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arxiv: 2605.16654 · v1 · pith:EYDZ5DKOnew · submitted 2026-05-15 · 💻 cs.CL · cs.AI

A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research

Divyesh Pratap Singh , Dakshesh Gusain , Federica Bulgarelli , Alison Eisel Hendricks , John Beavers , Nathan M. Beers , Ifeoma Nwogu This is my paper

Pith reviewed 2026-05-20 17:47 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords manner verbsresult verbsverb classificationRoBERTalarge language modelsevent structuredevelopmental languageVerbNet
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The pith

A RoBERTa classifier trained on large language model annotations identifies manner and result verbs in sentences with up to 89.6 percent accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a computational method to automatically distinguish manner verbs, which describe how an action occurs, from result verbs, which emphasize the outcome, in sentence context. This distinction matters for research on early verb learning in children but has been limited by the lack of large annotated datasets. The authors use large language models with targeted prompts to annotate sentences from existing corpora, extending coverage to 436 verb classes, then train a RoBERTa classifier on those labels. The model reaches up to 89.6% accuracy on held-out test sets, offering a scalable tool for future studies in developmental language research.

Core claim

Using linguistically informed prompts, large language models generate sentence-level annotations for manner and result verbs over data from MASC and InterCorp, extending coverage to 436 VerbNet classes. A RoBERTa-based classifier trained on these annotations achieves average accuracy up to 89.6% on three held-out gold-standard datasets.

What carries the argument

Linguistically informed prompting of large language models to produce sentence-level manner/result annotations, followed by training a RoBERTa classifier on the resulting labels.

If this is right

  • The method extends reliable verb classification to 436 VerbNet classes from previously smaller annotated sets.
  • The classifier can now be run on developmental language datasets to measure manner and result verb use without new manual labeling.
  • Performance generalizes across previously annotated items and a fresh expert-annotated test set.
  • The tool supports broader research on verb semantics in child language acquisition and other domains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Applying the classifier to child-directed speech corpora could quantify whether children learn manner verbs before result verbs or vice versa at population scale.
  • The prompting technique might transfer to other verb semantic distinctions if similar linguistic guidelines are written for each new category.
  • Downstream studies could test whether manner/result ratios in input speech predict children's verb production patterns.

Load-bearing premise

The sentence-level annotations produced by large language models via linguistically informed prompts are accurate and consistent enough to serve as reliable training data for the downstream RoBERTa classifier.

What would settle it

Evaluating the trained RoBERTa classifier on a new large expert-annotated dataset of sentences and finding accuracy well below 89.6% would show that the LLM-generated labels do not provide reliable training data.

Figures

Figures reproduced from arXiv: 2605.16654 by Alison Eisel Hendricks, Dakshesh Gusain, Divyesh Pratap Singh, Federica Bulgarelli, Ifeoma Nwogu, John Beavers, Nathan M. Beers.

Figure 1
Figure 1. Figure 1: Hierarchy of verb classification, with manner [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our data generation pipeline. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Result Manner Verb Definition Verb Root Classification Definition: The verb has to be classified based on its primary lexical meaning and the inherent information that the verb independently en￾codes irrespective of the context. Example: “Wipe” Sentence: “He wiped the table clean.” The verb wipe primarily indicates the manner of cleaning; the resulting state (“clean”) is introduced by the adjective clean, … view at source ↗
Figure 4
Figure 4. Figure 4: Verb Root Classification 4.2 Approaching the problem as part-of-speech (POS) tagging Since our task involves both verb classification and detection them in a sentence, we adopt a sequence￾tagging approach, similar to part-of-speech (POS) tagging, rather than formulating it as a binary classi￾fication task. This enables us to identify non-stative verbs, since modal and auxiliary verbs are readily identifiab… view at source ↗
Figure 5
Figure 5. Figure 5: Overview of model architecture Acc. F1 Precision Recall F1 Precision Recall (result) (result) (result) (manner) (manner) (manner) Model 1 (Trained using Prompt 1) Linguistic dataset 0.94 0.93 0.89 0.97 0.95 0.98 0.92 Psycholinguistic dataset 0.90 0.88 1.00 0.78 0.91 0.84 1.00 Expert-annotated dataset 0.86 0.85 0.84 0.85 0.88 0.89 0.87 Model 2 (Trained using Prompt 2) Linguistic dataset 0.94 0.93 0.91 0.94 … view at source ↗
Figure 6
Figure 6. Figure 6: Guidelines for Identifying Manner and Result [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Annotation Screen for Expert Human Annota [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Sample Annotation Screen. Manner Verbs Definition: These verbs encode the *how* of an action, focusing on the method or process by which an action is performed rather than its outcome. Syntactic Diagnostic 1: Unspecified Objects Manner verbs frequently occur with unspecified or non-subcategorized objects in nonmodal, nonhabitual sentences. Example: “Anna wept all day.” (Acceptable) Syntactic Diagnostic 2: … view at source ↗
Figure 9
Figure 9. Figure 9: Manner vs. Result Verb Sentence Construc [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
read the original abstract

