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Journal articles on the topic 'Statistical linguistics'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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1

Koplenig, Alexander. "Against statistical significance testing in corpus linguistics." Corpus Linguistics and Linguistic Theory 15, no. 2 (October 25, 2019): 321–46. http://dx.doi.org/10.1515/cllt-2016-0036.

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Abstract In the first volume of Corpus Linguistics and Linguistic Theory, Gries (2005. Null-hypothesis significance testing of word frequencies: A follow-up on Kilgarriff. Corpus Linguistics and Linguistic Theory 1(2). doi:10.1515/cllt.2005.1.2.277. http://www.degruyter.com/view/j/cllt.2005.1.issue-2/cllt.2005.1.2.277/cllt.2005.1.2.277.xml: 285) asked whether corpus linguists should abandon null-hypothesis significance testing. In this paper, I want to revive this discussion by defending the argument that the assumptions that allow inferences about a given population – in this case about the studied languages – based on results observed in a sample – in this case a collection of naturally occurring language data – are not fulfilled. As a consequence, corpus linguists should indeed abandon null-hypothesis significance testing.
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Zhukovska, Victoriia V., Oleksandr O. Mosiiuk, and Veronika V. Komarenko. "ЗАСТОСУВАННЯ ПРОГРАМНОГО ПАКЕТУ R У НАУКОВИХ ДОСЛІДЖЕННЯХ МАЙБУТНІХ ФІЛОЛОГІВ." Information Technologies and Learning Tools 66, no. 4 (September 30, 2018): 272. http://dx.doi.org/10.33407/itlt.v66i4.2196.

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Corpus linguistics is a newly emerging field of study in applied linguistics that deals with construction, processing, and exploitation of text corpora. To date, a high-quality analysis of vast amounts of empirical language data provided by computerized corpora is impossible without computer technologies and relevant statistical methods. Therefore, teaching future philologists to effectively apply statistical computer programs is an important stage in their research training. The article discusses the possibilities of using one of the leading in Western linguistics, but not well-known in Ukraine, software packages for statistical data analysis – R statistical software environment – in the research by future philologists. The paper reveals the advantages and disadvantages of this program in comparison with other similar software packages (SPSS and Statistica) and provides Internet links to R self-learn tutorials. The flexibility and efficacy of R for linguistic research are demonstrated on the example of a statistical analysis of the use of hedges in the corpus of academic speech. For novice philologists to properly understand the peculiarities of conducting a statistical linguistic experiment with R, a detailed description of each stage of the study is provided. The statistical verification of hedges in the speech of students and lecturers was carried out using such statistical methods as the Kolmogorov–Smirnov test and the Mann-Whitney U Test. The article presents the developed algorithms to calculate the specified tests applying the built-in commands and various specialized library functions, created by R user community to enhance the functionality of this statistical software. Each script for statistical calculations in R is accompanied by a detailed description and interpretation of the results obtained. Further study of the issue will involve a number of activities aimed at raising awareness and improving skills of future philologists in using R statistical software, which is important for their professional development as researchers.
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Gries, Stefan Th, and Nick C. Ellis. "Statistical Measures for Usage-Based Linguistics." Language Learning 65, S1 (May 21, 2015): 228–55. http://dx.doi.org/10.1111/lang.12119.

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4

Zhukovska, Viktoriia V., and Oleksandr O. Mosiiuk. "STATISTICAL SOFTWARE R IN CORPUS-DRIVEN RESEARCH AND MACHINE LEARNING." Information Technologies and Learning Tools 86, no. 6 (December 30, 2021): 1–18. http://dx.doi.org/10.33407/itlt.v86i6.4627.

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The rapid development of computer software and network technologies has facilitated the intensive application of specialized statistical software not only in the traditional information technology spheres (i.e., statistics, engineering, artificial intelligence) but also in linguistics. The statistical software R is one of the most popular analytical tools for statistical processing a huge array of digitalized language data, especially in quantitative corpus linguistic studies of Western Europe and North America. This article discusses the functionality of the software package R, focusing on its advantages in performing complex statistical analyses of linguistic data in corpus-driven studies and creating linguistic classifiers in machine learning. With this in mind, a three-stage strategy of computer-statistical analysis of linguistic corpus data is elaborated: 1) data processing and preparing to be subjected to a statistical procedure, 2) utilizing statistical hypothesis testing methods (MANOVA, ANOVA) and the Tukey post-hoc test, and 3) developing a model of a linguistic classifier and analyzing its effectiveness. The strategy is implemented on 11 000 tokens of English detached nonfinite constructions with an explicit subject extracted from the BNC-BYU corpus. The statistical analysis indicates significant differences in the realization of the factors of the parameter “Part of speech of the subject”. The analyzed linguistic data are employed to build a machine model for the classification of the given constructions. Particular attention is devoted to the methodological perspectives of interdisciplinary research in the fields of linguistics and computer studies. The potential application of the elaborated case study in training undergraduate, master, and postgraduate students of Applied Linguistics is indicated. The article provides all the statistical data and codes written in the R script with comprehensive descriptions and explanations. The concluding part of the article summarizes the obtained results and highlights the issues for further research connected with the popularization of the statistical software complex R and raising the awareness of specialists in this statistical analysis system.
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Mortarino, Cinzia. "An improved statistical test for historical linguistics." Statistical Methods and Applications 18, no. 2 (January 3, 2008): 193–204. http://dx.doi.org/10.1007/s10260-007-0085-1.

