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Volume 19, Issue 2
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Abstract

Abstract

Many English words contain historical roots that do not occur as free morphemes (e.g., in in ). These words often retain an appearance of compositionality and are associated with effects on lexical processing (Pastizzo & Feldman, 2004; Taft & Forster, 1975), but frequently their roots are difficult to identify without recourse to historical etymologies, and they are semantically opaque and unproductive. More practically, although such words are prominent in academic vocabulary, they are often difficult to learn, and instruction inspired by their apparent morphological structure has yielded mixed results (McKeown et al., 2018). We explore these psycholinguistic and educational challenges through a dynamic view of the mental lexicon (Libben, 2022), understanding morphological resources as gradient, emergent, and contextually adaptable for meaning making. We quantified bound roots’ morphological families by training an unsupervised parser on a lexicon approximating that of an educated English user, and then assessing polysemy and coherence of roots’ meanings, using vector semantic representations. Testing against behavioral data supported the validity of these measures, suggesting new ways of measuring the properties of bound roots independent from etymological data and demonstrating sensitivity even to unproductive morphological structure, that can support academic vocabulary development and meaning-making.

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2025-02-06
2025-12-09

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References

  1. Amenta, S., & Crepaldi, D.
    (2012) Morphological processing as we know it: An analytical review of morphological effects in visual word identification. Frontiers in Psychology, 31. 10.3389/fpsyg.2012.00232
     https://doi.org/10.3389/fpsyg.2012.00232 [Google Scholar]
  2. Amenta, S., Günther, F., & Marelli, M.
    (2020) A (distributional) semantic perspective on the processing of morphologically complex words. The Mental Lexicon, 15(1), 62–78. 10.1075/ml.00014.ame
     https://doi.org/10.1075/ml.00014.ame [Google Scholar]
  3. Archangeli, D., & Pulleyblank, D.
    (2016) Emergent morphology. InD. Siddiqi & H. Harley (Eds.), Linguistik Aktuell/Linguistics Today (Vol.2291, pp.237–270). John Benjamins Publishing Company. 10.1075/la.229.09arc
     https://doi.org/10.1075/la.229.09arc [Google Scholar]
  4. Baayen, R. H., Chuang, Y.-Y., Shafaei-Bajestan, E., & Blevins, J. P.
    (2019) The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discriminative Learning. Complexity, 20191, 1–39. 10.1155/2019/4895891
     https://doi.org/10.1155/2019/4895891 [Google Scholar]
  5. Baayen, R. H., & Schreuder, R.
    (Eds.) (2003) Morphological structure in language processing. Mouton de Gruyter. 10.1515/9783110910186
     https://doi.org/10.1515/9783110910186 [Google Scholar]
  6. Baayen, R. H., Wurm, L. H., & Aycock, J.
    (2007) Lexical dynamics for low-frequency complex words: A regression study across tasks and modalities. The Mental Lexicon, 2(3), 419–463. 10.1075/ml.2.3.06baa
     https://doi.org/10.1075/ml.2.3.06baa [Google Scholar]
  7. Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R.
    (2007) The English lexicon project. Behavior Research Methods, 39(3), 445. 10.3758/BF03193014
     https://doi.org/10.3758/BF03193014 [Google Scholar]
  8. Bar-Ilan, L., & Berman, R. A.
    (2007) Developing register differentiation: The Latinate-Germanic divide in English. Linguistics, 45(1), 1–35. 10.1515/LING.2007.001
     https://doi.org/10.1515/LING.2007.001 [Google Scholar]
  9. Baroni, M., Dinu, G., & Kruszewski, G.
    (2014) Don’t count, predict! A systematic comparison of context-counting vs. Context-predicting semantic vectors. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 238–247. 10.3115/v1/P14‑1023
     https://doi.org/10.3115/v1/P14-1023 [Google Scholar]
