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Статті в журналах з теми "Corpus method"
Waskita, Dana. "CORPUS LINGUISTICS: METHOD, THEORY, AND PRACTICE." Jurnal Sosioteknologi 16, no. 1 (April 29, 2017): 145–47. http://dx.doi.org/10.5614/sostek.itbj.2017.16.1.12.
Повний текст джерелаde Bruijn, L. M., A. Hasman, and J. W. Arends. "Automatic SNOMED classification—a corpus-based method." Computer Methods and Programs in Biomedicine 54, no. 1-2 (September 1997): 115–22. http://dx.doi.org/10.1016/s0169-2607(97)00040-0.
Повний текст джерелаXu, Gui-Xian, Chang-Zhi Wang, Li-Hui Wang, Yu-Hong Zhou, Wei-Kang Li, Hao Xu, and Qing Huang. "Semantic classification method for network Tibetan corpus." Cluster Computing 20, no. 1 (January 19, 2017): 155–65. http://dx.doi.org/10.1007/s10586-017-0742-6.
Повний текст джерелаReinert, par Max. "Classification Descendante Hierarchique et Analvse Lexicale par Contexte - Application au Corpus des Poesies D'A. Rihbaud." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 13, no. 1 (January 1987): 53–90. http://dx.doi.org/10.1177/075910638701300107.
Повний текст джерелаChitez, Madalina, and Loredana Bercuci. "Calibrating digital method integration into ESP courses according to disciplinary settings." New Trends and Issues Proceedings on Humanities and Social Sciences 7, no. 1 (July 2, 2020): 20–29. http://dx.doi.org/10.18844/prosoc.v7i1.4862.
Повний текст джерелаShima, Kazuaki, Takeshi Homma, Masataka Motohashi, Rintaro Ikeshita, Hiroaki Kokubo, Yasunari Obuchi, and Jinhua She. "Efficient Corpus Creation Method for NLU Using Interview with Probing Questions." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 5 (September 20, 2019): 947–55. http://dx.doi.org/10.20965/jaciii.2019.p0947.
Повний текст джерелаZhang, Jin Xi, Hong Zhi Yu, Ning Ma, and Zhao Yao Li. "The Phoneme Automatic Segmentation Algorithms Study of Tibetan Lhasa Words Continuous Speech Stream." Advanced Materials Research 765-767 (September 2013): 2051–54. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2051.
Повний текст джерелаHO, CHUKFONG, MASRAH AZRIFAH AZMI MURAD, RABIAH ABDUL KADIR, and SHYAMALA DORAISAMY. "COMPARING TWO CORPUS-BASED METHODS FOR EXTRACTING PARAPHRASES TO DICTIONARY-BASED METHOD." International Journal of Semantic Computing 05, no. 02 (June 2011): 133–78. http://dx.doi.org/10.1142/s1793351x11001225.
Повний текст джерелаDao, Jizhaxi, Zhijie Cai, Rangzhuoma Cai, Maocuo San, and Mabao Ban. "A method of constructing syllable level Tibetan text classification corpus." MATEC Web of Conferences 336 (2021): 06013. http://dx.doi.org/10.1051/matecconf/202133606013.
Повний текст джерелаWang, Shi, Zhujun Wang, Yi Jiang, and Huayu Wang. "Hierarchical Annotation Event Extraction Method in Multiple Scenarios." Wireless Communications and Mobile Computing 2021 (March 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/8899852.
Повний текст джерелаДисертації з теми "Corpus method"
Tariq, Mariam. "A corpus-based method for ontology acquisition." Thesis, University of Surrey, 1994. http://epubs.surrey.ac.uk/843178/.
Повний текст джерелаRayson, Paul Edward. "Matrix : a statistical method and software tool for linguistic analysis through corpus comparison." Thesis, Lancaster University, 2003. http://eprints.lancs.ac.uk/12287/.
Повний текст джерелаNorkevičius, Giedrius. "Method for creating phone duration models using very large, multi-speaker, automatically annotated speech corpus." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110201_144440-12017.
