Hostname: page-component-6bf8c574d5-rwnhh Total loading time: 0 Render date: 2025-02-21T12:49:59.208Z Has data issue: false hasContentIssue false
  • English
  • Français

Analyzing language samples of Spanish–English bilingual children for the automated prediction of language dominance

Published online by Cambridge University Press:  22 October 2010

T. SOLORIO ,
M. SHERMAN ,
Y. LIU ,
L. M. BEDORE ,
E. D. PEÑA  and
A. IGLESIAS
T. SOLORIO
Affiliation:
Department of Computer and Information Sciences, The University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA e-mail: [email protected]
M. SHERMAN
Affiliation:
Department of Computer Science, The University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080, USA e-mail: [email protected], [email protected]
Y. LIU
Affiliation:
Department of Computer Science, The University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080, USA e-mail: [email protected], [email protected]
L. M. BEDORE
Affiliation:
Department of Communication Sciences and Disorders, The University of Texas at Austin, 1 University Station A1100, Austin, TX 78712-0114, USA e-mail: [email protected], [email protected]
E. D. PEÑA
Affiliation:
Department of Communication Sciences and Disorders, The University of Texas at Austin, 1 University Station A1100, Austin, TX 78712-0114, USA e-mail: [email protected], [email protected]
A. IGLESIAS
Affiliation:
Department of Communication Sciences and Disorders, Temple University, 3307 N. Broad Street, Philadelphia, PA 19140, USA e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In this work we study how features typically used in natural language processing tasks, together with measures from syntactic complexity, can be adapted to the problem of developing language profiles of bilingual children. Our experiments show that these features can provide high discriminative value for predicting language dominance from story retells in a Spanish–English bilingual population of children. Moreover, some of our proposed features are even more powerful than measures commonly used by clinical researchers and practitioners for analyzing spontaneous language samples of children. This study shows that the field of natural language processing has the potential to make significant contributions to communication disorders and related areas.

Type
Articles
Information
Natural Language Engineering , Volume 17 , Issue 3 , July 2011 , pp. 367 - 395
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bedore, L. M., Fiestas, C. E., Peña, E. D., and Nagy, V. J. 2006. Crosslanguage comparisons of maze use in Spanish and English in functionally monolingual and bilingual children. Bilingualism: Language and Cognition 9 (3): 233–247.CrossRefGoogle Scholar
Bedore, L. M., Peña, E. D., Gillam, R. B., and Ho, T. (in press) Language sample measures and language ability in Spanish English bilingual kindergarteners. Journal of Communication Disorders.Google Scholar
Berman, R. A., and Slobin, D. I. 1994. Relating Events in Narrative: Crosslinguistic Developmental Study. Hillsdale, New Jersey: Lawrence Erlbaum Associates.Google Scholar
Bohman, T. M., Bedore, L. M., Peña, E. D., Mendez-Perez, A., and Gillam, R. B. 2010. What you hear and what you say: language performance in Spanish–English bilinguals. International Journal of Bilingual Education and Bilingualism 13 (3): 325–344.CrossRefGoogle ScholarPubMed
Brill, E., and Moore, R. C. 2000. An improved error model for noisy channel spelling correction. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics. Hong Kong. ACL.Google Scholar
Brown, R. 1973. A First Language: The Early Stages. Cambridge: Harvard University Press.CrossRefGoogle Scholar
Coleman, M., and Liau, T. L. 1975. A computer readability formula designed for machine scoring. Journal of Applied Psychology 60: 283–284.CrossRefGoogle Scholar
Dollaghan, C., and Campbell, T. 1992. A procedure for classifying disruptions in spontaneous language samples. Topics in Language Disorders 12: 56–68.CrossRefGoogle Scholar
Dollaghan, C., Campbell, T. F., Paradise, J., Feldman, H. M., Janosky, J. E., Pitcairn, D. N., and Kurs-Lasky, M. 1999. Maternal education and measures of early speech and language. Journal of Speech, Language and Hearing Research 42: 1432–1443.CrossRefGoogle ScholarPubMed
