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Books on the topic 'Language acquisition – Mathematical models'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Berwick, Robert C. The acquisition of syntactic knowledge. Cambridge, Mass: MIT Press, 1985.

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2

Titone, Renzo. Theoretical models and research methods in the study of second-language acquisition. Toronto: Centro Canadese Scuola e Cultura Italiana, 1988.

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3

Dakowska, Maria. Models of language use and language learning in the theory of language didactics. Warszawa: Wydawnictwa Uniwersytetu Warszawskiego, 1995.

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4

Models of language use and language learning in the theory of language didactics. Frankfurt am Main: Peter Lang, 1996.

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5

Complexity, language, and life: Mathematical approaches. Berlin: Springer-Verlag, 1986.

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6

Dang, Tri Vi. Essays on information acquisition. Mannheim: Universität Mannheim?, 2005.

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7

Perspectives on formulaic language: Acquisition and communication. London: Continuum, 2010.

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8

Stevenson, Rosemary J. Models of language development. Milton Keynes [England]: Open University Press, 1988.

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9

Kotlikoff, Laurence J. On the general relativity of fiscal language. Cambridge, Mass: National Bureau of Economic Research, 2006.

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10

Fomichov, Vladimir A. Semantics-oriented natural language processing: Mathematical models and algorithms. New York: Springer, 2010.

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11

Semantics-oriented natural language processing: Mathematical models and algorithms. New York: Springer, 2010.

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12

1921-, Ferguson Charles Albert, Menn Lise, and Stoel-Gammon Carol, eds. Phonological development: Models, research, implications. Timonium, Md: York Press, 1992.

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13

Carroll, Susanne. Input and evidence: The raw material of second language acquisition. Amsterdam: J. Benjamins, 2001.

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14

M, Ford Kenneth, and Bradshaw Jeffrey M, eds. Knowledge acquisition as modeling. New York: Wiley, 1993.

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15

Andersen, Torben M. Imperfect competition, information acquisition, and price adjustment. Aarhus, Denmark: Institute of Economics, University of Aarhus, 1989.

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16

Ludo, Verhoeven, and Jong, John H. A. L. de., eds. The Construct of language proficiency: Applications of psychological models to language assessment. Amsterdam: John Benjamins, 1992.

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17

Schütze, Hinrich. Ambiguity resolution in language learning: Computational and cognitive models. Stanford, Calif: CSLI Publications, 1997.

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18

Language, mind and computation. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2014.

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19

Two-level representations for natural language. Bloomington, Ind: Indiana University Linguistics Club, 1985.

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20

Krulee, Gilbert K. Two-level representations for natural language. Bloomington: Indiana University Linguistics Club, 1985.

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21

Giovanni, Felici, and Vercellis Carlo, eds. Mathematical methods for knowledge discovery and data mining. Hershey, PA: Information Science Reference, 2007.

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22

Rumi︠a︡nt︠s︡ev, M. K. Mashinnoe modelirovanie edinit︠s︡ rechi. Moskva: Izd-vo Moskovskogo universiteta, 1990.

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23

Mustajīr, Aḥmad. Madkhal riyāḍī ilá ʻarūḍ al-shiʻr al-ʻArabī. [Cairo]: A. Mustajīr, 1987.

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24

Kapoor, Sant Kumar. Vedic mathematical basis of English language (of orthodox and classical formats). New Delhi: M/s Paramount Knowledge Pvt. Ltd., 2013.

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25

Th, Verhoeven Ludo, and Jong, John H. A. L. de, 1947-, eds. The Construct of language proficiency: Applications of psychological models to language assessment. Amsterdam: John Benjamins Pub. Co., 1992.

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26

Angrist, Joshua David. Is Spanish-only schooling responsible for the Puerto Rican language gap? Cambridge, Mass: National Bureau of Economic Research, 2006.

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27

The elements of mathematical semantics. Berlin: Mouton de Gruyter, 1992.

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28

Tolpegin, P. V. Novye metody i algoritmy avtomaticheskogo razreshenii︠a︡ referent︠s︡ii mestoimeniĭ tretʹego lit︠s︡a russkoi︠a︡zychnykh tekstov. Moskva: URSS, 2006.

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29

Paolillo, John C. Analyzing linguistic variation: Statistical models and methods. Stanford, CA: Center for the Study of Language and Information, 2002.

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30

Jeffrey, Conklin E., and Hill Jane Anne Collins, eds. From schema theory to language. New York: Oxford University Press, 1987.

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31

MaryEllen, Vogt, and Short Deborah, eds. The SIOP model for teaching mathematics to English learners. Boston: Allyn & Bacon, 2010.

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32

Echevarria, Jana. The SIOP model for teaching mathematics to English learners. Boston: Allyn & Bacon, 2010.

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33

W. P. A. van Deursen. Geographical information systems and dynamic models: Development and application of a prototype spatial modelling language. Utrecht: Koninklijk Nederlands Aardrijkskundig Genntschap/Faculteit Ruimtelijke Wetenschappen Universiteit Utrecht, 1995.

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34

Ekshembeeva, L. V. I͡A︡zykovye moduli i problemy obuchenii͡a︡. Almaty: Qazaq universitetī, 2000.

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35

Skrzypczak, Waldemar. Analog-based modelling of meaning representations in English. Toruń: Nicolaus Copernicus University Press, 2006.

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36

O'Donnell, Timothy J. Productivity and reuse in language: A theory of linguistic computation and storage. Cambridge, Massachusetts: The MIT Press, 2015.

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37

Lopatnikov, L. I. Ėkonomiko-matematicheskiĭ slovarʹ. Moskva: Nauka, 1987.

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38

Ėkonomiko-matematicheskiĭ slovarʹ. 3rd ed. Moskva: VO "Nauka", 1993.

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39

Lopatnikov, L. I. Ėkonomiko-matematicheskiĭ slovarʹ: Slovarʹ sovremennoĭ ėkonomicheskoĭ nauki. 5th ed. Moskva: Delo, 2003.

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40

Lopatnikov, L. I. Ėkonomiko-matematicheskiĭ slovarʹ. Moskva: "Nauka", 1987.

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41

Lopatnikov, L. I. Ėkonomiko-matematicheskiĭ slovarʹ: Slovarʹ sovremennoĭ ėkonomicheskoĭ nauki. 4th ed. Moskva: ABF, 1996.

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42

1941-, Chambers John M., and Hastie Trevor, eds. Statistical models in S. New York: Champan & Hall, 1993.

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43

Berwick, Robert C. Acquisition of Syntactic Knowledge. MIT Press, 2018.

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44

Sanchez, Ricardo, Michael E. McMahon, James G. Kallimani, Lauren A. Mayer, and Craig A. Bond. Developing a Methodology for Risk-Informed Trade-Space Analysis in Acquisition. RAND Corporation, 2015.

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45

Developing a Methodology for Risk-Informed Trade-Space Analysis in Acquisition. RAND Corporation, 2015.

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46

Samuelsson, Christer. Statistical Methods. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0019.

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Abstract:

Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.

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47

Levinson, Stephen. Mathematical Models of Spoken Language. Wiley, 2003.

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48

Poibeau, Thierry, and Aline Villavicencio. Language, Cognition, and Computational Models. Cambridge University Press, 2018.

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49

Poibeau, Thierry, and Aline Villavicencio. Language, Cognition, and Computational Models. Cambridge University Press, 2018.

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50

Poibeau, Thierry, and Aline Villavicencio. Language, Cognition, and Computational Models. University of Cambridge ESOL Examinations, 2017.

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