Повний текст джерелаDisertacijoje nagrinėjamos dvi iki šiol netyrinėtos problemos: 1. Lietuvių kalbos garsų trukmių prognozavimo modelių kūrimas Iki šiol visi darbai, kuriuose yra nagrinėjamos lietuvių kalbos garsų trukmės, yra atlikti kalbininkų, tačiau šie tyrimai yra daugiau aprašomosios statistikos pobūdžio ir apsiriboja pavienių požymių įtakos garso trukmei analize. Šiame darbe, mašininio mokymo algoritmo pagalba, požymių įtaka garsų trukmei yra išmokstama iš duomenų ir užrašoma sprendimo medžio pavidalu. 2. Nuo kalbos nepriklausomų garsų trukmių prognozavimo modelių kūrimo metodas, naudojant didelės apimties daugelio, kalbėtojų automatiškai, anotuotą garsyną. Dėl skirtingų kalbėtojų tarties specifikos ir dėl automatinio anotavimo netikslumų, kuriant garsų trukmės modelius visame pasaulyje yra apsiribojama vieno kalbėtojo ekspertų anotuotais nedidelės apimties garsynais. Darbe pasiūlyti skirtingų kalbėtojų tarties ypatybių normalizavimo ir garsyno duomenų triukšmo atmetimo algoritmai leidžia garsų trukmių modelių kūrimui naudoti didelės apimties, daugelio kalbėtojų automatiškai anotuotus garsynus. Darbo metu atliktas audicinis tyrimas, kurio pagalba parodoma, kad šnekos signalą sudarančių garsų trukmės turi įtakos klausytojų/respondentų suvokiamam šnekos signalo natūralumui; kontekstinės informacijos panaudojimas garsų trukmių prognozavimo uždavinio sprendime yra svarbus faktorius įtakojantis sintezuotos šnekos natūralumą; natūralaus šnekos signalo atžvilgiu, geriausiai vertinamas yra... [toliau žr. visą tekstą]
Lynn, Ethan Michael. "Getting All the Ducks in a Row: Towards a Method for the Consolidation of English Idioms." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6014.
Повний текст джерелаTheunissen, M. W. (Marthinus Wilhelmus). "Phonene-based topic spotting on the switchboard corpus." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52998.
Повний текст джерелаENGLISH ABSTRACT: The field of topic spotting in conversational speech deals with the problem of identifying "interesting" conversations or speech extracts contained within large volumes of speech data. Typical applications where the technology can be found include the surveillance and screening of messages before referring to human operators. Closely related methods can also be used for data-mining of multimedia databases, literature searches, language identification, call routing and message prioritisation. The first topic spotting systems used words as the most basic units. However, because of the poor performance of speech recognisers, a large amount of topic-specific hand-transcribed training data is needed. It is for this reason that researchers started concentrating on methods using phonemes instead, because the errors then occur on smaller, and therefore less important, units. Phoneme-based methods consequently make it feasible to use computer generated transcriptions as training data. Building on word-based methods, a number of phoneme-based systems have emerged. The two most promising ones are the Euclidean Nearest Wrong Neighbours (ENWN) algorithm and the newly developed Stochastic Method for the Automatic Recognition of Topics (SMART). Previous experiments on the Oregon Graduate Institute of Science and Technology's Multi-Language Telephone Speech Corpus suggested that SMART yields a large improvement over ENWN which outperformed competing phoneme-based systems in evaluations. However, the small amount of data available for these experiments meant that more rigorous testing was required. In this research, the algorithms were therefore re-implemented to run on the much larger Switchboard Corpus. Subsequently, a substantial improvement of SMART over ENWN was observed, confirming the result that was previously obtained. In addition to this, an investigation was conducted into the improvement of SMART. This resulted in a new counting strategy with a corresponding improvement in performance.