Manner and result verbs encode different aspects of event structure and have been discussed in developmental work as a potentially informative distinction for studying early verb learning. However, this distinction remains difficult to measure at scale because large annotated resources for manner and result classification are not currently available. We present a computational approach for identifying manner and result verbs in sentence context. Using linguistically informed prompts, we generate sentence-level annotations with large language models over data drawn from MASC and InterCorp, extending coverage from previously annotated portions of VerbNet to 436 classes. We then train a RoBERTa-based classifier on these annotations and evaluate it on three held-out gold-standard datasets, including previously annotated items and a new expert-annotated set. Across these evaluations, the model shows promising performance, with average accuracy up to 89.6%. We present this work as a scalable measurement tool that can support future research on verb semantics in developmental and other language datasets, while noting that further validation is needed for borderline cases, mixed manner/result verbs, and downstream developmental applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents a computational pipeline for identifying manner and result verbs in sentence context. Linguistically informed prompts are used to generate sentence-level annotations via large language models on data from MASC and InterCorp, extending VerbNet coverage to 436 classes. These LLM-generated labels are then used to train a RoBERTa classifier, which is evaluated on three held-out gold-standard datasets (including a new expert-annotated set) and reaches up to 89.6% accuracy. The work is positioned as a scalable measurement tool for verb semantics in developmental language research, while noting the need for further validation on borderline cases, mixed verbs, and downstream applications.

Significance. If the LLM-generated training labels are sufficiently accurate, the paper delivers a practical and extensible resource that addresses the scarcity of large annotated datasets for manner/result distinctions, potentially enabling new large-scale studies in developmental linguistics. The evaluation design using independent held-out gold-standard sets (rather than training labels) is a clear strength that avoids circularity and supports the reported performance. The extension of VerbNet coverage through this method is a useful contribution if the underlying annotations hold up.

major comments (1)
  1. Data annotation pipeline (prior to RoBERTa training): The manuscript reports no inter-annotator agreement, Cohen's kappa, or error analysis comparing the LLM-generated manner/result labels to expert judgments on any sample of the training data. This is load-bearing for the central claim of a reliable scalable tool, because any systematic biases in the LLM outputs (e.g., on borderline or mixed verbs) would propagate into the classifier without being caught by the downstream gold-standard evaluations alone.
minor comments (2)
  1. Abstract: The phrasing 'average accuracy up to 89.6%' is imprecise; reporting the exact accuracy on each of the three held-out datasets would improve clarity.
  2. Discussion section: The limitations paragraph could more explicitly outline concrete validation steps for the LLM annotations, such as sampling strategy and expert review protocol.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and recommendation for major revision. We address the concern about validation of the LLM-generated annotations below and agree that additional analysis will strengthen the manuscript.

read point-by-point responses

  1. Referee: Data annotation pipeline (prior to RoBERTa training): The manuscript reports no inter-annotator agreement, Cohen's kappa, or error analysis comparing the LLM-generated manner/result labels to expert judgments on any sample of the training data. This is load-bearing for the central claim of a reliable scalable tool, because any systematic biases in the LLM outputs (e.g., on borderline or mixed verbs) would propagate into the classifier without being caught by the downstream gold-standard evaluations alone.