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6

Shaikevich, Anatole. "Contrastive and Comparable Corpora: Quantitative Aspects." International Journal of Corpus Linguistics 6, no. 2 (December 31, 2001): 229–55. http://dx.doi.org/10.1075/ijcl.6.2.03sha.

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This paper draws attention to the complexity of problems arising in statistical linguistics when it must compare various corpora. Those problems are discussed from the point of view of distributional statistical analysis of texts; that is, a set of formal procedures with a minimum of preconceived linguistic knowledge. The terminological distinction between contrastive and comparable corpora is introduced.
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7

Kusz, Ewa. "Statistics for linguists revisited: the review of some basic statistical tools in linguistic research and data analysis." Studia Anglica Resoviensia 17 (2021): 31–46. http://dx.doi.org/10.15584/sar.2020.17.3.

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The major aim of this paper is to emphasise the importance of implementing statistical tools in the field of linguistic research, as well as to acquaint the reader with the basic statistical methods that can be used while conducting linguistic studies. The article introduces the idea of five steps in data analysis that any researcher of applied linguistics can take in order to carry out relevant studies. The steps include choosing statistical programmes, eliciting data, selecting some visual methods and applying normality tests, as well as choosing applicable parametric or nonparametric tests, all of which requires appropriate planning, designing, analysing and interpreting data. The theoretical part is an interlude to the practical realisation of the above-mentioned five steps, which is based on the part of linguistic research conducted on the students of English Philology. The major purpose of it was to prove (or refute) that there is a positive correlation between participants’ level of musical intelligence and their L2 pronunciation skills. The practical use of statistical methods enables the readers to familiarise themselves with one of the patterns of statistical analysis in the field of applied linguistics.
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8

Sidnyaev, Nikolai I., Juliia I. Butenko, and Vladislav V. Garazha. "STATISTICAL ASSESSMENT OF MEANINGLESS LETTER STRINGS ASSOCIATIVE POWER." Theoretical and Applied Linguistics, no. 4 (2019): 107–24. http://dx.doi.org/10.22250/2410-7190_2019_5_4_107_124.

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The article proposes a method of compiling statistics of the most common trigrams in texts of different lengths, comparing several small passages with general statistics, and on the basis of the obtained data, a minimum adequate sample is proposed. The method for verification of hypotheses is proposed to test the distribution laws by using different criteria. The statistical processing of the results of the quantitative analysis of trigrams is presented. Calculation of metrological parameters for estimation of unknown parameters of the trigram distribution is performed. In the quantitative analysis, not an infinitely large number of definitions but several independent definitions is made, that is, having a sample (total sample) of 5-6 options. The conditions for the choice of linguistic models, as well as the following types of linguistic-mathematical models are described: ideal and reproducing. The methodological functions of applied linguistics are reviewed. The special sections of mathematics used in linguistic theory and practice are reviewed. The possibility of extracting the sample from the log-normal general population is statistically tested as a complex non-parametric hypothesis. The test was carried out using Kolmogorov's criterion.
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Janda, Laura A. "Quantitative perspectives in Cognitive Linguistics." Review of Cognitive Linguistics 17, no. 1 (August 20, 2019): 7–28. http://dx.doi.org/10.1075/rcl.00024.jan.

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Abstract As a usage-based approach to the study language, cognitive linguistics is theoretically well poised to apply quantitative methods to the analysis of corpus and experimental data. In this article, I review the historical circumstances that led to the quantitative turn in cognitive linguistics and give an overview of statistical models used by cognitive linguists, including chi-square test, Fisher test, Binomial test, t-test, ANOVA, correlation, regression, classification and regression trees, naïve discriminative learning, cluster analysis, multi-dimensional scaling, and correspondence analysis. I stress the essential role of introspection in the design and interpretation of linguistic studies, and assess the pros and cons of the quantitative turn. I also make a case for open access science and appropriate archiving of linguistic data.
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Silva-Corvalán, Carmen. "Analyzing Linguistic Variation: Statistical Models and Methods." Journal of Linguistic Anthropology 16, no. 2 (December 2006): 295–96. http://dx.doi.org/10.1525/jlin.2006.16.2.295.

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11

Pande, Hemlata, and H. S. Dhami. "Statistical Methods in Language and Linguistic Research." Journal of Quantitative Linguistics 21, no. 3 (June 17, 2014): 295–97. http://dx.doi.org/10.1080/09296174.2014.911507.

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12

Parunak, H. Van Dyke, Yehuda T. Radday, and Haim Shore. "Genesis: An Authorship Study in Computer-Assisted Statistical Linguistics." Journal of Biblical Literature 106, no. 3 (September 1987): 518. http://dx.doi.org/10.2307/3261076.

