BALANCE OF MEANING PROBABILITIES IN PROCESSING OF SERBIAN HOMONYMY
DOI:
https://doi.org/10.19090/pp.2019.3.283-304Keywords:
entropy, homonymy, number of meanings, redundancy, visual lexical decision taskAbstract
The research deals with the set of Serbian homonymous nouns (nouns with multiple unrelated meanings) presented in the norming study and in the visual lexical decision task experiment. Native speakers listed the meanings of homonymous words and provided word familiarity and word concreteness ratings. Accordingly, the first database of Serbian homonyms was constructed containing subjective meanings of homonymous nouns along with the estimated meaning probabilities, as well as a number of meanings, redundancy and entropy of the distribution of meaning probabilities, word familiarity and word concreteness. The processing disadvantage of homonymous nouns over unambiguous nouns was replicated in the visual lexical decision task. Additionally, the processing of homonymous nouns was linked with redundancy: the information theory measure of the balance of meaning probabilities. The results revealed that homonyms with higher redundancy of the meaning probability distribution (i.e., unbalanced meaning probabilities) were processed faster. This finding was in accordance with the hypothesis derived from the Semantic Settling Dynamics account of the processing of ambiguous words, according to which the competition among the unrelated meanings derived the processing disadvantage in homonymy. However, the same pattern was not observed for the number of meanings and entropy, inviting for further research of the processing of ambiguous words.
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References
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Armstrong, B. C., & Plaut, D. C. (2016). Disparate semantic ambiguity effects from semantic processing dynamics rather than qualitative task differences. Language, Cognition, and Neuroscience, 1(7), 1–27. doi:10.1080/23273798.201 6.1171366
Armstrong, B. C., Tokowicz, N., & Plaut, D. C. (2012). eDom: Norming software and relative meaning frequencies for 544 English homonyms. Behavior Research Methods, 44, 1015–1027. doi:10.3758/s13428-012-0199-8
Azuma, T. (1996). Familiarity and relatedness of word meanings: Ratings for 110 homographs. Behavior Research Methods, Instruments, and Computers, 28(1), 109–124. doi:10.3758/BF03203645
Azuma, T., & Van Orden, G. C. (1997). Why SAFE Is Better Than FAST: The Relatedness of a Word’s Meanings Affects Lexical Decision Times. Journal of Memory and Language, 36(4), 484–504. doi:10.1006/jmla.1997.2502
Baayen, R. H., & Milin, P. (2010). Analyzing reaction times. International Journal of Psychological Research, 32, 12–28. doi:10.21500/20112084.807
Balota, D. A., Pilotti, M., & Cortese, M. J. (2001). Subjective frequency estimates for 2,938 monosyllabic words. Memory and Cognition, 29, 639–647. doi:10.3758/ BF03200465
Borowsky, R., & Masson, M. E. J. (1996). Semantic ambiguity effects in word identification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(1), 63–85. doi:10.1037//0278-7393.22.1.63
Clark, H. H. (1973). The Language-as-Fixed-Effect-Fallacy: A Critique of Language Statistics in Psychological Research. Journal of Verbal Learning and Verbal Behavior, 12, 335–359. doi:10.1016/S0022-5371(73)80014-3
Coltheart, M., Davelaar, E., Jonasson, J. T., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic (Ed.), Attention and performance VI (pp. 535–555). NJ: Erlbaum.
Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: John Wiley & Sons. doi:10.1002/0471200611
Eddington, C. M., & Tokowicz, N. (2015). How meaning similarity influences ambiguous word processing: the current state of the literature. Psychonomic Bulletin & Review, 22(1), 13–37. doi:10.3758/s13423-014-0665-7
Ferraro, F. R., & Kellas, G. (1990). Normative data for number of word meanings. Behavior Research Methods, Instruments, and Computers, 22(6), 491–498. doi:10.3758/BF03204432
Filipović Đurđević, D. (2007). Polysemy effect in processing of Serbian nouns (Unpublished doctoral dissertation). University of Belgrade, Serbia.
Filipović Đurđević, D. (2018). Naïve discrimination learning approach to polysemy. Proceedings of the XXIV Scientific Conference Empirical Studies in Psychology. March 23-25, 2018, Faculty of Philosophy, University of Belgrade, Institute of Psychology, Laboratory for Experimental Psychology, Faculty of Philosophy, University of Belgrade, 63–64.