    Authors: We appreciate the referee highlighting this gap. While the independent held-out gold-standard evaluations avoid circularity and support the reported accuracies, we agree that direct expert validation of the training labels would more rigorously address potential LLM biases on borderline or mixed verbs. In the revised manuscript, we will add an error analysis section: experts will annotate a sample of the LLM-labeled training data from MASC and InterCorp, and we will report agreement metrics including Cohen's kappa along with qualitative discussion of discrepancies. revision: yes

Circularity Check

0 steps flagged

No significant circularity; classifier performance evaluated on independent held-out gold-standard datasets.

full rationale

The paper's pipeline generates sentence-level manner/result annotations via LLM prompts on MASC and InterCorp data to extend VerbNet coverage, then trains a RoBERTa classifier on those labels. The central performance claim (up to 89.6% accuracy) is measured on three separate held-out gold-standard datasets, including a new expert-annotated set, rather than on the LLM-generated training data itself. This external benchmark prevents any reduction of the reported results to the training inputs by construction. No self-citations, self-definitional steps, fitted inputs renamed as predictions, or other enumerated circularity patterns appear in the derivation chain. The work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that LLM prompts can produce high-quality training labels for a linguistic distinction that is known to be subtle; no numerical free parameters or new invented entities are introduced.

axioms (1)
  • domain assumption Large language models can reliably produce sentence-level manner/result annotations when given linguistically informed prompts
    This assumption underpins the generation of the training data used to extend VerbNet coverage to 436 classes.

pith-pipeline@v0.9.0 · 5738 in / 1238 out tokens · 73679 ms · 2026-05-20T17:47:02.659457+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages · 3 internal anchors

  1. [2]

    John Beavers and Andrew Koontz-Garboden. 2012. Manner and result in the roots of verbal meaning. Linguistic inquiry, 43(3):331--369

    work page 2012

  2. [3]

    Susan Windisch Brown, Julia Bonn, James Gung, Annie Zaenen, James Pustejovsky, and Martha Palmer. 2019. Verbnet representations: Subevent semantics for transfer verbs. In Proceedings of the First International Workshop on Designing Meaning Representations, pages 154--163

    work page 2019

  3. [4]

    Hugh W Catts, Donald Compton, J Bruce Tomblin, and Mindy Sittner Bridges. 2012. Prevalence and nature of late-emerging poor readers. Journal of educational psychology, 104(1):166

    work page 2012

  4. [5]

    Gina Conti-Ramsden, Kevin Durkin, Umar Toseeb, Nicola Botting, and Andrew Pickles. 2018. Education and employment outcomes of young adults with a history of developmental language disorder. International journal of language & communication disorders, 53(2):237--255

    work page 2018

  5. [6]

    Steven J. DeRose. 1988. https://aclanthology.org/J88-1003/ Grammatical category disambiguation by statistical optimization . Computational Linguistics, 14(1):31--39

    work page 1988

  6. [7]

    Laura D'Odorico and Valentina Jacob. 2006. Prosodic and lexical aspects of maternal linguistic input to late-talking toddlers. International Journal of Language & Communication Disorders, 41(3):293--311

    work page 2006

  7. [8]

    David R Dowty. 2012. Word meaning and Montague grammar: The semantics of verbs and times in generative semantics and in Montague's PTQ, volume 7. Springer Science & Business Media

    work page 2012

  8. [9]

    Franti ek C erm \'a k and Alexandr Rosen. 2012. The case of intercorp, a multilingual parallel corpus. International Journal of Corpus Linguistics, 17(3):411--427

    work page 2012

  9. [10]