AFRIKAANSE OPSOMMING: Die veld van onderwerp-herkenning in spraak het te doen met die probleem om "interessante" gesprekke of spraaksegmente te identifiseer tussen groot hoeveelhede spraakdata. Die tegnologie word tipies gebruik om gesprekke te verwerk voor dit verwys word na menslike operateurs. Verwante metodes kan ook gebruik word vir die ontginning van data in multimedia databasisse, literatuur-soektogte, taal-herkenning, oproep-kanalisering en boodskap-prioritisering. Die eerste onderwerp-herkenners was woordgebaseerd, maar as gevolg van die swak resultate wat behaal word met spraak-herkenners, is groot hoeveelhede hand-getranskribeerde data nodig om sulke stelsels af te rig. Dit is om hierdie rede dat navorsers tans foneemgebaseerde benaderings verkies, aangesien die foute op kleiner, en dus minder belangrike, eenhede voorkom. Foneemgebaseerde metodes maak dit dus moontlik om rekenaargegenereerde transkripsies as afrigdata te gebruik. Verskeie foneemgebaseerde stelsels het verskyn deur voort te bou op woordgebaseerde metodes. Die twee belowendste stelsels is die "Euclidean Nearest Wrong Neighbours" (ENWN) algoritme en die nuwe "Stochastic Method for the Automatic Recognition of Topics" (SMART). Vorige eksperimente op die "Oregon Graduate Institute of Science and Technology's Multi-Language Telephone Speech Corpus" het daarop gedui dat die SMART algoritme beter vaar as die ENWN-stelsel wat ander foneemgebaseerde algoritmes geklop het. Die feit dat daar te min data beskikbaar was tydens die eksperimente het daarop gedui dat strenger toetse nodig was. Gedurende hierdie navorsing is die algoritmes dus herimplementeer sodat eksperimente op die "Switchboard Corpus" uitgevoer kon word. Daar is vervolgens waargeneem dat SMART aansienlik beter resultate lewer as ENWN en dit het dus die geldigheid van die vorige resultate bevestig. Ter aanvulling hiervan, is 'n ondersoek geloods om SMART te probeer verbeter. Dit het tot 'n nuwe telling-strategie gelei met 'n meegaande verbetering in resultate.
Huiza, Pereyra Eric Raphael. "Talking with signs: a simple method to detect nouns and numbers in a non annotated signs language corpus." Master's thesis, Pontificia Universidad Católica del Perú, 2020. http://hdl.handle.net/20.500.12404/16906.
Повний текст джерелаTrabajo de investigación
Lorenzi, Mikaela, and Sofia Bergström. ""I can tell a story that my dads friend tell me" : A corpus- and interview-based study on grammar education, with focus on verb forms." Thesis, Uppsala universitet, Institutionen för pedagogik, didaktik och utbildningsstudier, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-275268.
Повний текст джерелаOlsson, Fredrik. "Bootstrapping Named Entity Annotation by Means of Active Machine Learning: A Method for Creating Corpora." Doctoral thesis, SICS, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-22935.
Повний текст джерелаDo, Thi Ngoc Diep. "Extraction de corpus parallèle pour la traduction automatique depuis et vers une langue peu dotée." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00680046.
Повний текст джерелаChan, Nok Chin Lydia. "Grammar "bores the crap out of me!": A mixed-method study on the XTYOFZ construction and its usage by ESL and ENL speakers." Thesis, Stockholms universitet, Engelska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-194086.
Повний текст джерелаКниги з теми "Corpus method"
Tony, McEnery. Corpus linguistics: Method, theory and practice. Cambridge: Cambridge University Press, 2011.
Знайти повний текст джерела1928-, Kennedy George Alexander, and Rabe Hugo, eds. Invention and method: Two rhetorical treatises from the Hermogenic corpus. Atlanta: Society of Biblical Literature, 2005.
Знайти повний текст джерелаHermogenes. Invention and method: Two rhetorical treatises from the Hermogenic corpus. Atlanta, GA: Society of Biblical Literature, 2006.
Знайти повний текст джерелаHermogenes. Invention and method in Greek rhetorical theory: Two treatises from the Hermogenic corpus. Leiden: Brill, 2005.
Знайти повний текст джерелаBubenhofer, Noah. Patterns of Language Usage. Corpus Linguistics as a Method of Analyzing Discourse and Culture. Berlin, New York: Walter de Gruyter, 2009. http://dx.doi.org/10.1515/9783110215854.
Повний текст джерелаGlynn, Dylan, and Justyna A. Robinson, eds. Corpus Methods for Semantics. Amsterdam: John Benjamins Publishing Company, 2014. http://dx.doi.org/10.1075/hcp.43.
Повний текст джерелаStatistics for corpus linguistics. Edinburgh: Edinburgh University Press, 1998.