Feng, L., Elhadad, N., and Huenerfauth, M. 2009. Cognitively motivated features for readability assessment. In Proceedings of the 12th Conference of the European Chapter of the ACL, pp. 229–237. Athens, Grece, ACL.Google Scholar
Flesch, R. 1948. A new readability yardstick. Journal of Applied Psychology 32: 221–233.CrossRefGoogle ScholarPubMed
Gabani, K. 2009. Automatic Identification of Language Impairment in Monolingual English-Speaking Children. M.S. thesis, Department of Computer Science, The University of Texas at Dallas.Google Scholar
Gabani, K., Sherman, M., Solorio, T., Liu, Y., Bedore, L. M., and Peña, E. D. 2009. A corpus-based approach for the prediction of language impairment in monolingual English and Spanish–English bilingual children. In North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL–HLT) 2009, pp. 46–55, Boulder, Colorado. ACL.Google Scholar
Goldstein, B. and Kohnert, K. 2005. Speech, language, and hearing in developing bilingual children: current findings and future directions. Language, Speech and Hearing Services 36: 264–267.CrossRefGoogle ScholarPubMed
Grosjean, F. 1989. Neurolinguists, beware! The bilingual is not two monolinguals in one person. Brain and Language 36: 3–15.CrossRefGoogle Scholar
Gunning, R. 1952. The Technique of Clear Writing. New York, NY: R. McGraw-Hill International Book Co.Google Scholar
Guo, L.-Y., Tomblin, J. B., and Samelson, V. 2008. Speech disruptions in the narratives of English-speaking children with specific language impairment. Journal of Speech, Language and Hearing Research 51: 722–738.CrossRefGoogle ScholarPubMed
Gutiérrez-Clellen, V. F., and Kreiter, J. 2003. Understanding child bilingual acquisition using parent and teacher reports. Applied Psycholinguistics 24: 267–288.CrossRefGoogle Scholar
Jurafsky, D., and Martin, J. H. 2000. Speech and Language Processing: An Introduction to Natural Language Processing. Englewood Cliffs, New Jersey: Prentice Hall.Google Scholar
Kincaid, P. J., Fishburne, R. P., Rogers, R. L., and Chisson, B. S. 1975. Derivation of new readability formulas for Navy enlisted personnel. Research Branch Report 8-75, US Naval Air Station, Memphis, 1975.Google Scholar
Klee, T., and Fitzgerald, M. D. 1985. The relation between grammatical development and mean length of utterance in morphemes. Journal of Child Language 12: 251–269.CrossRefGoogle ScholarPubMed
Kowal, S., O'Connell, D. C., and Sabin, E. 1975. Development of temproal patterning and vocal hesitations in spontaneous narratives. Journal of Psycholinguistic Research 4: 195–207.CrossRefGoogle Scholar
Leonard, L. B. 1998. Children with Specific Language Impairment. Cambridge, MA: MIT Press.Google ScholarPubMed
Loban, W.Language Development: Kindergarten Through Grade Twelve. Urbana, IL: National Council of Teachers of English.Google Scholar
MacLachlan, B. G. and Chapman, R. S. 1988. Communication breakdowns in normal and language learning-disabled children's conversation and narration. Journal of Speech and Hearing Disorders 53: 2–7.CrossRefGoogle ScholarPubMed
MacWhinney, B. 2000. The CHILDES Project: Tools for Analyzing Talk. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
MacWhinney, B. 2008. Trends in corpus research: finding structure in data. In Behrens, H. (ed.), Enriching CHILDES for Morphosyntactic Analysis, pp. 165–198, Amsterdam: Benjamins.Google Scholar
Manning, C. D., and Schütze, H. 1999. Foundations of Statistical Natural Language Processing. The MIT Press.Google Scholar
McLaughlin, H. G. 1969. SMOG grading - a new readability formula. Journal of Reading 12 (8): 639–646.Google Scholar
Mayer, M. 1967. A Boy, a Dog and a Frog. Dial Press.Google Scholar
Mayer, M. 1969. Frog, Where are You? Dial Press.Google Scholar
Mayer, M. 1973. Frog on His Own. Dial Press.Google Scholar
Mayer, M. 1974. Frog Goes to Dinner. Dial Press.Google Scholar
Miller, J., and Iglesias, A. 2008. Systematic Analysis of Language Transcripts (SALT) Research Version 2008. Madison, WI: SALT Software LLC.Google Scholar