    Annemarie Friedrich and Damyana Gateva. 2017. Classification of telicity using cross-linguistic annotation projection. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2559--2565

    work page 2017

  10. [11]

    Annemarie Friedrich, Alexis Palmer, and Manfred Pinkal. 2016. Situation entity types: automatic classification of clause-level aspect. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1757--1768

    work page 2016

  11. [13]

    Dedre Gentner and Lera Boroditsky. 2001. Individuation, relativity, and early word learning. Language acquisition and conceptual development, 3:215--256

    work page 2001

  12. [14]

    Pamela A Hadley, Matthew Rispoli, and Ning Hsu. 2016. Toddlers' verb lexicon diversity and grammatical outcomes. Language, speech, and hearing services in schools, 47(1):44--58

    work page 2016

  13. [16]

    Carla W Hess, Karen M Sefton, and Richard G Landry. 1986. Sample size and type-token ratios for oral language of preschool children. Journal of Speech, Language, and Hearing Research, 29(1):129--134

    work page 1986

  14. [17]

    Matthew Honnibal and Ines Montani. 2017. spaCy 2 : Natural language understanding with B loom embeddings, convolutional neural networks and incremental parsing. To appear

    work page 2017

  15. [18]

    Sabrina Horvath, Justin B Kueser, Jaelyn Kelly, and Arielle Borovsky. 2022. Difference or delay? syntax, semantics, and verb vocabulary development in typically developing and late-talking toddlers. Language Learning and Development, 18(3):352--376

    work page 2022

  16. [19]

    Sabrina Horvath, Leslie Rescorla, and Sudha Arunachalam. 2019. The syntactic and semantic features of two-year-olds’ verb vocabularies: A comparison of typically developing children and late talkers. Journal of Child Language, 46(3):409--432

    work page 2019

  17. [20]

    Malka Rappaport Hovav and Beth Levin. 2010. Reflections on manner/result complementarity. Syntax, lexical semantics, and event structure, pages 21--38

    work page 2010

  18. [21]

    Nancy Ide, Collin Baker, Christiane Fellbaum, Charles Fillmore, and Rebecca Passonneau. 2008. Masc: The manually annotated sub-corpus of american english. In 6th International Conference on Language Resources and Evaluation, LREC 2008, pages 2455--2460. European Language Resources Association (ELRA)

    work page 2008

  19. [22]

    Karin Kipper, Anna Korhonen, Neville Ryant, and Martha Palmer. 2008. A large-scale classification of english verbs. Language Resources and Evaluation, 42:21--40

    work page 2008

  20. [23]

    Manfred Krifka. 1992. Thematic relations as links between nominal reference and temporal constitution. Lexical matters, (24):29

    work page 1992

  21. [24]

    Beth Levin. 2008. A constraint on verb meanings: Manner/result complementarity. Cognitive Science Department Colloqium Series, Brown University, Providence, RI, March, 17:2008

    work page 2008

  22. [25]

    Beth Levin and Malka Rappaport Hovav. 1991. Wiping the slate clean: A lexical semantic exploration. cognition, 41(1-3):123--151

    work page 1991

  23. [26]

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. https://api.semanticscholar.org/CorpusID:198953378 Roberta: A robustly optimized bert pretraining approach . ArXiv, abs/1907.11692

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  24. [27]

    Eleni Metheniti, Tim Van De Cruys, and Nabil Hathout. 2022. About time: Do transformers learn temporal verbal aspect? In 12th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2022), pages 88--101. ACL: Association for Computational Linguistic

    work page 2022

  25. [28]

    Letitia R Naigles and Erika Hoff-Ginsberg. 1998. Why are some verbs learned before other verbs? effects of input frequency and structure on children's early verb use. Journal of child language, 25(1):95--120

    work page 1998

  26. [29]

    Hollis S Scarborough. 1990. Index of productive syntax. Applied psycholinguistics, 11(1):1--22

    work page 1990

  27. [31]