  10. Bates, D., Maechler, M., Bolker, B., & Walker, S.
    (2015) Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. 10.18637/jss.v067.i01
     https://doi.org/10.18637/jss.v067.i01 [Google Scholar]
  11. Bauer, L.
    (2001) Morphological productivity. Cambridge University Press. 10.1017/CBO9780511486210
     https://doi.org/10.1017/CBO9780511486210 [Google Scholar]
  12. Bertram, R., Schreuder, R., & Baayen, R. H.
    (2000) The balance of storage and computation in morphological processing: The role of word formation type, affixal homonymy, and productivity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(2), 489–511. 10.1037//0278‑7393.26.2.489
     https://doi.org/10.1037//0278-7393.26.2.489 [Google Scholar]
  13. Bowers, P. N., & Kirby, J. R.
    (2010) Effects of morphological instruction on vocabulary acquisition. Reading and Writing, 23(5), 515–537. 10.1007/s11145‑009‑9172‑z
     https://doi.org/10.1007/s11145-009-9172-z [Google Scholar]
  14. Brysbaert, M., Stevens, M., Mandera, P., & Keuleers, E.
    (2016) How many words do we know? Practical estimates of vocabulary size dependent on word definition, the degree of language input and the participant’s age. Frontiers in Psychology, 71, 1116. 10.3389/fpsyg.2016.01116
     https://doi.org/10.3389/fpsyg.2016.01116 [Google Scholar]
  15. Burani, C., & Thornton, A. M.
    (2003) The interplay of root, suffix, and whole-word frequency in processing derived words. InR. H. Baayen & R. Schreuder (Eds.), Morphological structure in language processing (pp.157–208). Mouton de Gruyter. 10.1515/9783110910186.157
     https://doi.org/10.1515/9783110910186.157 [Google Scholar]
  16. Bybee, J. L., & Hopper, P. J.
    (Eds.) (2001) Frequency and the emergence of linguistic structure. John Benjamins. 10.1075/tsl.45
     https://doi.org/10.1075/tsl.45 [Google Scholar]
  17. Cao, K., & Rei, M.
    (2016) A Joint Model for Word Embedding and Word Morphology (No. arXiv:1606.02601). arXiv. arxiv.org/abs/1606.02601. 10.18653/v1/W16‑1603
     https://doi.org/10.18653/v1/W16-1603 [Google Scholar]
  18. Carlisle, J. F.
    (2010) Effects of instruction in morphological awareness on literacy achievement: An integrative review. Reading Research Quarterly, 45(4), 464–487. 10.1598/RRQ.45.4.5
     https://doi.org/10.1598/RRQ.45.4.5 [Google Scholar]
  19. CCSO (Council Of Chief State School Officers)
    CCSO (Council Of Chief State School Officers) (2009) Common Core State Standards Initiative. United States; Library of Congress. https://www.loc.gov/item/lcwaN0010852/
     [Google Scholar]
  20. Chuang, Y.-Y., Vollmer, M. L., Shafaei-Bajestan, E., Gahl, S., Hendrix, P., & Baayen, R. H.
    (2021) The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning. Behavior Research Methods, 53(3), 945–976. 10.3758/s13428‑020‑01356‑w
     https://doi.org/10.3758/s13428-020-01356-w [Google Scholar]
  21. Colenbrander, D., Parsons, L., Bowers, J. S., & Davis, C. J.
    (2022) Assessing the effectiveness of Structured Word Inquiry for students in grades 3 and 5 with reading and spelling difficulties: A randomized controlled trial. Reading Research Quarterly, 57(1), 307–352. 10.1002/rrq.399
     https://doi.org/10.1002/rrq.399 [Google Scholar]
  22. Corson, D.
    (1997) The learning and use of academic English words. Language Learning, 47(4), 671–718. 10.1111/0023‑8333.00025
     https://doi.org/10.1111/0023-8333.00025 [Google Scholar]
