Bibliografías temáticas / Bigrama

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Literatura académica sobre el tema "Bigrama"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 1 de febrero de 2022

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Artículos de revistas sobre el tema "Bigrama"

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Villas Bôas Júnior, Manoel, and Sergio Luiz Machado. "USO DE DEEP LEARNING NA DETECÇÃO DE FAKE NEWS." Projectus 3, no. 3 (2020): 109–20. http://dx.doi.org/10.15202/25254146.2018v3n3p109.

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Este documento apresenta uma aplicação para a detecção de notícias falsas através douso da técnica de aprendizagem profunda. Foram utilizadas duas bases-de-dados: umacompilação de notícias falsas perpetradas na Internet, e outra de notícias genuínas, publicadas pelo jornal brasileiro Folha de São Paulo. O modelo proposto tem como objetivoaplicar diferentes métodos de extração de dados para a tarefa de classificação de textossupervisionados. Para que os dados relevantes ao experimento fossem adequadamenteprocessados pelo algoritmo de aprendizagem de máquina, foram usados os modelosde extração s
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Zivkovic, Miodrag, Sasa Malkov, Snezana Zaric, et al. "Statistical dependence of protein secondary structure on amino acid bigrams." Chemical Industry and Chemical Engineering Quarterly 12, no. 1 (2006): 82–85. http://dx.doi.org/10.2298/ciceq0601082z.

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The statistical dependence of protein secondary structure on amino acid bigram frequencies was studied. Proteins in the PDBSELECT subset of the Protein Data Bank database were investigated. Protein secondary structures were determined using DSSP software. The conditional probabilities of protein secondary structures were calculated and presented. The results on bigrams show the frequencies of all the possible bigrams in all secondary structure types. These results elucidate some factors important for the prediction of the secondary structures of proteins based on the amino acid sequence.
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Pérez Lancho, Cruz, Antonio Sánchez Cabaco, Sonia González Díaz, Luz María Fernández Mateos, and Sarai Mata Gil. "EL PARADIGMA STROOP EMOCIONAL EN EL ESTUDIO DEL PROCESAMIENTO COGNITIVO DE LA CONTRAPUBLICIDAD DEL TABACO." International Journal of Developmental and Educational Psychology. Revista INFAD de Psicología. 1, no. 1 (2016): 581. http://dx.doi.org/10.17060/ijodaep.2014.n1.v1.406.

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Abstract:THE EMOTIONAL STROOP PARADIGM IN THE STUDY OF TOBACCO COUNTERPUBLICITY COGNITIVE PROCESSINGThe aim of this work is to analyze the attentional and mnesic processing of words related to smoking and health using the emotional stroop paradigm and a bigram completing task. A modified stroop task consisting of three types of words: neutral, smoking related words and health related words extracted from health warnings messages presented in smoking publicity, was given to a sample integrated by smokers, non-smokers and former-smokers. The individuals performed then an implicit memory task. Th
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Crossley, Scott, and Max M. Louwerse. "Multi-dimensional register classification using bigrams." International Journal of Corpus Linguistics 12, no. 4 (2007): 453–78. http://dx.doi.org/10.1075/ijcl.12.4.02cro.

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A corpus linguistic analysis investigated register classification using frequency of bigrams in nine spoken and two written corpora. Four dimensions emerged from a factor analysis using bigram frequencies shared across corpora: (1) Scripted vs. Unscripted Discourse, (2) Deliberate vs. Unplanned Discourse, (3) Spatial vs. Non-Spatial Discourse, and (4) Directional vs. Non-Directional Discourse. These findings were replicated in a second analysis. Both analyses demonstrate the strength of bigrams for classifying spoken and written registers, especially in locating distinct collocations among spo
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Sardinha, Tony Berber. "A historical characterisation of American and Brazilian cultures based on lexical representations." Corpora 15, no. 2 (2020): 183–212. http://dx.doi.org/10.3366/cor.2020.0194.

