Relevant bibliographies by topics / Language sampling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Language sampling'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Language sampling"

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Rijkhoff, Jan, Dik Bakker, Kees Hengeveld, and Peter Kahrel. "A Method of Language Sampling." Studies in Language 17, no. 1 (January 1, 1993): 169–203. http://dx.doi.org/10.1075/sl.17.1.07rij.

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In recent years more attention is being paid to the quality of language samples in typological work. Without an adequate sampling strategy, samples may suffer from various kinds of bias. In this article we propose a sampling method in which the genetic criterion is taken as the most important: samples created with this method will reflect optimally the diversity of the languages of the world. On the basis of the internal structure of each genetic language tree a measure is computed that reflects the linguistic diversity in the language families represented by these trees. This measure is used to determine how many languages from each phylum should be selected, given any required sample size.
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Hux, Karen, Mary Morris-Friehe, and Dixie D. Sanger. "Language Sampling Practices." Language, Speech, and Hearing Services in Schools 24, no. 2 (April 1993): 84–91. http://dx.doi.org/10.1044/0161-1461.2402.84.

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A total of 239 school-based speech-language pathologists from nine midwestern states were surveyed about their language sampling practices. Survey data provided information on collection and analysis procedures and on clinicians' attitudes toward language sampling. Findings revealed that school speech-language pathologists routinely supplement quantitative assessment procedures with language sample analyses, are sensitive to the effects of some contextual variables on sample representativeness, and use language sampling information to assist in planning intervention services. Points of concern surfacing from the data analysis included clinician reliance on self-designed analysis procedures, limitations in the types of students assessed through language sampling, and the lack of research consultation when clinicians make decisions about collection and analysis procedures.
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Rojas, Raúl, and Aquiles Iglesias. "Using Language Sampling to Measure Language Growth." Perspectives on Language Learning and Education 17, no. 1 (March 2010): 24–31. http://dx.doi.org/10.1044/lle17.1.24.

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Abstract This article illustrates how speech-language pathologists (SLPs) can use language sampling and growth curve modeling (GCM) to examine the language growth rates of English Language Learners. GCM data on language samples provides SLPs with powerful, new tools to evaluate actual progress over time instead of relying on single, static measurement endpoints to determine typical development.
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Gui, Wenli, Liping Jing, Liu Yang, and Jian Yu. "Unsupervised Cross-Language Classification with Stratified Sampling-Based Cluster Ensemble." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 165–71. http://dx.doi.org/10.7763/ijmlc.2015.v5.502.

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Furey, Joan E., and Ruth V. Watkins. "Accuracy of Online Language Sampling." American Journal of Speech-Language Pathology 11, no. 4 (November 2002): 434–39. http://dx.doi.org/10.1044/1058-0360(2002/046).

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This study investigated the accuracy of online language sample data collection. Language samples were collected from 22 preschoolers (11 with language impairments and 11 typically developing) using a semiscripted, play-based sampling procedure designed to elicit 50 target verbs. During each sampling session, examiners recorded the target verbs a child produced. The online target verb repertoire for each child was then compared with the target verb repertoire extracted from transcription of the audiotape. Results indicated strong positive correlations between target verb repertoires recorded online and those obtained from transcription. Our results indicate that online transcription can be a useful procedure for clinicians who may be restricted in their ability to use language sampling procedures because of the significant time required for transcription and subsequent sample analysis. Although the current study revealed strong accuracy in online recording for one relatively focused aspect of language production, additional investigation will be needed to evaluate real-time recording for a broader range of linguistic abilities.
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Finestack, Lizbeth H., Bita Payesteh, Jill Rentmeester Disher, and Hannah M. Julien. "Reporting Child Language Sampling Procedures." Journal of Speech, Language, and Hearing Research 57, no. 6 (December 2014): 2274–79. http://dx.doi.org/10.1044/2014_jslhr-l-14-0093.

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Purpose Despite the long history of language sampling use in the study of child language development and disorders, there are no set guidelines specifying the reporting of language sampling procedures. The authors propose reporting standards for use by investigators who employ language samples in their research. Method The authors conducted a literature search of child-focused studies published in journals of the American Speech-Language-Hearing Association between January 2000 and December 2011 that included language sampling procedures to help characterize child participants or to derive measures to serve as dependent variables. Following this search, they reviewed each study and documented the language sampling procedures reported. Results The authors' synthesis revealed that approximately 25% of all child-focused studies use language samples to help characterize participants and/or derive dependent variables. They found remarkable inconsistencies in the reporting of language sampling procedures. Conclusion To maximize the conclusions drawn from research using language samples, the authors strongly encourage investigators of child language to consistently report language sampling procedures using the proposed reporting checklist.
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Bain, Barbara A., Lesley B. Olswang, and Glenn A. Johnson. "Language sampling for repeated measures with language-impaired preschoolers." Topics in Language Disorders 12, no. 2 (February 1992): 13–27. http://dx.doi.org/10.1097/00011363-199202000-00004.

