newswire: Lessons learned from the document understanding conference. En M. Veloso & S. Kambhampati (Eds.), The 20th National Conference on Artificial Intelligence (pp. 1436-1441). Menlo Park, CA: AAAI Press. : Nieuwland, M. S. (2015b). The truth before and after: Brain potentials reveal automatic activation of event knowledge during sentence comprehension. Journal of Cognitive Neuroscience, 27(11), 2215-2228. doi: 10.1162/jocn_a_00856 : Papineni, K., Roukos, S., Ward, T. & Zhu, W. (2001). BLEU: A method for automatic evaluation of machine translation. Technical Report RC22176 (W0109-022), IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New York, U.S.A. : 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. : Pérez, D. & Alfonseca, E. (2005). Adapting the automatic assessment of free-text answers to the students. Proceedings of the 9^th Computer Assisted Assessment (CAA) international conference, Loughborough, U.K. : Ratcliff, R. & McKoon, G. (2008). Passive parallel automatic minimalist processing. En C. Engel & W. Singer (Eds.), Better than conscious? Decision making, the human mind, and implications for institutions (pp. 176-189). Cambridge, MA: MIT Press. : Rocchio, J. (1971). Relevance feedback in information retrieval. En G. Salton (Ed.), The SMART Retrieval SystemExperiments in automatic document processing (pp. 313-323). New Jersey: Prentice-Hall. : Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic retrieval. Information Processing y Management, 24(5), 513-523. : Salton, G. (1968). Automatic information organization and retrieval. New York: McGraw-Hill. : Salton, G., Wong, A. & Yang, C. (1975). A Vector Space Model for Automatic Indexing. Communications of the AMC, 18(11), 613-620. : Samuels, J., & Laberge, D. (1985). Toward a theory of automatic information processing in reading. En H. Singer & R. Ruddell (Eds.), Theoretical models and processes of reading (pp. 548-579). Newark, DE: International Reading Association. : Shimizu, Y., Lee, H. & Uleman, J. S. (2017). Culture as automatic processes for making meaning: Spontaneous trait inferences. Journal of Experimental Social Psychology, 69, 79-85. : Shriberg, E., Stolcke, A., Hakkani-Tür, D. & Tür, G. (2000). Prosody-based automatic segmentation of speech into sentences and topics. Speech Communication, 32(1-2), 127-154. : Spärck Jones, K. (1999). Automatic summarising: Factors and directions. En I. Mani & M. T. Maybury (Eds.), Advances in Automatic Text Summarization (pp. 1-12). Cambridge MA: MIT Press. : Spärck Jones, K. (2007). Automatic summarising: The state of the art. Information Processing & Management, 43(6), 1449-1481. : Strange, W. (2011). Automatic selective perception (ASP) of first and second language speech: A working model. Journal of phonetics, 39(4), 456-466. : Strik, H., Truong, K., de Wet, F. & Cucchiarini, C. (2009). Comparing different approaches for automatic pronunciation error detection. Speech Communication, 51(10), 845-852. : Teufel, S., Siddharthan, A. & Tidhar, D. (2006). Automatic classification of citation function. Ponencia presentada en the Conference on Empirical Methods in Natural Language Processing, Sydney, Australia. : Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z. & Kálmán, J. (2018). A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech. Current Alzheimer Research, 15(2), 130-138. : Truong, K., Neri, A., Cucchiarini, C. & Strik, H. (2004). Automatic pronunciation error detection: An acoustic-phonetic approach. In Proceedings of InSTIL/ICALL Symposium, 135-138. : Wanner, L. (2004). Towards automatic fine-grained semantic classification of verb-noun collocations. Natural Language Engineering, 10(2), 95–143. : You, W., Fontaine, D. & Barthes, J. P. (2013). An automatic keyphrase extraction system for scientific documents. Knowledge and Information Systems, 34(3), 691-724. : Yu, D. & Deng, L. (2012). Automatic speech recognition. Berlin-Heidelberg: Springer. : Yulan, H., Siu, Ch. & Tho, Th. (2009). Automatic summary assessment for intelligent tutoring systems. Computers & Education, 53, 890- 899. : Zhao, T., Hoshino, A., Suzuki, M., Minematsu, N. & Hirose, K. (2012). Automatic Chinese pronunciation error detection using SVM trained with structural features. InProceedings of Spoken Language Technology Workshop (SLT), IEEE, 473-478. : Zipitria, I., Elorriaga, J., Arruate, A. & de IIarraza, A. (2004). From human to automatic summary evaluation. En Seventh International Conference on Intelligent Tutoring System, Maceió, Brasil. : da Cunha, I., Montané, M. A. & Hysa, L. (2017). The arText prototype: An automatic system for writing specialized texts. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Software Demonstrations, 57-60. |