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13

Davies, G. I., Y. T. Radday, and H. Shore. "Genesis. An Authorship Study in Computer-Assisted Statistical Linguistics." Vetus Testamentum 42, no. 1 (January 1992): 134. http://dx.doi.org/10.2307/1519137.

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14

Davies, Paul, and Y. T. Radday. "Genesis: An Authorship Study in Computer-Assisted Statistical Linguistics." Applied Statistics 35, no. 2 (1986): 218. http://dx.doi.org/10.2307/2347278.

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15

Kuznetsova, Olena. "Traditional and new chapters of linguistics in works of Ivan Franko in research by Professor Olexandra Serbenska." Proceedings of Research and Scientific Institute for Periodicals, no. 9(27) (2019): 271–83. http://dx.doi.org/10.37222/2524-0331-2019-9(27)-16.

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Linguistics in works of Ivan Franko, or the so-called «Lingvofrankiana » is one of the key directions of numerous researches of Professor of Journalism Department of The Ivan Franko National University in Lviv, Oleksandra Serbenska, Doctor of Philological Sciences, Professor. It is very relevant in the conditions of a fight for the role of the Ukrainian language as the basis for the Ukrainian state. The article rediscovers numerous linguistics chapters in monograph and works of Oleksandra Serbenska dealing with the research of Ivan Franko’s works so that to spread knowledge about Ukrainian linguistics, Ukrainian language and the language of the Ukrainian genius, Ivan Franko. It also classified her scientific publications about Ivan Franko according to genres and periods. Additionally, it interpreted, analyzed and differentiated these publications per the traditional, new and dominant chapters of the applied linguistics utilizing the methods of identification, systematization, linguistics and statistical analysis. The novelty of the research is a complex approach to the discovery of traditional and new chapters of linguistics in works of professor Oleksandra Serbenska about the language, language creation and linguistics of Ivan Franko. The findings gained in the course of the research of Ivan Franko’s works’ linguistics by Prof. Oleksandra Serbenska have important theoretical, linguistic and practical meaning and support the development of research of Ivan Franko heritage. Keywords: Olexandra Serbenska, linguistic research of Franko works, terminology, lexicography, stylistics, linguistic semantics, sociolinguistics, communicative linguistics, cognitive linguistics, linguistics concepts, linguistic phenomenology.
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Cantos Gómez, Pascual. "Do we need statistics when we have linguistics?" DELTA: Documentação de Estudos em Lingüística Teórica e Aplicada 18, no. 2 (2002): 233–71. http://dx.doi.org/10.1590/s0102-44502002000200003.

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Statistics is known to be a quantitative approach to research. However, most of the research done in the fields of language and linguistics is of a different kind, namely qualitative. Succinctly, qualitative analysis differs from quantitative analysis is that in the former no attempt is made to assign frequencies, percentages and the like, to the linguistic features found or identified in the data. In quantitative research, linguistic features are classified and counted, and even more complex statistical models are constructed in order to explain these observed facts. In qualitative research, however, we use the data only for identifying and describing features of language usage and for providing real occurrences/examples of particular phenomena. In this paper, we shall try to show how quantitative methods and statistical techniques can supplement qualitative analyses of language. We shall attempt to present some mathematical and statistical properties of natural languages, and introduce some of the quantitative methods which are of the most value in working empirically with texts and corpora, illustrating the various issues with numerous examples and moving from the most basic descriptive techniques (frequency counts and percentages) to decision-taking techniques (chi-square and z-score) and to more sophisticated statistical language models (Type-Token/Lemma-Token/Lemma-Type formulae, cluster analysis and discriminant function analysis).
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17

Rizakulovich, Irmatov Ikhtiyor. "Economic relations in the terminological system." Linguistics and Culture Review 5, S1 (December 13, 2021): 1623–31. http://dx.doi.org/10.21744/lingcure.v5ns1.1944.

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The purpose of the study is to study the peculiarities of terminology based on the analysis of economic relations in the dictionaries of Uzbek linguistics. In order to achieve the purpose of the research, the following tasks are identified: analysis of the literature on the relationship between terms and equonyms, a critical approach to existing theories; highlighting the relationship between terms and equonyms; coverage of equonyms and equonymic relations in Uzbek linguistic terminology. The following methods of scientific analysis were used in the research: linguistic description, system, statistical, contextual analysis methods. The method of linguistic analysis was used in the analysis of the scientific literature on the research topic. The system analysis method was used to illuminate the relationship. Statistical analysis has been widely used in the study of the place of linguistic terms in the Uzbek dictionaries. The results of the research allowed to develop the following scientific and practical conclusions and recommendations: analysis of terms and equonymic relations, helps to organize the terms and interpret the meanings and include them in the general dictionary. In particular, in Uzbek linguistics, issues such as the relationship between equonyms and terms, the peculiarities of linguistic terms are relevant.
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18

Toirova, Guli Ibragimovna. "THE IMPORTANCE OF LINGUISTIC MODELS IN THE DEVELOPMENT OF LANGUAGE BASES GE BASE." Scientific Reports of Bukhara State University 4, no. 6 (December 29, 2020): 98–106. http://dx.doi.org/10.52297/2181-1466/2020/4/6/8.