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Klee, Thomas. "Clinical language sampling: analysing the analyses." Child Language Teaching and Therapy 1, no. 2 (July 1985): 182–98. http://dx.doi.org/10.1177/026565908500100206.

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Dryer, Matthew S. "Large Linguistic Areas and Language Sampling." Studies in Language 13, no. 2 (January 1, 1989): 257–92. http://dx.doi.org/10.1075/sl.13.2.03dry.

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Rojas, Raul, and Aquiles Iglesias. "Making a Case for Language Sampling." ASHA Leader 14, no. 3 (March 2009): 10–13. http://dx.doi.org/10.1044/leader.ftr1.14032009.10.

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Dissertations / Theses on the topic "Language sampling"

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Lundén, Daniel. "Delayed sampling in the probabilistic programming language Anglican." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210756.

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Many probabilistic inference algorithms, both exact and approximate, have been developed to run efficiently on various probabilistic models in the recent decades. For many probabilistic models, exact inference is, however, infeasible or impossible. As such, approximate algorithms are often necessary. In this thesis, a method for partially applying exact inference in probabilistic programming languages using Monte Carlo inference algorithms is introduced and formalized. More specifically, this method allows for conditioning on observations in the probabilistic program before performing sampling, where applicable. We show that this method, called delayed sampling, can be used to reduce mean squared error of estimators based on samples generated by probabilistic programs. We also show that delayed sampling never leads to an increase in mean squared error of estimators. An evaluation is performed with an implementation of delayed sampling in the probabilistic programming language Anglican. The results demonstrate clear reductions in mean squared error for simple examples, but the overhead is also shown to be quite substantial for the Anglican implementation.
Många probabilistiska inferensalgoritmer, både exakta och approximativa, har utvecklats för att fungera effektivt på olika probabilistiska modeller under de senaste decennierna. För många probabilistiska modeller är exakt inferens emellertid otänkbar eller omöjlig. På grund av detta är ofta approximativa algoritmer nödvändiga. I denna avhandling introduceras och formaliseras en metod för att delvis tillämpa exakt inferens i probabilistiska programmeringsspråk med Monte Carlo-inferensalgoritmer. Mer specifikt tillåter denna metod att konditionera på observationer i det probabilistiska programmet innan provtagning utförs, där så är tillämpligt. Vi visar att den här metoden, som kallas fördröjd provtagning, kan användas för att minska genomsnittliga kvadratiska fel för estimatorer som baseras på prover genererade av probabilistiska program. Vi visar också att fördröjd provtagning aldrig leder till en ökning av genomsnittliga kvadratiska fel för estimatorer. En utvärdering utförs med en implementering av fördröjd provtagning i det probabilistiska programmeringsspråket Anglican. Resultaten visar tydliga minskningar i genomsnittligt kvadratfel för enkla exempel, men beräkningskostnaderna visar sig också vara ganska betydande för implementationen i Anglican.
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Sealey, Linda Rae. "Effects of language sampling task on language production in children with typical development." Oklahoma City : [s.n.], 2008.

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Smith, Tanner. "The Dixit Method of Language Sampling in Early Adolescence." TopSCHOLAR®, 2018. https://digitalcommons.wku.edu/theses/2339.

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The current study examined two methods of language sampling (interview and the Dixit Method) in early adolescents with typical language development. The purpose of this study was to examine the effectiveness of the two procedures in eliciting lexical diversity in spoken language samples in the early adolescent population. To examine lexical diversity, traditional methods of analysis (type token ratio, average type token ratio, and mean length of utterance) were applied. However, because literature historically alludes to flaws in these traditional methods, the researchers also applied an ecological approach to analysis of lexical diversity proposed by Scott Jarvis (2013). Student participants (n = 22) in the sixth grade (11:0 -12:11 years of age) were recruited through a local middle school. Both methods of language sampling were compared in a within-subject design for diversity of spoken language samples by applying the previously mentioned analysis procedures. The data was evaluated using a pairedsamples two-tailed t test. Although the sample size was small, evidence from this study indicates the Dixit Method provides a more holistic view of lexical diversity than the traditional method by considering six ecological components that are reflected in typical language use.
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Anderson, Luke (Luke James). "An embedded domain specific sampling language for Monte Carlo rendering." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111909.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 95-96).
Implementing Monte Carlo integration requires significant domain expertise. While simple algorithms, such as unidirectional path tracing, are relatively forgiving, more complex algorithms, such as bidirectional path tracing or Metropolis methods, are notoriously difficult to implement correctly. We propose a domain specific language for Monte Carlo rendering that offers primitives and data structures for writing concise and correct-by-construction sampling code. The compiler then automatically generates the necessary code for evaluating PDFs and combining multiple samples. Our language focuses on ease of implementation for rapid exploration and research, at the cost of run time performance. We demonstrate the effectiveness of the language by implementing several challenging rendering algorithms, as well as a new algorithm, which would otherwise be prohibitively difficult to implement.
by Luke Anderson.
S.M.
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O’Malley, A. S., and A. Lynn Williams. "Language Sampling for Preschool Children with SLI: Comparison of Three Procedures." Digital Commons @ East Tennessee State University, 2001. https://dc.etsu.edu/etsu-works/2094.