Filipović Ðurđević, D., Ðurđević, Đ., & Kostić, A. (2009). Vector based semantic analysis reveals absence of competition among related senses. Psihologija, 42, 95–106. doi:10.2298/PSI0901095F
Filipović Đurđević, D., & Kostić, A. (2008). The effect of polysemy on processing of Serbian nouns. Psihologija, 41(1), 69–86. doi:10.2298/PSI0801059F
Filipović Đurđević, D., & Kostić, A. (2017). Number, Relative Frequency, Entropy, Redundancy, Familiarity, and Concreteness of Word Senses: Ratings for 150 Serbian Polysemous Nouns. In S. Halupka-Rešetar & S. Martínez-Ferreiro (Eds.) Studies in Language and Mind 2 (pp. 13–77). Novi Sad: Filozofski fakultet.
Filipović Đurđević, D., & Kostić, A. We probably sense sense probabilities. Under review.
Gawlick-Grendell, L. A., & Woltz, D. J. (1994). Meaning dominance norms for 120 homographs. Behavior Research Methods, Instruments, and Computers, 26(1), 5–25. doi:10.3758/BF03204557
Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. doi:10.1017/ CBO9780511790942
Gernsbacher, M. A. (1984). Resolving 20 years of inconsistent interactions between lexical familiarity and orthography, concreteness, and polysemy. Journal of Experimental Psychology: General, 113, 256–281. doi:10.1037/0096-3445.113.2.256
Gilhooly, K. J., & Logie, R. H. (1980). Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words. Behavior Research Methods & Instrumentation, 12(4), 395–427. doi:10.3758/BF03201693
Hino, Y., & Lupker, S. J. (1996). Effects of polysemy in lexical decision and naming: An alternative to lexical access accounts. Journal of Experimental Psychology: Human Perception and Performance, 22(6), 1331–1356. doi:10.1037//0096-1523.22.6.1331
Jastrzembski, J. E. (1981). Multiple meanings, number of related meanings, frequency of occurrence, and the lexicon. Cognitive Psychology, 13(2), 278–305. doi:10.1016/0010-0285(81)90011-6
Kellas, G., Ferraro, R. R., & Simpson, G. B. (1988). Lexical Ambiguity and the Timecourse of Attentional Allocation In Word Recognition. Journal of Experimental Psychology: Human Perception and Performance, 14(4), 601–609. doi:10.1037//0096-1523.14.4.601
Kostić, Đ. (1999). Frekvencijski rečnik savremenog srpskog jezika [Frequency Dictionary of Contemporary Serbian Language]. Beograd: Institut za eksperimentalnu fonetiku i patologiju govora i Laboratorija za eksperimentalnu psihologiju.
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314–324. doi:10.3758/s13428-011-0168-7
Medeiros, J., & Armstrong, B. C. (2017). Semantic Ambiguity Effects: A Matter of Time? In Proceedings of the 39th Annual Conference of the Cognitive Science Society, pp. 2693–2698. Mahwah, N: Lawrence Erlbaum Associates.
Millis, M. L., & Bution, S. B. (1989). The effect of polysemy on lexical decision time: Now you see it, now you don’t. Memory & Cognition, 17(2), 141–147. doi:10.3758/BF03197064
Paivio, A., Yuille, J. C., & Madigan, S. A. (1968). Concreteness, imagery, and meaningfulness values for 925 nouns. Journal of Experimental Psychology, 76(1p2), 1–25. doi:10.1037/h0025327
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/
Rice, C., Beekhuizen, B., Dubrovsky, V., Stevenson, S., & Armstrong, B. C. (2018). A comparison of homonym meaning frequency estimates derived from movie and television subtitles, free association, and explicit ratings. Behavior Research Methods, 50, 1399–1425. doi:10.3758/s13428-018-1107-7
RMS – Rečnik Matice Srpske. (1967 – 1976). Rečnik srpskohrvatskoga književnog jezika, t. I-VI [Dictionary of Serbo-Croatian literary language]. Novi Sad: Matica Srpska.