    Marije L Verhage, Carlo Schuengel, Robbie Duschinsky, Marinus H van IJzendoorn, RM Pasco Fearon, Sheri Madigan, Glenn I Roisman, Marian J Bakermans-Kranenburg, and Mirjam Oosterman. 2020. The collaboration on attachment transmission synthesis (cats): A move to the level of individual-participant-data meta-analysis. Current Directions in Psychological Scie...

    work page 2020

  28. [32]

    Susan Ellis Weismer, Courtney E Venker, Julia L Evans, and Maura Jones Moyle. 2013. Fast mapping in late-talking toddlers. Applied Psycholinguistics, 34(1):69--89

    work page 2013

  29. [34]

    2017 , Note =

    Honnibal, Matthew and Montani, Ines , TITLE =. 2017 , Note =

    work page 2017

  30. [35]

    Cognitive Science Department Colloqium Series, Brown University, Providence, RI, March , volume=

    A constraint on verb meanings: Manner/result complementarity , author=. Cognitive Science Department Colloqium Series, Brown University, Providence, RI, March , volume=. 2008 , publisher=

    work page 2008

  31. [36]

    12th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2022) , pages=

    About Time: Do Transformers Learn Temporal Verbal Aspect? , author=. 12th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2022) , pages=. 2022 , organization=

    work page 2022

  32. [37]

    Journal of Child Language , volume=

    The syntactic and semantic features of two-year-olds’ verb vocabularies: A comparison of typically developing children and late talkers , author=. Journal of Child Language , volume=. 2019 , publisher=

    work page 2019

  33. [38]

    Why nouns are learned before verbs: Linguistic relativity versus natural partitioning

    Dedre Gentner. Why nouns are learned before verbs: Linguistic relativity versus natural partitioning. Language. 1982

    work page 1982

  34. [39]

    Language acquisition and conceptual development , volume=

    Individuation, relativity, and early word learning , author=. Language acquisition and conceptual development , volume=. 2001 , publisher=

    work page 2001

  35. [40]

    Syntax, lexical semantics, and event structure , pages=

    Reflections on manner/result complementarity , author=. Syntax, lexical semantics, and event structure , pages=. 2010 , publisher=

    work page 2010

  36. [41]

    Child Development , volume=

    The development of verb concepts: Children's use of verbs to label familiar and novel events , author=. Child Development , volume=. 1990 , publisher=

    work page 1990

  37. [42]

    Language Learning and Development , volume=

    Difference or delay? Syntax, semantics, and verb vocabulary development in typically developing and late-talking toddlers , author=. Language Learning and Development , volume=. 2022 , publisher=

    work page 2022

  38. [43]

    Grammatical Category Disambiguation by Statistical Optimization

    DeRose, Steven J. Grammatical Category Disambiguation by Statistical Optimization. Computational Linguistics. 1988

    work page 1988

  39. [44]

    Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , pages=

    Classification of telicity using cross-linguistic annotation projection , author=. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , pages=

    work page 2017

  40. [45]

    arXiv preprint arXiv:2208.09012 , year=

    A kind introduction to lexical and grammatical aspect, with a survey of computational approaches , author=. arXiv preprint arXiv:2208.09012 , year=

    work page arXiv

  41. [46]

    6th International Conference on Language Resources and Evaluation, LREC 2008 , pages=

    MASC: The manually annotated sub-corpus of American English , author=. 6th International Conference on Language Resources and Evaluation, LREC 2008 , pages=. 2008 , organization=

    work page 2008

  42. [47]

    International Journal of Corpus Linguistics , volume=

    The case of InterCorp, a multilingual parallel corpus , author=. International Journal of Corpus Linguistics , volume=. 2012 , publisher=

    work page 2012

  43. [48]

    Language Resources and Evaluation , volume=

    A large-scale classification of English verbs , author=. Language Resources and Evaluation , volume=. 2008 , publisher=

    work page 2008

  44. [49]

    cognition , volume=

    Wiping the slate clean: A lexical semantic exploration , author=. cognition , volume=. 1991 , publisher=

    work page 1991

  45. [50]