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The goal of this study is to detect the historical distribution of representations of the United States and Brazil formed around the use of the nationality adjectives American and Brazilian. To achieve this goal, the study used a pre-existing multi-dimensional analysis of representations based on bigrams from the half-a-trillion-word Google Books bigram dataset of English writing ( Berber Sardinha, 2019 ), which provided the major representations of both cultures. The method was based on the text type approach developed by Biber (1989) , which uses cluster analysis to identify the groupings of
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Garner, James, Scott Crossley, and Kristopher Kyle. "Beginning and intermediate L2 writer’s use of N-grams: an association measures study." International Review of Applied Linguistics in Language Teaching 58, no. 1 (2020): 51–74. http://dx.doi.org/10.1515/iral-2017-0089.

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AbstractAcommon approach to analyzing phraseological knowledge in first language (L1) and second language (L2) learners is to employ raw frequency data. Several studies have also analyzed n-gram use on the basis of statistical association scores. Results from n-gram studies have found significant differences between L1 and L2 writers and between intermediate and advanced L2 writers in terms of their bigram use. The current study expands on this research by investigating the connection between bigram and trigram association measures and human judgments of L2 writing quality. Using multiple stat
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Alsharman, Nesreen Mohammad, and Inna V. Pivkina. "Generating Summaries Through Unigram and Bigram." International Journal of Information Technology and Web Engineering 15, no. 1 (2020): 64–74. http://dx.doi.org/10.4018/ijitwe.2020010105.

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This article describes a new method for generating extractive summaries directly via unigram and bigram extraction techniques. The methodology uses the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences. Extracted unigrams and bigrams along with other features are used to build a final summary. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations: noun, verb, and adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesse
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Denk, Michaela, Peter Hackl, and Norbert Rainer. "String Matching Techniques: An Empirical Assessment Based on Statistics Austria's Business Register." Austrian Journal of Statistics 34, no. 3 (2016): 235–49. http://dx.doi.org/10.17713/ajs.v34i3.415.

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The maintenance and updating of Statistics Austria's business register requires a regularly matching of the register against other data sources; one of them is the register of tax units of the Austrian Federal Ministry of Finance. The matching process is based on string comparison via bigrams of enterprise names and addresses, and a quality class approach assigning pairs of register units into classes of different compliance (i.e., matching quality) based on bigram similarity values and the comparison of other matching variables, like the NACE code or the year of foundation.Based on methodolog
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van Kesteren, Ron, Ton Dijkstra, and Koenraad de Smedt. "Markedness effects in Norwegian–English bilinguals: Task-dependent use of language-specific letters and bigrams." Quarterly Journal of Experimental Psychology 65, no. 11 (2012): 2129–54. http://dx.doi.org/10.1080/17470218.2012.679946.

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This study investigates how bilinguals use sublexical language membership information to speed up their word recognition process in different task situations. Norwegian–English bilinguals performed a Norwegian–English language decision task, a mixed English lexical decision task, or a mixed Norwegian lexical decision task. The mixed lexical decision experiments included words from the nontarget language that required a “no” response. The language specificity of the Bokmål (a Norwegian written norm) and English (non)words was varied by including language-specific letters (“smør”, “hawk”) or big
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Hadwan, Mohammed, Mohammed A. Al-Hagery, Maher Al-Sanabani, and Salah Al-Hagree. "Soft Bigram distance for names matching." PeerJ Computer Science 7 (April 21, 2021): e465. http://dx.doi.org/10.7717/peerj-cs.465.

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Background Bi-gram distance (BI-DIST) is a recent approach to measure the distance between two strings that have an important role in a wide range of applications in various areas. The importance of BI-DIST is due to its representational and computational efficiency, which has led to extensive research to further enhance its efficiency. However, developing an algorithm that can measure the distance of strings accurately and efficiently has posed a major challenge to many developers. Consequently, this research aims to design an algorithm that can match the names accurately. BI-DIST distance is
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Tesis sobre el tema "Bigrama"

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Grigaitytė, Justina. "BNS informacinių žinučių analizė teminiu aspektu." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100617_111801-05840.