Rodd, J. M., Gaskell, M. G., & Marslen-Wilson, W. D. (2002). Making sense of semantic ambiguity: Semantic competition in lexical access. Journal of Memory and Language, 46, 245–266. doi:10.1006/jmla.2001.2810
Rodd, J. M., Gaskell, M. G., & Marslen-Wilson, W. D. (2004). Modelling the effects of semantic ambiguity in word recognition. Cognitive Science, 28, 89–104. doi:10.1207/s15516709cog2801_4
Rubenstein, H., Garfield, L., & Millikan, J. A. (1970). Homographic entries in the internal lexicon. Journal of Verbal Learning and Verbal Behavior, 9(5), 487–494. doi:10.1016/S0022-5371(70)80091-3
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. doi:10.1002/j.1538-7305.1948.tb01338.x
Twilley, L. C., Dixon, P., Taylor, D., & Clark, K. (1994). University of Alberta norms of relative meaning frequency for 566 homographs. Memory and Cognition, 22(1), 111–126. doi:10.3758/BF03202766
Van Rij, J., Wieling, M., Baayen, R., & van Rijn, H. (2017). “itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs”. R package version 2.3.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag. doi:10.1007/978-3-319-24277-4_9
Wood, S. N. (2006). Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC. MY, Florida: Boca Raton. doi:10.1201/9781420010404
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society B, 731, 3–36. doi:10.1111/j.1467-9868.2010.00749.x
Armstrong, B. C., & Plaut, D. C. (2016). Disparate semantic ambiguity effects from semantic processing dynamics rather than qualitative task differences. Language, Cognition, and Neuroscience, 1(7), 1–27. doi:10.1080/23273798.201 6.1171366
Armstrong, B. C., Tokowicz, N., & Plaut, D. C. (2012). eDom: Norming software and relative meaning frequencies for 544 English homonyms. Behavior Research Methods, 44, 1015–1027. doi:10.3758/s13428-012-0199-8
Azuma, T. (1996). Familiarity and relatedness of word meanings: Ratings for 110 homographs. Behavior Research Methods, Instruments, and Computers, 28(1), 109–124. doi:10.3758/BF03203645
Azuma, T., & Van Orden, G. C. (1997). Why SAFE Is Better Than FAST: The Relatedness of a Word’s Meanings Affects Lexical Decision Times. Journal of Memory and Language, 36(4), 484–504. doi:10.1006/jmla.1997.2502
Baayen, R. H., & Milin, P. (2010). Analyzing reaction times. International Journal of Psychological Research, 32, 12–28. doi:10.21500/20112084.807
Balota, D. A., Pilotti, M., & Cortese, M. J. (2001). Subjective frequency estimates for 2,938 monosyllabic words. Memory and Cognition, 29, 639–647. doi:10.3758/ BF03200465
Borowsky, R., & Masson, M. E. J. (1996). Semantic ambiguity effects in word identification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(1), 63–85. doi:10.1037//0278-7393.22.1.63
Clark, H. H. (1973). The Language-as-Fixed-Effect-Fallacy: A Critique of Language Statistics in Psychological Research. Journal of Verbal Learning and Verbal Behavior, 12, 335–359. doi:10.1016/S0022-5371(73)80014-3
Coltheart, M., Davelaar, E., Jonasson, J. T., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic (Ed.), Attention and performance VI (pp. 535–555). NJ: Erlbaum.
Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: John Wiley & Sons. doi:10.1002/0471200611
Eddington, C. M., & Tokowicz, N. (2015). How meaning similarity influences ambiguous word processing: the current state of the literature. Psychonomic Bulletin & Review, 22(1), 13–37. doi:10.3758/s13423-014-0665-7
Ferraro, F. R., & Kellas, G. (1990). Normative data for number of word meanings. Behavior Research Methods, Instruments, and Computers, 22(6), 491–498. doi:10.3758/BF03204432
Filipović Đurđević, D. (2007). Polysemy effect in processing of Serbian nouns (Unpublished doctoral dissertation). University of Belgrade, Serbia.
Filipović Đurđević, D. (2018). Naïve discrimination learning approach to polysemy. Proceedings of the XXIV Scientific Conference Empirical Studies in Psychology. March 23-25, 2018, Faculty of Philosophy, University of Belgrade, Institute of Psychology, Laboratory for Experimental Psychology, Faculty of Philosophy, University of Belgrade, 63–64.
Filipović Ðurđević, D., Ðurđević, Đ., & Kostić, A. (2009). Vector based semantic analysis reveals absence of competition among related senses. Psihologija, 42, 95–106. doi:10.2298/PSI0901095F
Filipović Đurđević, D., & Kostić, A. (2008). The effect of polysemy on processing of Serbian nouns. Psihologija, 41(1), 69–86. doi:10.2298/PSI0801059F
Filipović Đurđević, D., & Kostić, A. (2017). Number, Relative Frequency, Entropy, Redundancy, Familiarity, and Concreteness of Word Senses: Ratings for 150 Serbian Polysemous Nouns. In S. Halupka-Rešetar & S. Martínez-Ferreiro (Eds.) Studies in Language and Mind 2 (pp. 13–77). Novi Sad: Filozofski fakultet.
Filipović Đurđević, D., & Kostić, A. We probably sense sense probabilities. Under review.