  23. Coxhead, A.
    (2000) A New Academic Word List. TESOL Quarterly, 34(2), 213–238. 10.2307/3587951
     https://doi.org/10.2307/3587951 [Google Scholar]
  24. Creemers, A., & Embick, D.
    (2022) The role of semantic transparency in the processing of spoken compound words. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(5), 734–751. 10.1037/xlm0001132
     https://doi.org/10.1037/xlm0001132 [Google Scholar]
  25. Crosson, A. C., Kieffer, M. J., McKeown, M. G., & Nagy, W. E.
    (2025) Cross-language morphological analysis improves academic word learning for multilingual adolescents. Scientific Studies of Reading, 29(1), 55–84. 10.1080/10888438.2024.2415916
     https://doi.org/10.1080/10888438.2024.2415916 [Google Scholar]
  26. Crosson, A. C., Lei, P.-W., Cheng, W., & McKeown, M. G.
    (2020) The curious role of morphological family size in language minority learners’ problem solving of unfamiliar words. Scientific Studies of Reading, 24(6), 445–461. 10.1080/10888438.2019.1701475
     https://doi.org/10.1080/10888438.2019.1701475 [Google Scholar]
  27. Crosson, A. C., & McKeown, M. G.
    (2016) Middle school learners’ use of Latin roots to infer the meaning of unfamiliar words. Cognition and Instruction, 34(2), 148–171. 10.1080/07370008.2016.1145121
     https://doi.org/10.1080/07370008.2016.1145121 [Google Scholar]
  28. Crosson, A. C., McKeown, M. G., Lei, P., Zhao, H., Li, X., Patrick, K., Brown, K., & Shen, Y.
    (2021) Morphological analysis skill and academic vocabulary knowledge are malleable through intervention and may contribute to reading comprehension for multilingual adolescents. Journal of Research in Reading, 44(1), 154–174. 10.1111/1467‑9817.12323
     https://doi.org/10.1111/1467-9817.12323 [Google Scholar]
  29. Deacon, S. H., & Levesque, K.
    (2024) Mechanisms in the relation between morphological awareness and the development of reading comprehension. Journal of Educational Psychology, 116(6), 1052–1069. 10.1037/edu0000871
     https://doi.org/10.1037/edu0000871 [Google Scholar]
  30. Feldman, L. B., Soltano, E. G., Pastizzo, M. J., & Francis, S. E.
    (2004) What do graded effects of semantic transparency reveal about morphological processing?Brain and Language, 90(1–3), 17–30. 10.1016/S0093‑934X(03)00416‑4
     https://doi.org/10.1016/S0093-934X(03)00416-4 [Google Scholar]
  31. Gardner, D., & Davies, M.
    (2014) A New Academic Vocabulary List. Applied Linguistics, 35(3), 305–327. 10.1093/applin/amt015
     https://doi.org/10.1093/applin/amt015 [Google Scholar]
  32. Gonnerman, L. M., Seidenberg, M. S., & Andersen, E. S.
    (2007) Graded semantic and phonological similarity effects in priming: Evidence for a distributed connectionist approach to morphology. Journal of Experimental Psychology: General, 136(2), 323–345. 10.1037/0096‑3445.136.2.323
     https://doi.org/10.1037/0096-3445.136.2.323 [Google Scholar]
  33. Goodwin, A. P.
    (2016) Effectiveness of word solving: Integrating morphological problem-solving within comprehension instruction for middle school students. Reading and Writing, 29(1), 91–116. 10.1007/s11145‑015‑9581‑0
     https://doi.org/10.1007/s11145-015-9581-0 [Google Scholar]
  34. Hay, J.
    (2003) Causes and consequences of word structure. Routledge.
     [Google Scholar]
  35. Hay, J., & Baayen, R. H.
    (2003) Phonotactics, parsing, and productivity. Rivista Di Linguistica, 15(1), 99–130.