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Darbe nagrinėjamas temų identifikavimo uždavinys, kuris siejamas su teksto klasifikavimu į tam tikras kategorijas, t.y. įvairių tekstinių duomenų grupavimas pagal atitinkamas temas. Žinutės naujienų agentūrose yra skirstomos į atskiras grupes ir pogrupius pagal temas. Šis darbas atliekamas rankomis, t.y. perskaitomas tekstas ir priskiriamas kokiai nors temai. Vis dėlto, vystantis žiniasklaidai ir kuriantis įvairiems naujienų portalams, aktualu naujienas skirstyti ne rankiniu, o automatiniu būdu, todėl galimybė automatizuoti šį procesą galėtų būti naudinga įvairiems naujienų portalams, padedant
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Palinski, Amy M. "Investigating the Role of Open Bigrams in Visual Word Perception." Cleveland State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=csu1464265892.

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Teline, Maria Fernanda. ""Avaliação de métodos para a extração automática de terminologia de textos em português"." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25052004-012204/.

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Nas últimas décadas, o grande avanço da ciência e tecnologia com suas invenções, novos materiais, equipamentos e métodos gerou a necessidade da criação de novos nomes, chamados aqui de termos, e alterações nos seus significados, para nomear adequadamente esses avanços, principalmente em áreas dinâmicas como a Ciência da Computação, a Genética e a Medicina. Dado que o desenvolvimento de repertórios terminológicos é um trabalho difícil quando realizado manualmente, lingüistas computacionais, lingüistas aplicados, tradutores, intérpretes, jornalistas científicos têm se interessado pela extração a
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Teng, Yan. "Objective speech intelligibility assessment using speech recognition and bigram statistics with application to low bit-rate codec evaluation." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1456283581&sid=5&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Comez, Murat Ali. "Large Vocabulary Continuous Speech Recogniton For Turkish Using Htk." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1205491/index.pdf.

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This study aims to build a new language model that can be used in a Turkish large vocabulary continuous speech recognition system. Turkish is a very productive language in terms of word forms because of its agglutinative nature. For such languages like Turkish, the vocabulary size is far from being acceptable. From only one simple stem, thousands of new word forms can be generated using inflectional or derivational suffixes. In this thesis, words are parsed into their stems and endings. One ending includes the suffixes attached to the associated root. Then the search network based on bigrams i
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Al-Muhtaseb, Husni Abdulghani. "Arabic text recognition of printed manuscripts : efficient recognition of off-line printed Arabic text using Hidden Markov Models, Bigram Statistical Language Model, and post-processing." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4426.

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Arabic text recognition was not researched as thoroughly as other natural languages. The need for automatic Arabic text recognition is clear. In addition to the traditional applications like postal address reading, check verification in banks, and office automation, there is a large interest in searching scanned documents that are available on the internet and for searching handwritten manuscripts. Other possible applications are building digital libraries, recognizing text on digitized maps, recognizing vehicle license plates, using it as first phase in text readers for visually impaired peop
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Al-Muhtaseb, Husni A. "Arabic text recognition of printed manuscripts. Efficient recognition of off-line printed Arabic text using Hidden Markov Models, Bigram Statistical Language Model, and post-processing." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4426.

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Arabic text recognition was not researched as thoroughly as other natural languages. The need for automatic Arabic text recognition is clear. In addition to the traditional applications like postal address reading, check verification in banks, and office automation, there is a large interest in searching scanned documents that are available on the internet and for searching handwritten manuscripts. Other possible applications are building digital libraries, recognizing text on digitized maps, recognizing vehicle license plates, using it as first phase in text readers for visually impaired peop
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Sahin, Serkan. "Language Modeling For Turkish Continuous Speech Recognition." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/2/1223254/index.pdf.