Знайти повний текст джерелаLu, Xiaofei. Computational Methods for Corpus Annotation and Analysis. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-8645-4.
Повний текст джерелаOakes, Michael P., and Meng Ji, eds. Quantitative Methods in Corpus-Based Translation Studies. Amsterdam: John Benjamins Publishing Company, 2012. http://dx.doi.org/10.1075/scl.51.
Повний текст джерелаYoung, Steve. Corpus-Based Methods in Language and Speech Processing. Dordrecht: Springer Netherlands, 1997.
Знайти повний текст джерелаЧастини книг з теми "Corpus method"
Brezina, Vaclav, and Dana Gablasova. "The Corpus Method." In English Language, 595–609. London: Macmillan Education UK, 2018. http://dx.doi.org/10.1057/978-1-137-57185-4_40.
Повний текст джерелаMarchi, Anna. "Method, Corpus, Process." In Self-Reflexive Journalism, 37–76. New York : Routledge , 2019. | Series: Routledge advances in corpus linguistics ; 23: Routledge, 2019. http://dx.doi.org/10.4324/9781315178691-3.
Повний текст джерелаPlecháč, Petr. "A Collocation-Driven Method of Discovering Rhymes (in Czech, English, and French Poetry)." In Taming the Corpus, 79–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98017-1_5.
Повний текст джерелаVillaseñor-Pineda, Luis, Manuel Montes-y-Gómez, Manuel Alberto Pérez-Coutiño, and Dominique Vaufreydaz. "A Corpus Balancing Method for Language Model Construction." In Computational Linguistics and Intelligent Text Processing, 393–401. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36456-0_40.
Повний текст джерелаKaraoglan, Bahar, Tarık Kışla, and Senem Kumova Metin. "Description of Turkish Paraphrase Corpus Structure and Generation Method." In Computational Linguistics and Intelligent Text Processing, 208–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75477-2_13.
Повний текст джерелаAussenac-Gilles, Nathalie, Brigitte Biébow, and Sylvie Szulman. "Revisiting Ontology Design: A Method Based on Corpus Analysis." In Knowledge Engineering and Knowledge Management Methods, Models, and Tools, 172–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39967-4_13.
Повний текст джерелаFu, Jianhui, Shi Wang, Ya Wang, and Cungen Cao. "A Practical Method of Identifying Chinese Metaphor Phrases from Corpus." In Knowledge Science, Engineering and Management, 43–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47650-6_4.
Повний текст джерелаHerrera, William G., Giovana S. Cover, and Leticia Rittner. "Pixel-Based Classification Method for Corpus Callosum Segmentation on Diffusion-MRI." In VipIMAGE 2017, 217–24. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68195-5_24.
Повний текст джерелаDeepa Modi and Neeta Nain. "Part-of-Speech Tagging of Hindi Corpus Using Rule-Based Method." In Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing, 241–47. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2638-3_28.
Повний текст джерелаKotani, Katsunori, and Takehiko Yoshimi. "Design for a Listening Learner Corpus for a Listenability Measurement Method." In Advances in Intelligent Systems and Computing, 15–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70016-8_2.
Повний текст джерелаТези доповідей конференцій з теми "Corpus method"
Pak, Alexander Alexandrovich, Sergazy Sakenovich Narynov, Arman Serikuly Zharmagambetov, Sholpan Nazarovna Sagyndykova, Zhanat Elubaevna Kenzhebayeva, and Irbulat Turemuratovich. "The method of synonyms extraction from unannotated corpus." In 2015 Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC). IEEE, 2015. http://dx.doi.org/10.1109/dinwc.2015.7054207.
Повний текст джерелаul Haque, Rakib, Parisa Mehera, M. F. Mridha, and Md Abdul Hamid. "Bengali Stop Phrase Detection Mechanism using Corpus Based Method." In 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2019. http://dx.doi.org/10.1109/iciev.2019.8858583.
Повний текст джерелаWu, Yaguang, Haichun Sun, and Chungang Yan. "An event timeline extraction method based on news corpus." In 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA). IEEE, 2017. http://dx.doi.org/10.1109/icbda.2017.8078725.