    Linguistic inquiry , volume=

    Manner and result in the roots of verbal meaning , author=. Linguistic inquiry , volume=. 2012 , publisher=

    work page 2012

  46. [51]

    2012 , publisher=

    Word meaning and Montague grammar: The semantics of verbs and times in generative semantics and in Montague's PTQ , author=. 2012 , publisher=

    work page 2012

  47. [52]

    Lexical matters , number=

    Thematic Relations as Links between Nominal Reference and Temporal Constitution , author=. Lexical matters , number=. 1992 , publisher=

    work page 1992

  48. [53]

    arXiv preprint arXiv:2303.16854 , year=

    Annollm: Making large language models to be better crowdsourced annotators , author=. arXiv preprint arXiv:2303.16854 , year=

    work page arXiv

  49. [54]

    arXiv preprint arXiv:2310.19596 , year=

    Llmaaa: Making large language models as active annotators , author=. arXiv preprint arXiv:2310.19596 , year=

    work page arXiv

  50. [55]

    , author=

    Prevalence and nature of late-emerging poor readers. , author=. Journal of educational psychology , volume=. 2012 , publisher=

    work page 2012

  51. [56]

    Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Situation entity types: automatic classification of clause-level aspect , author=. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    work page

  52. [57]

    Neural Machine Translation of Rare Words with Subword Units

    Neural machine translation of rare words with subword units , author=. arXiv preprint arXiv:1508.07909 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  53. [58]

    ArXiv , year=

    RoBERTa: A Robustly Optimized BERT Pretraining Approach , author=. ArXiv , year=

    work page

  54. [59]

    International conference on intelligent text processing and computational linguistics , pages=

    Part-of-speech tagging from 97\ author=. International conference on intelligent text processing and computational linguistics , pages=. 2011 , organization=

    work page 2011

  55. [60]

    GPT-4 Technical Report

    Gpt-4 technical report , author=. arXiv preprint arXiv:2303.08774 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  56. [61]

    Proceedings of the First International Workshop on Designing Meaning Representations , pages=

    VerbNet representations: Subevent semantics for transfer verbs , author=. Proceedings of the First International Workshop on Designing Meaning Representations , pages=

    work page

  57. [62]

    Applied Psycholinguistics , volume=

    Fast mapping in late-talking toddlers , author=. Applied Psycholinguistics , volume=. 2013 , publisher=

    work page 2013

  58. [63]

    International Journal of Language & Communication Disorders , volume=

    Prosodic and lexical aspects of maternal linguistic input to late-talking toddlers , author=. International Journal of Language & Communication Disorders , volume=. 2006 , publisher=

    work page 2006

  59. [64]

    Journal of child language , volume=

    Why are some verbs learned before other verbs? Effects of input frequency and structure on children's early verb use , author=. Journal of child language , volume=. 1998 , publisher=

    work page 1998

  60. [65]

    Applied psycholinguistics , volume=

    Index of productive syntax , author=. Applied psycholinguistics , volume=. 1990 , publisher=

    work page 1990

  61. [66]

    Journal of Speech, Language, and Hearing Research , volume=

    Sample size and type-token ratios for oral language of preschool children , author=. Journal of Speech, Language, and Hearing Research , volume=. 1986 , publisher=

    work page 1986

  62. [67]

    Current Directions in Psychological Science , volume=

    The collaboration on attachment transmission synthesis (CATS): A move to the level of individual-participant-data meta-analysis , author=. Current Directions in Psychological Science , volume=. 2020 , publisher=

    work page 2020

  63. [68]

    International journal of language & communication disorders , volume=

    Education and employment outcomes of young adults with a history of developmental language disorder , author=. International journal of language & communication disorders , volume=. 2018 , publisher=

    work page 2018

  64. [69]

    Language, speech, and hearing services in schools , volume=

    Toddlers' verb lexicon diversity and grammatical outcomes , author=. Language, speech, and hearing services in schools , volume=. 2016 , publisher=

    work page 2016