     [Google Scholar]
  36. (2005) Shifting paradigms: Gradient structure in morphology. Trends in Cognitive Sciences, 9(7), 342–348. 10.1016/j.tics.2005.04.002
     https://doi.org/10.1016/j.tics.2005.04.002 [Google Scholar]
  37. Jackendoff, R., & Audring, J.
    (2020) The Texture of the Lexicon: Relational Morphology and the Parallel Architecture. Oxford University Press.
     [Google Scholar]
  38. Jared, D., Jouravlev, O., & Joanisse, M. F.
    (2017) The effect of semantic transparency on the processing of morphologically derived words: Evidence from decision latencies and event-related potentials. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(3), 422–450. 10.1037/xlm0000316
     https://doi.org/10.1037/xlm0000316 [Google Scholar]
  39. Kearns, D. M., & Hiebert, E. H.
    (2022) The word complexity of primary-level texts: Differences between first and third grade in widely used curricula. Reading Research Quarterly, 57(1), 255–285. 10.1002/rrq.429
     https://doi.org/10.1002/rrq.429 [Google Scholar]
  40. Kuperman, V., Bertram, R., & Baayen, R. H.
    (2010) Processing trade-offs in the reading of Dutch derived words. Journal of Memory and Language, 62(2), 83–97. 10.1016/j.jml.2009.10.001
     https://doi.org/10.1016/j.jml.2009.10.001 [Google Scholar]
  41. Kuperman, V., Schreuder, R., Bertram, R., & Baayen, R. H.
    (2009) Reading polymorphemic Dutch compounds: Toward a multiple route model of lexical processing. Journal of Experimental Psychology: Human Perception & Performance, 35(3), 876–895. 10.1037/a0013484
     https://doi.org/10.1037/a0013484 [Google Scholar]
  42. Levesque, K. C., Breadmore, H. L., & Deacon, S. H.
    (2021) How morphology impacts reading and spelling: Advancing the role of morphology in models of literacy development. Journal of Research in Reading, 44(1), 10–26. 10.1111/1467‑9817.12313
     https://doi.org/10.1111/1467-9817.12313 [Google Scholar]
  43. Levesque, K. C., Kieffer, M. J., & Deacon, S. H.
    (2017) Morphological awareness and reading comprehension: Examining mediating factors. Journal of Experimental Child Psychology, 1601, 1–20. 10.1016/j.jecp.2017.02.015
     https://doi.org/10.1016/j.jecp.2017.02.015 [Google Scholar]
  44. Libben, G.
    (2006) Reading complex morphological structures. InS. Andrews (Ed.), From inkmarks to ideas: Current issues in lexical processing (pp.259–274). Psychology Press.
     [Google Scholar]
  45. (2014) The nature of compounds: A psychocentric perspective. Cognitive Neuropsychology, 31(1–2), 8–25. 10.1080/02643294.2013.874994
     https://doi.org/10.1080/02643294.2013.874994 [Google Scholar]
  46. (2022) From lexicon to flexicon: The principles of morphological transcendence and lexical superstates in the characterization of words in the mind. Frontiers in Artificial Intelligence, 41. https://www.frontiersin.org/article/10.3389/frai.2021.788430. 10.3389/frai.2021.788430
     https://doi.org/10.3389/frai.2021.788430 [Google Scholar]
  47. Marantz, A.
    (2013) No escape from morphemes in morphological processing. Language and Cognitive Processes, 28(7), 905–916. 10.1080/01690965.2013.779385
     https://doi.org/10.1080/01690965.2013.779385 [Google Scholar]
  48. Marelli, M., Amenta, S., & Crepaldi, D.
    (2015) Semantic transparency in free stems: The effect of orthography-semantics consistency on word recognition. Quarterly Journal of Experimental Psychology, 68(8), 1571–1583. 10.1080/17470218.2014.959709
     https://doi.org/10.1080/17470218.2014.959709 [Google Scholar]
  49. Marelli, M., & Baroni, M.
    (2015) Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics. Psychological Review, 122(3), 485–515. 10.1037/a0039267
     https://doi.org/10.1037/a0039267 [Google Scholar]
