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Artículos de revistas sobre el tema "Bigrama"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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1

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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2

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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3

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Christanti Mawardi, Viny, Niko Susanto, and Dali Santun Naga. "Spelling Correction for Text Documents in Bahasa Indonesia Using Finite State Automata and Levinshtein Distance Method." MATEC Web of Conferences 164 (2018): 01047. http://dx.doi.org/10.1051/matecconf/201816401047.

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Any mistake in writing of a document will cause the information to be told falsely. These days, most of the document is written with a computer. For that reason, spelling correction is needed to solve any writing mistakes. This design process discuss about the making of spelling correction for document text in Indonesian language with document's text as its input and a .txt file as its output. For the realization, 5 000 news articles have been used as training data. Methods used includes Finite State Automata (FSA), Levenshtein distance, and N-gram. The results of this designing process are sh

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12

Hendrikx, Roy Johannus Petrus, Hanneke Wil-Trees Drewes, Marieke Spreeuwenberg, Dirk Ruwaard, and Caroline Baan. "Measuring Regional Quality of Health Care Using Unsolicited Online Data: Text Analysis Study." JMIR Medical Informatics 7, no. 4 (2019): e13053. http://dx.doi.org/10.2196/13053.

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Background Regional population management (PM) health initiatives require insight into experienced quality of care at the regional level. Unsolicited online provider ratings have shown potential for this use. This study explored the addition of comments accompanying unsolicited online ratings to regional analyses. Objective The goal was to create additional insight for each PM initiative as well as overall comparisons between these initiatives by attempting to determine the reasoning and rationale behind a rating. Methods The Dutch Zorgkaart database provided the unsolicited ratings from 2008

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13

Tremblay, Antoine, and Benjamin V. Tucker. "The effects of N-gram probabilistic measures on the recognition and production of four-word sequences." Mental Lexicon 6, no. 2 (2011): 302–24. http://dx.doi.org/10.1075/ml.6.2.04tre.

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The present study investigates the processing and production of four-word sequences such as I don’t really know, at the age of, and I think it’s the. Specifically, we investigate the influence of families of probabilistic measures such as unigram, bigram, trigram, and quadgram frequency of occurrence, logarithmic (log) probability of occurrence, and mutual information. Log probability of occurrence emerged as the predominant predictor family in the onset latency analysis, suggesting that recognition is mainly underpinned by competition between a target N-gram and its family members. In contras

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14

홍문표. "Bigramm-basierte Sentimentanalyse." Zeitschrift f？r Deutsche Sprache und Literatur ll, no. 65 (2014): 27–46. http://dx.doi.org/10.30947/zfdsl.2014..65.27.

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15

Martinez, Angel R. "Bigram proximity matrix." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 2 (2010): 251–55. http://dx.doi.org/10.1002/wics.67.

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16

Yang, Kang, Kamal Al-Sabahi, Yanmin Xiang, and Zuping Zhang. "An Integrated Graph Model for Document Summarization." Information 9, no. 9 (2018): 232. http://dx.doi.org/10.3390/info9090232.

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Extractive summarization aims to produce a concise version of a document by extracting information-rich sentences from the original texts. The graph-based model is an effective and efficient approach to rank sentences since it is simple and easy to use. However, its performance depends heavily on good text representation. In this paper, an integrated graph model (iGraph) for extractive text summarization is proposed. An enhanced embedding model is used to detect the inherent semantic properties at the word level, bigram level and trigram level. Words with part-of-speech (POS) tags, bigrams and

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17

Нокель, М. А., and Н. В. Лукашевич. "Topic models: adding bigrams and taking account of the similarity between unigrams and bigrams." Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie), no. 2 (June 30, 2015): 215–34. http://dx.doi.org/10.26089/nummet.v16r222.