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Relevance. In Uzbek linguistics, a number of studies have been carried out on automatic translation, the development of the linguistic foundations of the author's corpus, the processing of lexicographic texts and linguistic-statistical analysis. However, the processing of the Uzbek language as the language of the Internet: spelling, automatic processing and translation programs, search programs for various characters, text generation, the linguistic basis of the text corpus and national corpus, the technology of its software is not studied in any monograph. The article discusses such problems as: the transformation of language into the language of the Internet, computer technology, mathematical linguistics, its continuation and the formation and development of computer linguistics, in particular the question of modeling natural languages for artificial intelligence. The Uzbek National Corps plays an important role in enhancing the international status of the Uzbek language.
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19

Brown, James Dean. "Resources on quantitative/statistical research for applied linguists." Second Language Research 20, no. 4 (October 2004): 372–93. http://dx.doi.org/10.1191/0267658304sr245ra.

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The purpose of this review article is to survey and evaluate existing books on quantitative/statistical research in applied linguistics. The article begins by explaining the types of texts that will not be reviewed, then it briefly describes nine books that address how to do quantitative/statistical applied linguistics research. The review then compares (in prose and tables) the general characteristics of these nine books, and their coverage of conceptual and statistical topics. Examining all nine books together provides answers to three questions: What is the range of conceptual/statistical topics in applied linguistics? What are the most important conceptual/statistical topics in applied linguistics? And, how has the coverage of topics changed over the years? This review article should help readers sort through current books on how to do quantitative/statistical research so that they can locate one or more texts that will best meet their needs.
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Baayen, R. Harald, and Eugene Charniak. "Statistical Language Learning." Language 73, no. 3 (September 1997): 588. http://dx.doi.org/10.2307/415888.

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Hug, Marc. "French demonstrative particlesciandlà: Linguistic intuitions and statistical facts." Journal of Quantitative Linguistics 5, no. 3 (December 1998): 195–205. http://dx.doi.org/10.1080/09296179808590127.

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Gonulal, Talip, Shawn Loewen, and Luke Plonsky. "The development of statistical literacy in applied linguistics graduate students." ITL - International Journal of Applied Linguistics 168, no. 1 (September 22, 2017): 4–32. http://dx.doi.org/10.1075/itl.168.1.01gon.

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Abstract Statistics play an important role in analyzing data in applied linguistics research. Given the increase over time in the field’s reliance on quantitative analysis (Brown, 2004; Gass, 2009), statistical literacy is critical for both producers and consumers of L2 research. Although there has been some investigation into statistical literacy among applied linguists, no research to date has examined how such literacy is obtained by masters and doctoral students in the field. The present study investigated the development of statistical literacy in a sample of such students taking semester-long discipline-specific quantitative research methods courses. Participants completed a pre-course and post-course survey. The results indicate that participants increased their knowledge of basic descriptive statistics and common inferential statistics to a great extent. Furthermore, participants reported that they felt more confident interpreting and using statistics. Based on these findings, recommendations for improving methodological practices and graduate training in our field are provided.
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Čermák, František. "Today's corpus linguistics." International Journal of Corpus Linguistics 7, no. 2 (December 31, 2002): 265–82. http://dx.doi.org/10.1075/ijcl.7.2.06cer.

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The paper is concerned with problems of methodology. Against this background, the situation of today's corpora is discussed and some fields are identified as being in a far from satisfactory shape. The place of corpora in linguistics is briefly looked at, suggesting that structuralist tradition is the only one to use them extensively. Problems of annotation and ways, less (statistical) or more successful (rule-based), are raised and discussed. Here, some of the most serious shortcomings, such as multi-word units or status of language units in general that computational linguists should deal with, are listed. In a more general direction, implications and status of paradigmatics and syntagmatics are discussed, too, with considerable and critical attention paid to ontologies.
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DE GISPERT, A., and J. B. MARIÑO. "Linguistic knowledge in statistical phrase-based word alignment." Natural Language Engineering 12, no. 1 (December 6, 2005): 91–108. http://dx.doi.org/10.1017/s1351324905003931.