Повний текст джерелаBodenreider, Olivier, Thomas C. Rindflesch, and Anita Burgun. "Unsupervised, corpus-based method for extending a biomedical terminology." In the ACL-02 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2002. http://dx.doi.org/10.3115/1118149.1118157.
Повний текст джерелаTu, Shiying, Haojin Hu, Ronglyu Sun, Yanmei Jing, and Wenxue He. "Research on the Construction Method of Chinese - Vietnamese Parallel Corpus." In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2019. http://dx.doi.org/10.1109/iaeac47372.2019.8998000.
Повний текст джерелаTerashima, Ryo, Hiroshi Echizen-ya, and Kenji Araki. "Learning Method for Extraction of Partial Correspondence from Parallel Corpus." In 2009 International Conference on Asian Language Processing (IALP). IEEE, 2009. http://dx.doi.org/10.1109/ialp.2009.69.
Повний текст джерелаXia, Jing-Jing, and Meng-Qiang Liu. "A Corpus Building Method on Forensic Identification of Dialects (FIDS)." In 2011 International Conference on Information Technology, Computer Engineering and Management Sciences (ICM). IEEE, 2011. http://dx.doi.org/10.1109/icm.2011.357.
Повний текст джерелаQingzhi, Sun, Du Qingfeng, Zhang Chenxi, and Li Jun. "Chinese News Event Corpus Construction Method Based on Syntax Tree." In ICBDT 2020: 2020 3rd International Conference on Big Data Technologies. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3422713.3422741.
Повний текст джерелаLi, Dan, Qiqi Zhang, Jiadong Cao, and Yihua Mao. "Study of Corpus Classification Management Method in Construction Correspondence Files." In Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/edmi-19.2019.50.
Повний текст джерелаBourigault, Didier. "An endogeneous corpus-based method for structural noun phrase disambiguation." In the sixth conference. Morristown, NJ, USA: Association for Computational Linguistics, 1993. http://dx.doi.org/10.3115/976744.976755.
Повний текст джерелаЗвіти організацій з теми "Corpus method"
Sánchez Sabaté, R., C. del Valle, and M. Mensa. Method for the construction of large thematic corpora of online news articles. Towards a corpus of food-related news. Revista Latina de Comunicación Social, February 2019. http://dx.doi.org/10.4185/rlcs-2019-1347en.
Повний текст джерелаMulliner, Sara. A Mixed Methods Analysis of Corpus Data from Reddit Discussions of "Gay Voice". Portland State University Library, December 2019. http://dx.doi.org/10.15760/etd.7316.
Повний текст джерелаPhilpot, J. N. Lecture Utilized as the Primary Method of Instruction in the Marine Corps. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada509952.
Повний текст джерелаLouie, David, Yifeng Wang, Rekha R. Rao, Alec Kucala, Kyle Ross, Jessica Nicole Kruichak, and William Robert Chavez. A New Method to Contain Molten Corium in Catastrophic Nuclear Reactor Accidents. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1573134.
Повний текст джерелаWhitlow, James L. A Method for Collectively Measuring the Operating Tempo of Individuals in Marine Corps Units -- Why and How. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada241099.
Повний текст джерелаMcInerney, Michael, Matthew Brenner, Sean Morefield, Robert Weber, and John Carlyle. Acoustic nondestructive testing and measurement of tension for steel reinforcing members. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42181.
Повний текст джерелаGomrick, Kathleen M. Gung Ho, Raider! The Philosophy and Methods of Brigadier General Evans F. Carlson, Marine Corps Raider. Fort Belvoir, VA: Defense Technical Information Center, April 1999. http://dx.doi.org/10.21236/ada388945.
Повний текст джерелаMalej, Matt, and Fengyan Shi. Suppressing the pressure-source instability in modeling deep-draft vessels with low under-keel clearance in FUNWAVE-TVD. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40639.
Повний текст джерелаDavid, Gabrielle, D. Somerville, Julia McCarthy, Spencer MacNeil, Faith Fitzpatrick, Ryan Evans, and David Wilson. Technical guide for the development, evaluation, and modification of stream assessment methods for the Corps Regulatory Program. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42182.
Повний текст джерелаHaring, Christopher. Data collection tools for river geomorphology studies : LiDAR and traditional methods. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42502.
Повний текст джерела