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Представлены результаты экспериментов по добавлению биграмм в тематические модели и учету сходства между ними и униграммами. Предложен новый алгоритм PLSA-SIM, являющийся модификацией алгоритма построения тематических моделей PLSA (Probabilistic Latent Semantic Analysis). Предложенный алгоритм позволяет добавлять биграммы и учитывать сходство между ними и униграммными компонентами. Исследована возможность применения ассоциативных мер для выбора и последующего включения биграмм в тематические модели. В качестве текстовых коллекций взяты русскоязычная подборка статей из электронных банковских жу

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18

Li, Yuanxiang, Xiaoqing Ding, and Chew Lim Tan. "Combining character-based bigrams with word-based bigrams in contextual postprocessing for Chinese script recognition." ACM Transactions on Asian Language Information Processing 1, no. 4 (2002): 297–309. http://dx.doi.org/10.1145/795458.795461.

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19

Schmalz, Xenia, and Claudio Mulatti. "Busting a myth with the Bayes Factor." Mental Lexicon 12, no. 2 (2017): 263–82. http://dx.doi.org/10.1075/ml.17009.sch.

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Abstract Psycholinguistic researchers identify linguistic variables and assess if they affect cognitive processes. One such variable is letter bigram frequency, or the frequency with which a given letter pair co-occurs in an orthography. While early studies reported that bigram frequency affects visual lexical decision, subsequent, well-controlled studies not shown this effect. Still, researchers continue to use it as a control variable in psycholinguistic experiments. We propose two reasons for the persistence of this variable: (1) Reporting no significant effect of bigram frequency cannot pr

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20

MAKIOKA, Shogo, and Katsuo TAMAOKA. "Open Bigrams in Kanji-Kana Words." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (2013): 3EV—064–3EV—064. http://dx.doi.org/10.4992/pacjpa.77.0_3ev-064.

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21

Muncer, Steven J., and David C. Knight. "The bigram trough hypothesis and the syllable number effect in lexical decision." Quarterly Journal of Experimental Psychology 65, no. 11 (2012): 2221–30. http://dx.doi.org/10.1080/17470218.2012.697176.

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There has been an increasing volume of evidence supporting the role of the syllable in various word processing tasks. It has, however, been suggested that syllable effects may be caused by orthographic redundancy. In particular, it has been proposed that the presence of bigram troughs at syllable boundaries cause what are seen as syllable effects. We investigated the bigram trough hypothesis as an explanation of the number of syllables effect for lexical decision in five-letter words and nonwords from the British Lexicon Project. The number of syllables made a significant contribution to predi

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22

Perruchet, Pierre, Jorge Gallego, and Chantal Pacteau. "A Reinterpretation of Some Earlier Evidence for Abstractiveness of Implicitly Acquired Knowledge." Quarterly Journal of Experimental Psychology Section A 44, no. 2 (1992): 193–210. http://dx.doi.org/10.1080/02724989243000000.

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Reber and Lewis (1977) exposed subjects to a subset of letter strings generated from a synthetic grammar, then asked them to reorder scrambled letters to generate new grammatical strings. The distribution of the frequency of the bigrams composing their solutions correlated better with the frequency of the bigrams composing the full set of strings generated by the grammar than with the frequency of the bigrams composing the subset of strings displayed in the study phase. In his recent overview on implicit learning, Reber (1989) develops this experimental result into one of the main supports for

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23

Cicres, Jordi, and Sheila Queralt. "An n-gram based approach to the automatic classification of schoolchildren’s writing." Vigo International Journal of Applied Linguistics, no. 16 (May 3, 2019): 53–80. http://dx.doi.org/10.35869/vial.v0i16.93.

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This article focuses on the analysis of schoolchildren’s writing (throughout the whole primary school period) using sets of morphological labels (n-grams). We analyzed the sets of bigrams and trigrams from a group of literary texts written by Catalan schoolchildren in order to identify which bigrams and trigrams can help discriminate between texts from the three cycles into which the Spanish primary education system is divided: lower cycle (6- and 7-year-olds), middle cycle (8- and 9-year- olds) and upper cycle (10- and 11-year-olds). The results obtained are close to 70% of correct classifica

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24

Whitney, Carol, Daisy Bertrand, and Jonathan Grainger. "On Coding the Position of Letters in Words." Experimental Psychology 59, no. 2 (2012): 109–14. http://dx.doi.org/10.1027/1618-3169/a000132.