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In this paper, a novel phrase alignment strategy combining linguistic knowledge and cooccurrence measures extracted from bilingual corpora is presented. The algorithm is mainly divided into four steps, namely phrase selection and classification, phrase alignment, one-to-one word alignment and postprocessing. The first stage selects a linguistically-derived set of phrases that convey a unified meaning during translation and are therefore aligned together in parallel texts. These phrases include verb phrases, idiomatic expressions and date expressions. During the second stage, very high precision links between these selected phrases for both languages are produced. The third step performs a statistical word alignment using association measures and link probabilities with the remaining unaligned tokens, and finally the fourth stage takes final decisions on unaligned tokens based on linguistic knowledge. Experiments are reported for an English-Spanish parallel corpus, with a detailed description of the evaluation measure and manual reference used. Results show that phrase cooccurrence measures convey a complementary information to word cooccurrences and a stronger evidence of a correct alignment, successfully introducing linguistic knowledge in a statistical word alignment scheme. Precision, Recall and Alignment Error Rate (AER) results are presented, outperforming state-of-the-art alignment algorithms.
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Loewen, Shawn, Talip Gönülal, Daniel R. Isbell, Laura Ballard, Dustin Crowther, Jungmin Lim, Jeffrey Maloney, and Magda Tigchelaar. "HOW KNOWLEDGEABLE ARE APPLIED LINGUISTICS AND SLA RESEARCHERS ABOUT BASIC STATISTICS?" Studies in Second Language Acquisition 42, no. 4 (October 11, 2019): 871–90. http://dx.doi.org/10.1017/s0272263119000548.

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AbstractDespite the prevalence of quantitative approaches in applied linguistics (AL) and second language acquisition (SLA) research (Gass, 2009), evidence indicates a need for improvement in analyzing and reporting SLA data (e.g., Larson-Hall & Plonsky, 2015). However, to improve quantitative research, researchers must possess the statistical knowledge necessary to conduct quality research. This study assesses AL and SLA researchers’ knowledge of key statistical concepts on a statistical knowledge test. One hundred and ninety-eight AL and SLA researchers from North America and Europe responded to 26 discipline-specific questions designed to measure participants’ ability to (a) understand basic statistical concepts and procedures, (b) interpret statistical analyses, and (c) critically evaluate statistical information. Results indicate that participants generally understood basic descriptive statistics, but performance on items requiring more advanced statistical knowledge was lower. Quantitative research orientation, number of statistics courses taken, and frequent use of statistics textbooks had positive influences on researchers’ statistical knowledge.
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Th. Gries, Stefan. "The most under-used statistical method in corpus linguistics: multi-level (and mixed-effects) models." Corpora 10, no. 1 (April 2015): 95–125. http://dx.doi.org/10.3366/cor.2015.0068.

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Much statistical analysis of psycholinguistic data is now being done with so-called mixed-effects regression models. This development was spearheaded by a few highly influential introductory articles that (i) showed how these regression models are superior to what was the previous gold standard and, perhaps even more importantly, (ii) showed how these models are used practically. Corpus linguistics can benefit from mixed-effects/multi-level models for the same reason that psycholinguistics can – because, for example, speaker-specific and lexically specific idiosyncrasies can be accounted for elegantly; but, in fact, corpus linguistics needs them even more because (i) corpus-linguistic data are observational and, thus, usually unbalanced and messy/noisy, and (ii) most widely used corpora come with a hierarchical structure that corpus linguists routinely fail to consider. Unlike nearly all overviews of mixed-effects/multi-level modelling, this paper is specifically written for corpus linguists to get more of them to start using these techniques more. After a short methodological history, I provide a non-technical introduction to mixed-effects models and then discuss in detail one example – particle placement in English – to show how mixed-effects/multi-level modelling results can be obtained and how they are far superior to those of traditional regression modelling.
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Schneider, Edgar W., and William A. Kretzschmar. "LAMSAS goes SASsy: Statistical Methods and Linguistic Atlas Data." Journal of English Linguistics 22, no. 1 (April 1989): 129–36. http://dx.doi.org/10.1177/007542428902200118.

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Hutchinson, Sterling, and Max Louwerse. "Language statistics and individual differences in processing primary metaphors." Cognitive Linguistics 24, no. 4 (November 20, 2013): 667–87. http://dx.doi.org/10.1515/cog-2013-0023.

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AbstractResearch in cognitive linguistics has emphasized the role of embodiment in metaphor comprehension, with experimental research showing activation of perceptual simulations when processing metaphors. Recent research in conceptual processing has demonstrated that findings attributed to embodied cognition can be explained through language statistics. The current study investigates whether language statistics explain processing of primary metaphors and whether this effect is modified by the gender of the participant. Participants saw word pairs with valence (Experiment 1: good–bad), authority (Experiment 2: doctor–patient), temperature (Experiment 3: hot–cold), or gender (Experiment 4: male–female) connotations. The pairs were presented in either a vertical configuration (X above Y or Y above X) matching the primary metaphors (e.g., HAPPY IS UP, CONTROL IS UP) or a horizontal configuration (X left of Y or Y left of X) not matching the primary metaphors. Even though previous research has argued that primary metaphor processing can best be explained by an embodied cognition account, results demonstrate that statistical linguistic frequencies also explain the response times of the stimulus pairs both in vertical and horizontal configurations, because language has encoded embodied relations. In addition, the effect of the statistical linguistic frequencies was modified by participant gender, with female participants being more sensitive to statistical linguistic context than male participants.
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Vasishth, Shravan, and Bruno Nicenboim. "Statistical Methods for Linguistic Research: Foundational Ideas - Part I." Language and Linguistics Compass 10, no. 8 (August 2016): 349–69. http://dx.doi.org/10.1111/lnc3.12201.