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Dong, Cheryl Diane. "A comparative study of three language sampling methods using developmental sentence scoring." PDXScholar, 1986. https://pdxscholar.library.pdx.edu/open_access_etds/3589.

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The present study sought to determine the effect different stimulus material has on the language elicited from children. Its purpose was to determine whether a significant difference existed among language samples elicited three different ways when analyzed using DSS. Eighteen children between the ages of 3.6 and 5.6 years were chosen to participate in the study. All of the children had normal bearing. normal receptive vocabulary skills and no demonstrated or suspected physical or social delays. Three language samples. each elicited by either toys. pictures. or stories. were obtained from each child. For each sample. a corpus of 50 utterances was selected for analysis and analyzed according to the DSS procedure as described by Lee and Ganter (1971).
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Spangenberg, Amanda May. "Quantifying Uses of Open-Ended Questions and Contingent Comments in Language Sampling: A Methodological Study." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530870714910087.

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Cline, Amber. "Assessment of Academic Vocabulary in Early Adolescents Using a Novel Sampling Method." TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3117.

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The current study examined a method of language sampling (the Dixit Method- Science, Math, Engineering, Arts, and Math) in early adolescents with typically developing language. The purpose of this study was to examine the effectiveness of the DM-STEAM in eliciting lexically sophisticated spoken language samples in the early adolescent population. To examine lexical sophistication, traditional measures of analysis such as mean length of utterance (MLU) and average type token ratio (AVG TTR) were applied along with a measure of low frequency vocabulary. To compare performance on the DM-STEAM, school standardized assessments were obtained to measure student skill in academic content areas. Twenty-two student participants in the sixth grade (11 years to 12 years 11 months) were recruited from a local elementary school. The data was evaluated using a paired tailed t test and a path analysis test. Although the sample size is small, results from the study indicate the DM-STEAM elicits low frequency academic vocabulary in early adolescent populations.
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Lee, Hwakyoung Joanna. "Analysis of the relationship between pre-kindergarten children's drawing and language." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/419.

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Young children begin to draw spontaneously, and their drawings develop in complexity and skill as they become older. Previously, children's drawings have been utilized to assess aspects of emotional and cognitive development. On the other hand, very few studies have used children's drawings as a tool for understanding their language development. The purpose of this study was to examine the potential for using drawing as an assessment tool for language skill. Therefore, this study explored the relationship between pre-kindergarten children's drawing and language development and stability of this relationship over time. This study also examined whether children's English-language status influenced the drawing-language relationship. The study's subjects were 34 children who attended a pre-kindergarten program in a local elementary school located in a Mid-Western university town. These children participated in take-home reading and drawing activities once a week for one school year. Children's drawings that were completed after reading storybooks with their parents served as data documenting their drawing skills. Classroom teachers' observations of the children's language use were recorded on the Work Sampling System's developmental checklists, and this served as data to document their language skills. A hierarchical regression analysis revealed that children's drawing skill was a significant predictor to their language skill and explained 10% of the variance in language scores. This finding was consistent at both the beginning and end of the school year. Children's English-language status did not make a statistically significant difference in the predictive relationship of drawing for their language skills. Therefore, this study concluded that children's drawings can be used as a supplementary assessment tool to provide information about children's language skills, regardless of their primary language. However, more than half of the language variance was not identified from this study; this is an area for further investigation.
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Mooney, Aine M. "Language Sample Collection and Analysis in People Who Use AAC: A New Approach." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554294907619342.

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Books on the topic "Language sampling"

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Language sampling with adolescents. San Diego: Plural Pub., 2010.

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Language sampling with adolescents: Implications for intervention. San Diego, CA: Plural Publishing Inc., 2014.

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Tyack, Dorothy. Language sampling, analysis, and training: A handbook. 3rd ed. Austin, Tex: Pro.ed, 1999.

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A handbook of English grammar and language sampling. Englewood Cliffs, N.J: Prentice-Hall, 1988.

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Gardner, Sheena. Focus on language sampling: A key issue in EAL assessment. Warwick: NALDIC, 2002.