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Open-bigram and spatial-coding schemes provide different accounts of how letter position is encoded by the brain during visual word recognition. Open-bigram coding involves an explicit representation of order based on letter pairs, while spatial coding involves a comparison function operating over representations of individual letters. We identify a set of priming conditions (subset primes and reversed interior primes) for which the two types of coding schemes give opposing predictions, hence providing the opportunity for strong scientific inference. Experimental results are consistent with th

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25

Saputra, Christian Eka, Derwin Suhartono, and Rini Wongso. "Question Categorization using Lexical Feature in Opini.id." ComTech: Computer, Mathematics and Engineering Applications 8, no. 4 (2017): 229. http://dx.doi.org/10.21512/comtech.v8i4.4026.

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This research aimed to categorize questions posted in Opini.id. N-gram and Bag of Concept (BOC) were used as the lexical features. Those were combined with Naïve Bayes, Support Vector Machine (SVM), and J48 Tree as the classification method. The experiments were done by using data from online media portal to categorize questions posted by user. Based on the experiments, the best accuracy is 96,5%. It is obtained by using the combination of Bigram Trigram Keyword (BTK) features with J48 Tree as classifier. Meanwhile, the combination of Unigram Bigram (UB) and Unigram Bigram Keyword (UBK) with a

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Wahl, Alexander. "Intonation unit boundaries and the storage of bigrams." Review of Cognitive Linguistics 13, no. 1 (2015): 191–219. http://dx.doi.org/10.1075/rcl.13.1.08wah.

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Much recent work on language and cognition has examined the psychological status of collocations/formulas/multi-word expressions as mentally stored units. These studies have used a variety of statistical metrics to quantify the degree of strength or association of these sequences, and then they have correlated these strengths with particular behavioral effects that evidence mental storage. However, the relationship between intonational prosody and storage of collocations has received little attention. Through a corpus-based approach, this study examines the hypothesis that boundaries between s

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Mitin, Nikolai Alekseevich, and Yuriy N. Orlov. "Statistical analysis of bigrams of specialized texts." Computer Research and Modeling 12, no. 1 (2020): 243–54. http://dx.doi.org/10.20537/2076-7633-2020-12-1-243-254.

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Kotov, Yuri Alexeevich. "Determinate Identification of Russian Text Letter Bigrams." SPIIRAS Proceedings 1, no. 44 (2016): 181. http://dx.doi.org/10.15622/sp.44.11.

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Lee, Chan-Do, Chade-Meng Tan, and Yuan-Fang Wang. "Improving Text Categorization with High Quality Bigrams." KIPS Transactions:PartB 9B, no. 4 (2002): 415–20. http://dx.doi.org/10.3745/kipstb.2002.9b.4.415.

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Kam, Xuân-Nga Cao, Iglika Stoyneshka, Lidiya Tornyova, Janet D. Fodor, and William G. Sakas. "Bigrams and the Richness of the Stimulus." Cognitive Science 32, no. 4 (2008): 771–87. http://dx.doi.org/10.1080/03640210802067053.

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van den Hoogen, Johan, and Francine Govers. "GPCR-bigrams: Enigmatic signaling components in oomycetes." PLOS Pathogens 14, no. 7 (2018): e1007064. http://dx.doi.org/10.1371/journal.ppat.1007064.

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Zholobov, Oleg F., and Victor A. Baranov. "Verbs meaning ‘know’ in Old Church Slavonic and Old Russian writing: Distributive, quantitative and semantic properties." Vestnik of Saint Petersburg University. Language and Literature 18, no. 1 (2021): 56–76. http://dx.doi.org/10.21638/spbu09.2021.104.

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The article presents the first attempt of a distributive and quantitative analysis of the lexico-semantic series in the Old Russian language based on three multi-genre subcorpora of the historical corpus “Manuscript” (manuscripts.ru): lists of the Gospels, menaia, and chronicles. The authors made a correlation between the software processing of the diachronic corpus of data and their historical and linguistic status. The semantic relations between the verbs with the general meaning ‘know’ in the Old Russian language are considered: věděti, vdati, znati. Their substitution in the modern Russian

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Nimb, Sanni, Nicolai Hartvig Sørensen, and Henrik Lorentzen. "Updating the dictionary: Semantic change identification based on change in bigrams over time." Slovenščina 2.0: empirical, applied and interdisciplinary research 8, no. 2 (2020): 112–38. http://dx.doi.org/10.4312/slo2.0.2020.2.112-138.