  50. McKeown, M. G., Crosson, A. C., Moore, D. W., & Beck, I. L.
    (2018) Word knowledge and comprehension effects of an academic vocabulary intervention for middle school students. American Educational Research Journal, 55(3), 572–616. 10.3102/0002831217744181
     https://doi.org/10.3102/0002831217744181 [Google Scholar]
  51. Nagy, W. E., & Anderson, R. C.
    (1984) How many words are there in printed school English?Reading Research Quarterly, 19(3), 304–330. 10.2307/747823
     https://doi.org/10.2307/747823 [Google Scholar]
  52. Narasimhan, K., Barzilay, R., & Jaakkola, T.
    (2015) An unsupervised method for uncovering morphological chains. Transactions of the Association for Computational Linguistics, 31, 157–167. 10.1162/tacl_a_00130
     https://doi.org/10.1162/tacl_a_00130 [Google Scholar]
  53. Oxford University Press
    Oxford University Press (1999) Oxford English Dictionary.
     [Google Scholar]
  54. Pacheco, M. B., & Goodwin, A. P.
    (2013) Putting two and two together: Middle school students’ morphological problem-solving strategies for unknown words. Journal of Adolescent & Adult Literacy, 56(7), 541–553. 10.1002/JAAL.181
     https://doi.org/10.1002/JAAL.181 [Google Scholar]
  55. Pastizzo, M. J., & Feldman, L. B.
    (2004) Morphological processing: A comparison between free and bound stem facilitation. Brain and Language, 90(1–3), 31–39. 10.1016/S0093‑934X(03)00417‑6
     https://doi.org/10.1016/S0093-934X(03)00417-6 [Google Scholar]
  56. Pollatsek, A., Slattery, T., & Juhasz, B.
    (2008) The processing of novel and lexicalised prefixed words in reading. Language and Cognitive Processes, 23(7), 1133–1158. 10.1080/01690960801945484
     https://doi.org/10.1080/01690960801945484 [Google Scholar]
  57. R Core Team
    R Core Team (2024) R: A Language and Environment for Statistical Computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
     [Google Scholar]
  58. Rastle, K., Davis, M. H. N., & New, B.
    (2004) The broth in my brother’s brothel: Morpho-orthographic segmentation in visual word recognition. Psychonomic Bulletin & Review, 11(6), 1090–1098. 10.3758/BF03196742
     https://doi.org/10.3758/BF03196742 [Google Scholar]
  59. Smit, P., Virpioja, S., Grönroos, S.-A., & Kurimo, M.
    (2014) Morfessor 2.0: Toolkit for statistical morphological segmentation. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, 21–24. 10.3115/v1/E14‑2006
     https://doi.org/10.3115/v1/E14-2006 [Google Scholar]
  60. Smolka, E., & Libben, G.
    (2017) ‘Can you wash off the hogwash?’ — semantic transparency of first and second constituents in the processing of German compounds. Language, Cognition and Neuroscience, 32(4), 514–531. 10.1080/23273798.2016.1256492
     https://doi.org/10.1080/23273798.2016.1256492 [Google Scholar]
  61. Snow, C. E., & Kim, Y.-S.
    (2007) Large problem spaces: The challenge of vocabulary for English language learners. InVocabulary acquisition: Implications for reading comprehension (pp.123–139). The Guilford Press.