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Nicenboim, Bruno, and Shravan Vasishth. "Statistical methods for linguistic research: Foundational Ideas-Part II." Language and Linguistics Compass 10, no. 11 (November 2016): 591–613. http://dx.doi.org/10.1111/lnc3.12207.

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BOONKWAN, Prachya, and Thepchai SUPNITHI. "A Linguistics-Driven Approach to Statistical Parsing for Low-Resourced Languages." IEICE Transactions on Information and Systems E98.D, no. 5 (2015): 1045–52. http://dx.doi.org/10.1587/transinf.2014dap0024.

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Kunnan, Antony John. "Statistical analyses for test fairness." Revue française de linguistique appliquée XV, no. 1 (2010): 39. http://dx.doi.org/10.3917/rfla.151.0039.

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Piotrowska, W., and X. Piotrowska. "Statistical Parameters in Pathological Text." Journal of Quantitative Linguistics 11, no. 1-2 (April 2004): 133–40. http://dx.doi.org/10.1080/09296170512331383685.

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Benamara, Farah, Maite Taboada, and Yannick Mathieu. "Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications." Computational Linguistics 43, no. 1 (April 2017): 201–64. http://dx.doi.org/10.1162/coli_a_00278.

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The study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including sociology, psychology, economics, linguistics, and computer science. A number of excellent computational linguistics and linguistic surveys of the field exist. Most surveys, however, do not bring the two disciplines together to show how methods from linguistics can benefit computational sentiment analysis systems. In this survey, we show how incorporating linguistic insights, discourse information, and other contextual phenomena, in combination with the statistical exploitation of data, can result in an improvement over approaches that take advantage of only one of these perspectives. We first provide a comprehensive introduction to evaluative language from both a linguistic and computational perspective. We then argue that the standard computational definition of the concept of evaluative language neglects the dynamic nature of evaluation, in which the interpretation of a given evaluation depends on linguistic and extra-linguistic contextual factors. We thus propose a dynamic definition that incorporates update functions. The update functions allow for different contextual aspects to be incorporated into the calculation of sentiment for evaluative words or expressions, and can be applied at all levels of discourse. We explore each level and highlight which linguistic aspects contribute to accurate extraction of sentiment. We end the review by outlining what we believe the future directions of sentiment analysis are, and the role that discourse and contextual information need to play.
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Pomuran, Natalja N., and Elena N. Kirichenko. "Developing Intercultural Sensitivity in Students of Linguistics." Bulletin of Kemerovo State University 21, no. 2 (July 8, 2019): 414–20. http://dx.doi.org/10.21603/2078-8975-2019-21-2-414-420.

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The research deals with the specific features of developing intercultural sensitivity in students of Linguistics in a non- linguistic environment. The paper comprises scientific literature analysis on intercultural sensitivity and the factors of its development. Chen and Starosta's five-factor Intercultural Sensitivity Scale, supplemented by a questionnaire, was employed to measure intercultural sensitivity of 1st, 2nd, and 3d year students majoring in Linguistics. The calculations employed the methods of statistical processing of Student T-test and Fisher criterion. The intercultural sensitivity of students was above average (mean value = 89.47), whereas the increase of all components of intercultural sensitivity, except "Interaction confidence", correlated with the level of foreign language proficiency. In addition, senior students revealed more respect for cultural differences, though they were less confident than junior ones. Moreover, the difference between these two components increased from the 1st year to the3d year of study. Finally, interaction confidence increased if students had travelled overseas and had an experience of interaction with culturally different counterparts. The results and conclusions can be used for the design of programs and training courses for translators and teachers of foreign languages in a non-linguistic environment.
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Biber, Douglas. "Corpus linguistics and the study of literature." Future of Scientific Studies in Literature 1, no. 1 (May 23, 2011): 15–23. http://dx.doi.org/10.1075/ssol.1.1.02bib.

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The present paper introduces corpus-based analytical techniques and surveys some of the specific ways in which corpus analysis has been applied to the study of literature. In recent years, those research efforts have been mostly carried out under the umbrella of ‘corpus stylistics’. Most of these studies focus on the distribution of words (analyzing keywords, extended lexical phrases, or collocations) to identify textual features that are especially characteristic of an author or particular text. Corpus-based grammatical and pragmatic analyses of literary language are also briefly considered. Then, in the concluding part of the paper, I briefly survey earlier computational and statistical research on authorship attribution and literary style. While that research tradition is in some ways the precursor to more recent work in corpus stylistics, it is also complementary to recent research in its application of sophisticated statistical and computational methods.
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Shutova, Ekaterina, Simone Teufel, and Anna Korhonen. "Statistical Metaphor Processing." Computational Linguistics 39, no. 2 (June 2013): 301–53. http://dx.doi.org/10.1162/coli_a_00124.