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Gore, Vivek. A quasi-polynomial-time algorithm for sampling words from a context-free language. Edinburgh: LFCS, Dept. of Computer Science, University of Edinburgh, 1995.

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Gao, Xiaohong. Sampling variability and generalizability of Word Keys listening and writing scores. Iowa City, Iowa: American College Testing Program, 1996.

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author, Bacci Silvia, and Gnaldi Michela author, eds. Statistical analysis of questionnaires: A unified approach based on R and Stata. Boca Raton: CRC Press, Taylor & Francos Group, 2016.

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Adaptive design theory and implementation using SAS and R. Boca Raton: CRC Press, 2014.

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Chang, Mark. Adaptive design theory and implementation using SAS and R. Boca Raton, FL: CRC Press, 2007.

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Book chapters on the topic "Language sampling"

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Lieven, Elena, and Heike Behrens. "Dense Sampling." In Research Methods in Child Language, 226–39. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781444344035.ch15.

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Leal, Tania. "Chapter 3. Data analysis and sampling." In Language Learning & Language Teaching, 63–88. Amsterdam: John Benjamins Publishing Company, 2018. http://dx.doi.org/10.1075/lllt.51.04lea.

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Naigles, Letitia R. "Not Sampling, Getting It All." In Research Methods in Child Language, 240–53. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781444344035.ch16.

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Hartmann, Peter, Song Jin, and Andreas Krause. "A Specification Language for Gibbs Sampling." In Compstat, 383–88. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-52463-9_45.

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Khanna, Sanjeev. "Matchings, Random Walks, and Sampling." In Language and Automata Theory and Applications, 32–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04921-2_3.

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Ross, Steven J. "Task Sequencing and Sampling on a Japanese LPI." In Interviewing for Language Proficiency, 197–224. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60528-9_9.

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Stoll, Sabine. "Sampling linguistic diversity to understand language development." In Current Perspectives on Child Language Acquisition, 247–62. Amsterdam: John Benjamins Publishing Company, 2020. http://dx.doi.org/10.1075/tilar.27.11sto.

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Wang, Chunli, Xilin Chen, and Wen Gao. "Re-sampling for Chinese Sign Language Recognition." In Lecture Notes in Computer Science, 57–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11678816_7.

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Penedo, Francisco, Cristian-Ioan Vasile, and Calin Belta. "Language-Guided Sampling-based Planning using Temporal Relaxation." In Springer Proceedings in Advanced Robotics, 128–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43089-4_9.

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Khajavy, Gholam Hassan, Peter D. MacIntyre, Tahereh Taherian, and Jessica Ross. "Examining the Dynamic Relationships Between Willingness to Communicate, Anxiety and Enjoyment Using the Experience Sampling Method." In Second Language Learning and Teaching, 169–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67634-6_9.

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Conference papers on the topic "Language sampling"

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Joshi, Deepak, and Shiv Dutt Joshi. "Improved language identification using sampling rate compensation & gender based language models for Indian languages." In 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2013. http://dx.doi.org/10.1109/ispcc.2013.6663413.

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Yang, Rui, and Zhen-Hua Ling. "Linguistic Steganography by Sampling-based Language Generation." In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023313.

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Osborne, Miles, Ashwin Lall, and Benjamin Van Durme. "Exponential Reservoir Sampling for Streaming Language Models." In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-2112.

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Park, Sungwoo, Frank Pfenning, and Sebastian Thrun. "A probabilistic language based upon sampling functions." In the 32nd ACM SIGPLAN-SIGACT sysposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1040305.1040320.

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Razo, Dante, and Sandra Kübler. "Investigating Sampling Bias in Abusive Language Detection." In Proceedings of the Fourth Workshop on Online Abuse and Harms. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.alw-1.9.

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Fernandez, Jared, and Doug Downey. "Sampling Informative Training Data for RNN Language Models." In Proceedings of ACL 2018, Student Research Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-3002.

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Li, Xiujun, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, and Yejin Choi. "Robust Navigation with Language Pretraining and Stochastic Sampling." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1159.

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Chen, Dong, Fangzhou Liu, Chen Ding, and Sreepathi Pai. "Locality analysis through static parallel sampling." In PLDI '18: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3192366.3192402.

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Logan IV, Robert L., Matt Gardner, and Sameer Singh. "On Importance Sampling-Based Evaluation of Latent Language Models." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.196.

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Pillai, Nisha, Edward Raff, Francis Ferraro, and Cynthia Matuszek. "Sampling Approach Matters: Active Learning for Robotic Language Acquisition." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378415.

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Reports on the topic "Language sampling"

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Dong, Cheryl. A comparative study of three language sampling methods using developmental sentence scoring. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5473.

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