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This study aims to build a new language model for Turkish continuous speech recognition. Turkish is very productive language in terms of word forms because of its agglutinative nature. For such languages like Turkish, the vocabulary size is far from being acceptable from only one simple stem, thousands of new words can be generated using inflectional and derivational suffixes. In this work, word are parsed into their stem and endings. First of all, we consider endings as words and we obtained bigram probabilities using stem and endings. Then, bigram probabilities are obtained using only the st
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Jančík, Zdeněk. "Modelování dynamiky prosodie pro rozpoznávání řečníka." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235929.

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Most current automatic speaker recognition system extract speaker-depend features by looking at short-term spectral information. This approach ignores long-term information. I explored approach that use the fundamental frequency and energy trajectories for each speaker. This approach models prosody dynamics on single fonemes or syllables. It is known from literature that prosodic systems do not work as well the acoustic one but it improve the system when fusing. I verified this assumption by fusing my results with state of the art acoustic system from BUT. Data from standard evaluation campaig
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Braga, Ígor Assis. "Aprendizado semissupervisionado multidescrição em classificação de textos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-02062010-160019/.

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Algoritmos de aprendizado semissupervisionado aprendem a partir de uma combinação de dados rotulados e não rotulados. Assim, eles podem ser aplicados em domínios em que poucos exemplos rotulados e uma vasta quantidade de exemplos não rotulados estão disponíveis. Além disso, os algoritmos semissupervisionados podem atingir um desempenho superior aos algoritmos supervisionados treinados nos mesmos poucos exemplos rotulados. Uma poderosa abordagem ao aprendizado semissupervisionado, denominada aprendizado multidescrição, pode ser usada sempre que os exemplos de treinamento são descritos por dois
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Libros sobre el tema "Bigrama"

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Mayer, Claude. Bigras: Louis Bigras et Rose de Lima Vanier. La Compagnie Jérémie, 1993.

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Peto, Linda Charlene Bauman. A comparison of two smoothing methods for word bigram models. National Library of Canada, 1993.

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Peto, Linda Charlene Bauman. A comparison of two smoothing methods for word bigram models. University of Toronto, Dept. of Computer Science, 1994.

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Saavedra, Sergio Pérez. Samy y Sandra-- la historia: Bigrafía autorizada. Sergio Pérez Saavedra], 2003.

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Clower, Michael. Mick Kinane: Bigrace king : the authorised biography. Mainstream, 1996.

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name, No. La rage de l'ange: Scénario et réalisation, Dan Bigras. Alliance Atlantis Vivafilm, 2006.

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Messi: El chico que siempre llegaba tarde [y hoy es el primero]. Random House Mondadori, 2011.

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Messi: El chico que siempre llegaba tarde [y hoy es el primero]. Vintage Español, 2012.

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Bigras, Carl. Infrared imaging of Coleridge annotations for Victoria University Library, Toronto, Ontario / Carl Bigras. Canadian Conservation Institute, 2004.

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Matveev, A. I︠A︡. (Anatoliĭ I︠A︡kovlevich), ed. Bagrash-Bigra: SPK "Pervyĭ maĭ" i istorii︠a︡ dereven', vkhodi︠a︡shchikh v nego. Udmurtii︠a︡, 2005.

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Capítulos de libros sobre el tema "Bigrama"

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Wehrli, Eric, and Luka Nerima. "When Rules Meet Bigrams." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54906-9_21.

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Ashuha, Arseniy, and Natalia Loukachevitch. "Bigram Anchor Words Topic Model." In Communications in Computer and Information Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52920-2_12.

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Valavala, Mounicasri, and Hemalatha Indukuri. "Comparative Analysis of Sentiment Analysis Between All Bigrams and Selective Adverb/Adjective Bigrams." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3828-5_69.

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Dzieciątko, Mariusz, Dominik Spinczyk, and Piotr Borowik. "Correcting Polish Bigrams and Diacritical Marks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23762-2_30.

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Yang, Qi, Ruixuan Li, Yuhua Li, and Qilei Liu. "Topic-Bigram Enhanced Word Embedding Model." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_7.

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Kinoshita, Jorge. "A Transfer Dictionary for Words and Bigrams." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/10692710_8.