Gawlick-Grendell, L. A., & Woltz, D. J. (1994). Meaning dominance norms for 120 homographs. Behavior Research Methods, Instruments, and Computers, 26(1), 5–25. doi:10.3758/BF03204557
Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. doi:10.1017/ CBO9780511790942
Gernsbacher, M. A. (1984). Resolving 20 years of inconsistent interactions between lexical familiarity and orthography, concreteness, and polysemy. Journal of Experimental Psychology: General, 113, 256–281. doi:10.1037/0096-3445.113.2.256
Gilhooly, K. J., & Logie, R. H. (1980). Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words. Behavior Research Methods & Instrumentation, 12(4), 395–427. doi:10.3758/BF03201693
Hino, Y., & Lupker, S. J. (1996). Effects of polysemy in lexical decision and naming: An alternative to lexical access accounts. Journal of Experimental Psychology: Human Perception and Performance, 22(6), 1331–1356. doi:10.1037//0096-1523.22.6.1331
Jastrzembski, J. E. (1981). Multiple meanings, number of related meanings, frequency of occurrence, and the lexicon. Cognitive Psychology, 13(2), 278–305. doi:10.1016/0010-0285(81)90011-6
Kellas, G., Ferraro, R. R., & Simpson, G. B. (1988). Lexical Ambiguity and the Timecourse of Attentional Allocation In Word Recognition. Journal of Experimental Psychology: Human Perception and Performance, 14(4), 601–609. doi:10.1037//0096-1523.14.4.601
Kostić, Đ. (1999). Frekvencijski rečnik savremenog srpskog jezika [Frequency Dictionary of Contemporary Serbian Language]. Beograd: Institut za eksperimentalnu fonetiku i patologiju govora i Laboratorija za eksperimentalnu psihologiju.
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314–324. doi:10.3758/s13428-011-0168-7
Medeiros, J., & Armstrong, B. C. (2017). Semantic Ambiguity Effects: A Matter of Time? In Proceedings of the 39th Annual Conference of the Cognitive Science Society, pp. 2693–2698. Mahwah, N: Lawrence Erlbaum Associates.
Millis, M. L., & Bution, S. B. (1989). The effect of polysemy on lexical decision time: Now you see it, now you don’t. Memory & Cognition, 17(2), 141–147. doi:10.3758/BF03197064
Paivio, A., Yuille, J. C., & Madigan, S. A. (1968). Concreteness, imagery, and meaningfulness values for 925 nouns. Journal of Experimental Psychology, 76(1p2), 1–25. doi:10.1037/h0025327
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/
Rice, C., Beekhuizen, B., Dubrovsky, V., Stevenson, S., & Armstrong, B. C. (2018). A comparison of homonym meaning frequency estimates derived from movie and television subtitles, free association, and explicit ratings. Behavior Research Methods, 50, 1399–1425. doi:10.3758/s13428-018-1107-7
RMS – Rečnik Matice Srpske. (1967 – 1976). Rečnik srpskohrvatskoga književnog jezika, t. I-VI [Dictionary of Serbo-Croatian literary language]. Novi Sad: Matica Srpska.
Rodd, J. M., Gaskell, M. G., & Marslen-Wilson, W. D. (2002). Making sense of semantic ambiguity: Semantic competition in lexical access. Journal of Memory and Language, 46, 245–266. doi:10.1006/jmla.2001.2810
Rodd, J. M., Gaskell, M. G., & Marslen-Wilson, W. D. (2004). Modelling the effects of semantic ambiguity in word recognition. Cognitive Science, 28, 89–104. doi:10.1207/s15516709cog2801_4
Rubenstein, H., Garfield, L., & Millikan, J. A. (1970). Homographic entries in the internal lexicon. Journal of Verbal Learning and Verbal Behavior, 9(5), 487–494. doi:10.1016/S0022-5371(70)80091-3
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. doi:10.1002/j.1538-7305.1948.tb01338.x
Twilley, L. C., Dixon, P., Taylor, D., & Clark, K. (1994). University of Alberta norms of relative meaning frequency for 566 homographs. Memory and Cognition, 22(1), 111–126. doi:10.3758/BF03202766
Van Rij, J., Wieling, M., Baayen, R., & van Rijn, H. (2017). “itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs”. R package version 2.3.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag. doi:10.1007/978-3-319-24277-4_9
Wood, S. N. (2006). Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC. MY, Florida: Boca Raton. doi:10.1201/9781420010404
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society B, 731, 3–36. doi:10.1111/j.1467-9868.2010.00749.x