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We investigate a method of updating a Danish monolingual dictionary with new semantic information on already included lemmas in a systematic way, based on the hypothesis that the variation in bigrams over time in a corpus might indicate changes in the meaning of one of the words. The method combines corpus statistics with manual annotations. The first step consists in measuring the collocational change in a homogeneous newswire corpus with texts from a 14 year time span, 2005 through 2018, by calculating all the statistically significant bigrams. These are then applied to a new version of the

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Peraldi, François. "Julien Bigras." Santé mentale au Québec 15, no. 2 (1990): 14. http://dx.doi.org/10.7202/031559ar.

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Fahrur Rozi, Imam, Annisa Taufika Firdausi, and Khalimatul Islamiyah. "ANALISIS SENTIMEN PADA TWITTER MENGENAI PASCA BENCANA MENGGUNAKAN METODE NAÏVE BAYES DENGAN FITUR N-GRAM." Jurnal Informatika Polinema 6, no. 2 (2020): 33–39. http://dx.doi.org/10.33795/jip.v6i2.316.

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Bencana alam sering terjadi di wilayah Indonesia, pemberian bantuan yang efisien dan sesuai kebutuhan sangat berpengaruh terhadap proses pemulihan pasca bencana alam. Melalui twitter masyarakat banyak memberikan respon mengenai bencana alam, akan tetapi respon tersebut belum terklasifikasikan. Oleh karenanya, diperlukan klasifikasi respon masyarakat mengenai bencana alam. Dalam penilitian ini, dilakukan analisis sentimen terhadap tweet dengan mengklasifikasikan kedalam kategori positif atau negatif menggunakan algoritma Naïve Bayes Classifier. Dari hasil klasifikasi tersebut kita dapat memprio

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Martin, Sven, Jörg Liermann, and Hermann Ney. "Algorithms for bigram and trigram word clustering." Speech Communication 24, no. 1 (1998): 19–37. http://dx.doi.org/10.1016/s0167-6393(97)00062-9.

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Ravichandran, M., G. Kulanthaivel, and T. Chellatamilan. "Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/617358.

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Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervi

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Wu, Yufang, Heng Zhang, Bo Xu, Hongwei Hao, and Chenglin Liu. "TR-LDA: A Cascaded Key-Bigram Extractor for Microblog Summarization." International Journal of Machine Learning and Computing 5, no. 3 (2015): 172–78. http://dx.doi.org/10.7763/ijmlc.2015.v5.503.

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Kinoshita, Sachiko, and Dennis Norris. "Letter order is not coded by open bigrams." Journal of Memory and Language 69, no. 2 (2013): 135–50. http://dx.doi.org/10.1016/j.jml.2013.03.003.

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Tan, Chade-Meng, Yuan-Fang Wang, and Chan-Do Lee. "The use of bigrams to enhance text categorization." Information Processing & Management 38, no. 4 (2002): 529–46. http://dx.doi.org/10.1016/s0306-4573(01)00045-0.

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Beauquier, Maxime, and Carsten Schürmann. "A Bigraph Relational Model." Electronic Proceedings in Theoretical Computer Science 71 (October 31, 2011): 14–28. http://dx.doi.org/10.4204/eptcs.71.2.

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Keller, Frank, and Mirella Lapata. "Using the Web to Obtain Frequencies for Unseen Bigrams." Computational Linguistics 29, no. 3 (2003): 459–84. http://dx.doi.org/10.1162/089120103322711604.