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Metaphor is highly frequent in language, which makes its computational processing indispensable for real-world NLP applications addressing semantic tasks. Previous approaches to metaphor modeling rely on task-specific hand-coded knowledge and operate on a limited domain or a subset of phenomena. We present the first integrated open-domain statistical model of metaphor processing in unrestricted text. Our method first identifies metaphorical expressions in running text and then paraphrases them with their literal paraphrases. Such a text-to-text model of metaphor interpretation is compatible with other NLP applications that can benefit from metaphor resolution. Our approach is minimally supervised, relies on the state-of-the-art parsing and lexical acquisition technologies (distributional clustering and selectional preference induction), and operates with a high accuracy.
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Fukushige, Yoshio, and Naohiko Noguchi. "Statistical and linguistic approaches to automatic term recognition." Terminology 6, no. 2 (December 31, 2000): 257–86. http://dx.doi.org/10.1075/term.6.2.08fuk.

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In this article we describe our approaches and the results to the Term Recognition (TMREC) task in the first NTCIR Workshop on Research in Japanese Text. Retrieval and Term Recognition, held 30 August-1 September 1999. Our first approach aims to collect words that appear distinctively in documents of the target domain through statistical method. Our second approach aims to collect terms that have a particular inner structure by applying several diagnostic tests using the collocational information in the corpus. Section 1 describes the outline of the term recognition task. Section 2 briefly describes the two approaches, details of which are described in Sections 3 and 4. In Section 6, we offer a short discussion based on the comparison between the candidates.
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Seneff, Stephanie, and Chao Wang. "Statistical modeling of phonological rules through linguistic hierarchies." Speech Communication 46, no. 2 (June 2005): 204–16. http://dx.doi.org/10.1016/j.specom.2005.03.005.

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40

Pociecha, Józef. "The paradigm of statistical inference and the paradigm of statistical learning." Przegląd Statystyczny 68, no. 1 (August 24, 2021): 1–16. http://dx.doi.org/10.5604/01.3001.0015.0375.

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The starting point for the presentation of the similarities and differences between the principles of conducting statistical research according to the rules of both statistical inference and statistical learning is the paradigm theory, formulated by Thomas Kuhn. In the first section of this paper, the essential features of the statistical inference paradigm are characterised, with particular attention devoted to its limitations in contemporary statistical research. Subsequently, the article presents the challenges faced by this research jointly with the expanding opportunities for their effective reduction. The essence of learning from data is discussed and the principles of statistical learning are defined. Moreover, significant features of the statistical learning paradigm are formulated in the context of the differences between the statistical inference paradigm and the statistical learning paradigm. It is emphasised that the statistical learning paradigm, as the more universal one of the two discussed, broadens the possibilities of conducting statistical research, especially in socio-economic sciences.
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Treiman, Rebecca. "Statistical Learning and Spelling." Language, Speech, and Hearing Services in Schools 49, no. 3S (August 14, 2018): 644–52. http://dx.doi.org/10.1044/2018_lshss-stlt1-17-0122.

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Purpose The purpose of this article is to provide a tutorial on statistical learning and its role in learning to spell and to discuss the implications of the research for educators. Method The tutorial begins with a discussion of statistical learning and its characteristics. It then discusses research on how statistical learning plays out in learning to spell, how spelling should be taught, and similarities and differences among learners. The focus is on the learning of English, although studies of other alphabetic writing systems are also considered. Research shows that, from an early age, children use their statistical learning skills to learn about the visual characteristics of written words. Children also use their statistical learning skills to help learn about the relations between visual units and units of language, supplementing what they are explicitly taught in school. Conclusion Statistical learning plays an important role in learning to spell, and this can help to explain why some aspects of spelling are more difficult to learn than others. If children are to learn to spell effectively and efficiently, structured instruction is also important.
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Arciuli, Joanne. "Reading as Statistical Learning." Language, Speech, and Hearing Services in Schools 49, no. 3S (August 14, 2018): 634–43. http://dx.doi.org/10.1044/2018_lshss-stlt1-17-0135.

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Purpose The purpose of this tutorial is to explain how learning to read can be thought of as learning statistical regularities and to demonstrate why this is relevant for theory, modeling, and practice. This tutorial also shows how triangulation of methods and cross-linguistic research can be used to gain insight. Method The impossibility of conveying explicitly all of the regularities that children need to acquire in a deep orthography, such as English, can be demonstrated by examining lesser-known probabilistic orthographic cues to lexical stress. Detection of these kinds of cues likely occurs via a type of implicit learning known as statistical learning (SL). The first part of the tutorial focuses on these points. Next, studies exploring how individual differences in the capacity for SL relate to variability in word reading accuracy in the general population are discussed. A brief overview of research linking impaired SL and dyslexia is also provided. The final part of the tutorial focuses on how we might supplement explicit literacy instruction with implicit learning methods and emphasizes the value of testing the efficacy of new techniques in the classroom. The basic and applied research reviewed here includes corpus analyses, behavioral testing, computational modeling, and classroom-based research. Although some of these methods are not commonly used in clinical research, the depth and breadth of this body of work provide a compelling case for why reading can be thought of as SL and how this view can inform practice. Conclusion Implicit methods that draw on the principles of SL can supplement the much-needed explicit instruction that helps children learn to read. This synergy of methods has the potential to spark innovative practices in literacy instruction and remediation provided by educators and clinicians to support typical learners and those with developmental disabilities.
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Bulgarelli, Federica, Amy L. Lebkuecher, and Daniel J. Weiss. "Statistical Learning and Bilingualism." Language, Speech, and Hearing Services in Schools 49, no. 3S (August 14, 2018): 740–53. http://dx.doi.org/10.1044/2018_lshss-stlt1-17-0139.