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Jurić, Damir, Marko Banek, and Šandor Dembitz. "Informativeness of Inflective Noun Bigrams in Croatian." In Agent and Multi-Agent Systems. Technologies and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30947-2_15.

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Jiang, Maojin, Eric Jensen, Steve Beitzel, and Shlomo Argamon. "Choosing the Right Bigrams for Information Retrieval." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_50.

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Yin, Ping, Ming Zhang, ZhiHong Deng, and DongQing Yang. "Metadata Extraction from Bibliographies Using Bigram HMM." In Digital Libraries: International Collaboration and Cross-Fertilization. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30544-6_33.

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Arun, S., V. V. Vishnu, and N. Arun Kumar. "Parse Tree Generation Using HMM Bigram Model." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3174-8_48.

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Actas de conferencias sobre el tema "Bigrama"

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Murks, Nina, Anže Omerzu, and Borko Bošković. "Analiza sentimenta komentarjev hotelov z uporabo slovarjev in metode Naivni Bayes." In 7th Student Computer Science Research Conference. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-516-0.15.

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V £lanku smo predstavili pristop k analizi sen-timenta komentarjev hotelskih gostov s pomo£jo slovarjev in metode Naivni Bayes. Najprej smo zgradili slovarja sentimenta, ki sta vsebovala n-grame, ter njihove verjetnosti, da pripadajo pozi-tivnemu ali negativnemu razredu. Nato smo s po-mo£jo zgrajenih slovarjev klasificirali komentarje hotelov, pri £emer smo uporabili metodo Naivni Bayes. Pri klasifikaciji komentarjev s mo ra£u-nali klasifikacijske vrednosti o z. verjetnosti, da so posamezni komentarji pozitivni ali negativni. Komentarje smo klasificirali s p omo£jo unigra-mov in bigramov, ter
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Johansson, Christer. "Good bigrams." In the 16th conference. Association for Computational Linguistics, 1996. http://dx.doi.org/10.3115/993268.993272.

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Spilka, M., G. Rozinaj, and R. Rybarova. "Context analysis using bigrams." In 2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES). IEEE, 2015. http://dx.doi.org/10.1109/ines.2015.7329742.

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Pla, Ferran, Antonio Molina, and Natividad Prieto. "Tagging and chunking with bigrams." In the 18th conference. Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/992730.992735.

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van Cranenburgh, Andreas, and Corina Koolen. "Identifying Literary Texts with Bigrams." In Proceedings of the Fourth Workshop on Computational Linguistics for Literature. Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/w15-0707.

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Nascimento, Gabriel, Fellipe Duarte, and Gustavo Paiva Guedes. "Emoções em português do Brasil: um conjunto de dados e resultados de base." In VII Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/brasnam.2018.3595.

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Este artigo apresenta um novo conjunto de dados para análise de sentimentos em português do Brasil. Os textos foram extráıdos de uma rede social brasileira denominada Meu Querido Diário. Nessa rede social, os usuários frequentemente compartilham sentimentos e emoções associados ao dia-a-dia. O principal diferencial deste conjunto de dados é que, nessa rede social, o próprio usuário pode informar a emoção associadaá sua entrada. Foram realizados experimentos preliminares com alguns modelos de classificação, criando os primeiros resultados de base. O modelo que obteve melhor resultado foi o SVM
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Kimura, Takeshi, Takuya Ugajin, and Yoshiyuki Kotani. "Bigram Realization Probability for Game Tree Search." In 2011 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2011. http://dx.doi.org/10.1109/taai.2011.53.

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Alnahas, Dima, and Baris Baykant Alagoz. "Probabilistic Relational Connectivity Analysis of Bigram Models." In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2019. http://dx.doi.org/10.1109/idap.2019.8875980.

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Liu, Dan, Weiguo Fang, Hong Zhou, and Yan Li. "Bigram Chinese Word Segmentation by Viterbi Algorithm." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.590.

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Liossis, Emmanuel. "Bigram constrained Linear Chain Conditional Random Fields." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850147.

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