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This article shows that the Web can be employed to obtain frequencies for bigrams that are unseen in a given corpus. We describe a method for retrieving counts for adjective-noun, noun-noun, and verb-object bigrams from the Web by querying a search engine. We evaluate this method by demonstrating: (a) a high correlation between Web frequencies and corpus frequencies; (b) a reliable correlation between Web frequencies and plausibility judgments; (c) a reliable correlation between Web frequencies and frequencies recreated using class-based smoothing; (d) a good performance of Web frequencies in

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Sridhar, Rajeswari, V. Janani, Rasiga Gowrisankar, and G. Monica. "Language Relationship Model for Automatic Generation of Tamil Stories from Hints." International Journal of Intelligent Information Technologies 13, no. 2 (2017): 21–40. http://dx.doi.org/10.4018/ijiit.2017040102.

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In this paper, we propose to develop a Story Generator from hints using a machine learning approach. During the learning phase, the system is fed with stories which are POS tagged and are converted into a Language Relationship model that is represented as a conceptual graph. During the synthesis phase, the input hints which are delimited using hyphen and converted to a conceptual graph. This graph is matched with the conceptual graph of the corpus and probable words, its sequences along with the relationship are determined using three proposed methods namely Randomized selection, Weighted Sele

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44

D'hondt, Eva, Suzan Verberne, Cornelis Koster, and Lou Boves. "Text Representations for Patent Classification." Computational Linguistics 39, no. 3 (2013): 755–75. http://dx.doi.org/10.1162/coli_a_00149.

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With the increasing rate of patent application filings, automated patent classification is of rising economic importance. This article investigates how patent classification can be improved by using different representations of the patent documents. Using the Linguistic Classification System (LCS), we compare the impact of adding statistical phrases (in the form of bigrams) and linguistic phrases (in two different dependency formats) to the standard bag-of-words text representation on a subset of 532,264 English abstracts from the CLEF-IP 2010 corpus. In contrast to previous findings on classi

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45

SALVADOR, ISMAEL, and JOSÉ-MIGUEL BENEDÍ. "RNA MODELING BY COMBINING STOCHASTIC CONTEXT-FREE GRAMMARS AND n-GRAM MODELS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 03 (2002): 309–15. http://dx.doi.org/10.1142/s0218001402001691.

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The RNA sentences present structured regions caused by pairwise correlations, and nonstructured regions where any global relation can be found. In this paper, we present a combination of stochastic context-free grammars (SCFG) and bigram models. The SCFGs are used to represent the long-term relations of the structured part of RNA sequences, while the bigram models are used to capture the local relations of the nonstructured part. A stochastic version of Sakakibara's algorithm is used to study the SCFGs. Finally, experiments to evaluate the behavior of this proposal were carried out.

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46

Novick, Laura R., and Steven J. Sherman. "Type-based bigram frequencies for five-letter words." Behavior Research Methods, Instruments, & Computers 36, no. 3 (2004): 397–401. http://dx.doi.org/10.3758/bf03195587.

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Litvishko, Ol'ga. "Structural Peculiarities of Bigram-Collocations in Legal English." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije 18, no. 2 (2019): 37–47. http://dx.doi.org/10.15688/jvolsu2.2019.2.4.

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48

Bassiou, Nikoletta, and Constantine Kotropoulos. "Long distance bigram models applied to word clustering." Pattern Recognition 44, no. 1 (2011): 145–58. http://dx.doi.org/10.1016/j.patcog.2010.07.006.

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ABUZEINA, Dia. "Exploring bigram character features for Arabic text clustering." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 27, no. 4 (2019): 3165–79. http://dx.doi.org/10.3906/elk-1808-103.

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Setiadi Citawan, Rico, Viny Christanti Mawardi, and Bagus Mulyawan. "Automatic Essay Scoring in E-learning System Using LSA Method with N-Gram Feature for Bahasa Indonesia." MATEC Web of Conferences 164 (2018): 01037. http://dx.doi.org/10.1051/matecconf/201816401037.

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In the world of education, e-learning system is a system that can be used to support the educational process. E-learning system is usually used by educators to learners in evaluating learning outcomes. In the process of evaluating learning outcomes in the e-learning system, the form type of exam questions that are often used are multiple choice and short stuffing. For exam questions in the form of essays are rarely used in the evaluation process of educational because of the difference in the subjectivity and time consuming in the assessment process. In this design aims to create an automatic

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