Paradis, J. 2005. Grammatical morphology in children learning English as a second language: implications of similarities with specific language impairment. Language, Speech and Hearing Services in Schools 36: 172–187.CrossRefGoogle Scholar
Paradis, J., Crago, M., and Genesee, F. 2003. French-English bilingual children with SLI: how do they compare with their monolingual peers? Journal of Speech, Language and Hearing Research 46: 113–127.CrossRefGoogle ScholarPubMed
Peña, E. D., Gutiérrez-Clellen, V., Iglesias, A., Goldstein, B. A., and Bedore, L. M.The Bilingual English-Spanish Assessment.Google Scholar
Petersen, S. E., and Ostendorf, M. 2008. A machine learning approach to reading level assessment. Computer Speech and Language 23: 89–106.CrossRefGoogle Scholar
Redmond, S. 2004. Conversational profiles of children with ADHD, SLI and typical development. Clinical Linguistics & Phonetics 18 (2): 107–125.CrossRefGoogle ScholarPubMed
Rice, M. L., and Wexler, K. 1996. A phenotype of specific language impairment: Extended optional infinitives. In Rice, M. L. (ed.), Toward a Genetics of Language, pp. 215–237, Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Restrepo, M. A. 1998. Identifiers of predominantly Spanish-speaking children with language impairment. Journal of Speech, Language and Hearing Research 41: 1398–1411.CrossRefGoogle ScholarPubMed
Roark, B., Bachrach, A., Cardenas, C., and Pallier, C. 2009. Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing. In The 2009 Conference on Empirical Methods for Natural Language Processing, pp. 324–333, Singapore, ACL.Google Scholar
Roark, B., Mitchell, M., and Hollingshead, K. 2007a. Syntactic complexity measures for detecting mild cognitive impairment. In BioNLP 2007: Biological, Translational and Clinical Language Processing, pp. 1–8, Prague.Google Scholar
Roark, B., Mitchell, M., and Kaye, J. A. 2007b. Automatically derived spoken language markers for detecting mild cognitive impairment. In Proceedings of the 2nd International Conference on Technology and Aging (ICTA), Toronto, Canada.Google Scholar
Sagae, K., Davis, E., Lavie, A., MacWhinney, B., and Wintner, S. 2010. Morphosyntactic annotation of CHILDES transcripts. Journal of Child Language 37 (3): 705–729.CrossRefGoogle ScholarPubMed
Sagae, K., Davis, E., Lavie, A., MacWhinney, B., and Wintner, S. 2007. High accuracy annotation and parsing of CHILDES transcripts. In Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition, pp. 25–32, Prague, Czech Republic.Google Scholar
Sagae, K., Lavie, A., and MacWhinney, B. 2005 Automatic measurement of syntactic development in child language. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pp. 197–204, Ann Arbor, Michigan, ACL.CrossRefGoogle Scholar
Scarborough, H. S. 1990. Index of productive syntax. Applied Psycholinguistics (11): 1–22.CrossRefGoogle Scholar
Solorio, T., and Liu, Y. 2008. Part-of-Speech tagging for English-Spanish code-switched text. In Empirical Methods on Natural Language Processing, EMNLP-2008, pp. 1051–1060. Honolulu, Hawaii, ACL.Google Scholar
Stolcke, A. 2002. SRILM - an extensible language modeling toolkit. In Proceedings of the International Conference on Spoken Language Processing. Denver, Colorado.Google Scholar
Thordardottir, E. T., and Weismer, S. E. 2002. Content mazes and filled pauses on narrative language samples of children with specific language impairment. Brain and Cognition 48 (2–3): 587–592.Google ScholarPubMed
Tomblin, J. B. 1997. Prevalence of specific language impairment in kindergarten children. Journal of Speech, Language and Hearing Research 40: 1245–1260.CrossRefGoogle ScholarPubMed
Wetherell, D., Botting, N., and Conti-Ramsden, G. 2007. Narrative in adolescent specific language impairment (SLI): a comparison with peers across two different narrative genres. International Journal of Language and Communication Disorders 42 (5): 583–605.CrossRefGoogle ScholarPubMed
Wexler, K. 1994. Optional infinitives, head movement and the economy of derivations. In Lightfoot, D. and Hornstein, N. (eds.), Verb Movement, pp. 305–350, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Witten, I. H., and Frank, E. 1999. Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA: Morgan Kaufmann.Google Scholar