     [Google Scholar]
  62. Taft, M.
    (1994) Interactive-activation as a framework for understanding morphological processing. Language and Cognitive Processes, 9(3), 271–294. 10.1080/01690969408402120
     https://doi.org/10.1080/01690969408402120 [Google Scholar]
  63. (2003) Morphological representation as a correlation between form and meaning. InE. M. H. Assink & D. Sandra (Eds.), Reading complex words (pp.113–137). Kluwer. 10.1007/978‑1‑4757‑3720‑2_6
     https://doi.org/10.1007/978-1-4757-3720-2_6 [Google Scholar]
  64. (2004) Morphological decomposition and the reverse base frequency effect. The Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 57(4), 745. 10.1080/02724980343000477
     https://doi.org/10.1080/02724980343000477 [Google Scholar]
  65. (2023) Localist lexical representation of polymorphemic words. InD. Crepaldi (Ed.), Linguistic Morphology in the Mind and Brain (1st ed., pp.152–166). Routledge. 10.4324/9781003159759‑11
     https://doi.org/10.4324/9781003159759-11 [Google Scholar]
  66. Taft, M., & Forster, K. I.
    (1975) Lexical storage and retrieval of prefixed words. Journal of Verbal Learning and Verbal Behavior, 14(6), 638–647. 10.1016/S0022‑5371(75)80051‑X
     https://doi.org/10.1016/S0022-5371(75)80051-X [Google Scholar]
  67. (1976) Lexical storage and retrieval of polymorphemic and polysyllabic words. Journal of Verbal Learning and Verbal Behavior, 15(6), 607–620. 10.1016/0022‑5371(76)90054‑2
     https://doi.org/10.1016/0022-5371(76)90054-2 [Google Scholar]
  68. Tomasello, M.
    (2003) Constructing a language: A usage-based theory of language acquisition. Harvard University Press.
     [Google Scholar]
  69. Uccelli, P.
    (2023) The language demands of analytical reading and writing at school. Written Communication, 40(2), 518–554. 10.1177/07410883221148727
     https://doi.org/10.1177/07410883221148727 [Google Scholar]
  70. Vaughn, S., Gersten, R., Dimino, J., Jayanthi, M., Taylor, M. J., Newman-Gonchar, R., Krowka, S., Kieffer, M. J., McKeown, M. G., Reed, D., Sanchez, M., St. Martin, K., Wexler, J., Morgan, S., Yañez, A., & Jayanthi, M.
    (2022) Providing reading interventions for students in grades 4–9. Wexler, J., Morgan, S., Yañez, A., & Jayanthi, M. InProviding Reading Interventions for Students in Grades 4–9 (WWC 2022007). National Center for Education Evaluation and Regional Assistance (NCEE), Institute of Education Sciences, U.S. Department of Education. https://whatworks.ed.gov/
     [Google Scholar]
  71. Virpioja, S., Lehtonen, M., Hultén, A., Kivikari, H., Salmelin, R., & Lagus, K.
    (2018) Using statistical models of morphology in the search for optimal units of representation in the human mental lexicon. Cognitive Science, 42(3), 939–973. 10.1111/cogs.12576
     https://doi.org/10.1111/cogs.12576 [Google Scholar]
  72. Westbury, C. F., & Hollis, G.
    (2007) Putting Humpty together again: Synthetic approaches to nonlinear variable effects underlying lexical access. InG. Jarema & G. Libben (Eds.), The mental lexicon: Core perspectives (pp.7–30). Elsevier. 10.1163/9780080548692_003
     https://doi.org/10.1163/9780080548692_003 [Google Scholar]
  73. Xu, H., Kodner, J., Marcus, M., & Yang, C.
    (2020) Modeling Morphological Typology for Unsupervised Learning of Language Morphology. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6672–6681. 10.18653/v1/2020.acl‑main.596
     https://doi.org/10.18653/v1/2020.acl-main.596 [Google Scholar]
  74. Xu, J., & Taft, M.
    (2014) The Effects of Semantic Transparency and Base Frequency on the Recognition of English Complex Words. Journal of Experimental Psychology: Learning, Memory, and Cognition. 10.1037/xlm0000052
     https://doi.org/10.1037/xlm0000052 [Google Scholar]
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The synchronic status of historical bound roots in the mental lexicon
The Mental Lexicon 19, 224 (2024); https://doi.org/10.1075/ml.24032.car
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  • Article Type: Research Article
Keyword(s): academic vocabulary; bound roots; lexical semantics; unsupervised parsing

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