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Purpose Over the last 2 decades, research on statistical learning has demonstrated its importance in supporting language development. Notably, most of the research to date has focused on monolingual populations (or has not reported the language background of participants). Several recent studies, however, have begun to focus on the impact of bilingualism on statistical learning. To date, the results have been quite mixed, with a handful of studies finding differences between monolinguals and bilinguals and several other studies reporting no differences. Thus, the purpose of this manuscript is to review the literature to date on how bilingualism impacts statistical learning abilities. Method We review the contemporary literature, organized by the age of participants and by task when relevant. Conclusions We note that there are many discrepant findings within this nascent field, although some trends have emerged. For instance, differences in performance may be attributed to factors such as age of acquisition. However, we note that the state of the field does not yet permit firm clinical recommendations.
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Preston, Dennis R. "Variation linguistics and SLA." Second Language Research 9, no. 2 (June 1993): 153–72. http://dx.doi.org/10.1177/026765839300900205.

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Sociolinguistics (here called variationist linguistics) has been misunderstood and misrepresented in second language acquistion (SLA) research. In spite of that, several productive studies (many of which use the VARBRUL statistical program) have made significant contributions to our understandings of variation in SLA data, contributions which touch on the linguistic and not the social concerns of such data. The failure of SLA researchers who belong to the so-called 'dominant paradigm' (or Chomskyan or Universal Grammar (UG) research programme) to realize that belief in a so-called variable competence is not a prerequisite to variation studies has been particularly harmful. On the other hand, the failure of sociolinguists to take psycholinguistic matters seriously has been another serious drawback to interfield co-operation; a summary of a plausible variationist psycholinguistics (within an SLA setting and allowing UG interpretation) is provided.
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Sankoff, David, and Pascale Rousseau. "Statistical evidence for rule ordering." Language Variation and Change 1, no. 1 (March 1989): 1–18. http://dx.doi.org/10.1017/s0954394500000090.

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ABSTRACTA set of ordered rules for generating variants of a variable determines (a) underlying/surface distinctions among some of the variants and (b) a hierarchical classification of the variants. In the analytical framework of variable rules, frequency data on variant occurrences in context bear only on (b) and not on (a). We provide a combinatorial characterization and enumeration of the set of classifications on n variants, the set of underlying/surface configurations, and the set of rule orders. We describe the statistical and computational techniques for generalizing variable rule analysis to the inference of rule order. These procedures are applied to the problems of the reduction of syllable-final consonants <s>, <n>, and <r> in Caribbean Spanish (n = 3, 4, 6 variants, respectively). Previous analyses have tended to assume that successive weakenings occur in an intrinsic order determined by phonological strength. Our results show that aspiration and deletion can indeed be seen to be intrinsically ordered in both <s> and <r> reduction, though an unordered analysis is equally likely in the case of <s>. On the other hand, velarization and deletion of <n> are unordered, while vocalization is a subsequent process, independent of the other two. Similarly, spirantization, aspiration, and lateralization of <r> are unordered, as confirmed by data sets from both Puerto Rican and Panamanian speakers. Furthermore, with both <n> and <r>, intrinsically ordered rule schemata proved to be extremely unlikely by statistical criteria. Syllable-final consonant reduction then consists of largely independent processes, most of which occur simultaneously.
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Davidson, Fred. "The language tester's statistical toolbox." System 28, no. 4 (December 2000): 605–17. http://dx.doi.org/10.1016/s0346-251x(00)00041-5.

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Xiaojun, Z. "Philipp Koehn: Statistical Machine Translation." Applied Linguistics 32, no. 3 (April 28, 2011): 359–62. http://dx.doi.org/10.1093/applin/amr017.

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Huang, Yi Ting, Mary Bounds, and Yuichi Suzuki. "Learning the Causative Alternation in English and Japanese Speakers: Statistical and Non-statistical Effects." Language Learning and Development 15, no. 4 (July 30, 2019): 338–49. http://dx.doi.org/10.1080/15475441.2019.1645667.

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Scriven, Michael. "Fallacies of statistical substitution." Argumentation 1, no. 3 (1987): 333–49. http://dx.doi.org/10.1007/bf00136782.

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Davis, Lawrence M. "Sampling and Statistical Inference in Dialectology." Journal of English Linguistics 19, no. 1 (April 1986): 42–48. http://dx.doi.org/10.1177/007542428601900104.

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