Discovering the Invisible: Towards Children’s Acquisition of the Implicit Object Construction

1. Discovering the Invisible: Towards Children’s Acquisition of the Implicit Object Construction Tamara Nicol1, Barbara Landau1 and Philip Resnik2 Department of Cognitive Science1, Johns Hopkins University Department of Linguistics2, University of Maryland Box 1 Box 3 Introduction Experiment 1: Do Verbs Vary In Their Object Predictability for Children and their Parents? Problem: If children rely on the number of explicit noun phrases to infer a verb’s argument structure, how do they learn that the absence of an overt direct object need not signal the absence of an underlying argument? Method Object Similarity Verb Selectivity Elicited a direct object corpus from 2 and 3 year olds for a list of 30 verbs (from Resnik, 1996). The same was done with parents. bring find hit play read take call get like pour say want catch give make pull see watch drink hang open push show wear eat hear pack put sing write Table 1. Comparison of Object Similarity Across Children’s and Parents’ Verbs Table 2. Comparison of Verb Selectivity Across Children’s and Parents’ Verbs Using Nouns to Learn the Verb Meanings Linguistic Accounts of Implicit Objects • Idiosyncracy (e.g., Katz & Postal, 1964; Fillmore, 1986) • Whether a verb allows implicit objects is simply a matter of lexical idiosyncracy (3 and 4). • 3. a. Mary eats. • b. * Mary devours. • 4. a. Mary approves. • b. * Mary authorizes. • Aspectual Criteria (e.g., Mittwoch, 1982; Van Hout, 1996; Olsen & Resnik, 1997) • Unbounded activity verbs are more likely to allow implicit objects (5a) than bounded accomplishment verbs (5b). However, some telic verbs do allow implicit objects (6a and b). • 5. a. Mary sang. • b. * Mary found. • 6. a. Mary won. • b. Mary left. • Typicality of the Object (e.g., Rice, 1988; Resnik, 1996) • Objects that are typical of the verb are more easily recoverable when omitted (7). • 7. John smokes (cigarettes / * Marlboros / * a pipe / • * SMOKING MATERIALS). According to most theories of generative grammar, a verb’s underlying semantic representation (its meaning) consists of a specification for its semantic arguments and their thematic roles, such as AGENT and PATIENT (Jackendoff, 1983). In English, some verbs allow their internal arguments to be unexpressed in the surface form (1), while others disallow it (2). 1. a. Mary left the house b. Mary left. 2. a. Mary pushed the cart. b. * Mary pushed. If object omission varies unpredictably across verbs, then the question arises of how the learner is to figure out which verbs allow implicit objects and which do not. This question is particularly relevant to theories of verb learning, such as syntactic bootstrapping (Gleitman, 1990), that require the learner to pay attention to the number of arguments used with a verb in order to narrow down its meaning. In order for these theories of learning to be viable, they must assume that an implicit object is somehow recoverable. Participants and Procedure Children (n = 20; 2;6-3;11) Question and Answer Game – Asked to provide 3 direct objects for each verb, following a question such as “What are some things you eat?” Parents (n = 14) Written Questionnaire – Asked to provide 10 direct objects for each verb. (Analysis only considered first 3 objects listed for verb.) 2df=1 = 4.92 p < 0.05 2df=1 = 3.76 p < 0.10 2df=1 = 6.67 p < 0.05 2df=1 = 0.27 n.s. Low Object Similarity: Of the parents’ 15 verbs that had low object similarity ratings, 11 verbs also had low object similarity ratings for children. High Object Similarity: Of the parents’ 15 verbs that had high object similarity ratings, 13 verbs also had high object similarity ratings for children. Low Verb Selectivity: Of the parents’ 15 verbs that had low verb selectivity, 13 verbs also had low verb selectivity for children. High Verb Selectivity : Of the parents’ 15 verbs that had high verb selectivity, only 6 verbs also had high verb selectivity for children. r = 0.70, p < 0.05 r = 0.61, p < 0.05 Object Similarity ratings across verbs were correlated for children and parents. Moreover, a separate analysis showed that both children’s and parents’ Object Similarity ratings were correlated with Selectional Preference Strength for parents’ spontaneous speech from Resnik, 1996. Verb Selectivity scores across verbs were correlated for children and parents. Moreover, a separate analysis showed that both children’s and parents’ Verb Selectivity scores were correlated with Selectional Preference Strength for parents’ spontaneous speech from Resnik, 1996. Box 4 Experiment 2: Does Object Predictability Correlate with Percentage of Omitted Objects? Method Relationship Between Object Predictability and Percentage of Omitted Objects Percentage of Omitted Objects Examined the relationship between object predictability from Experiment 1 and percent omitted objects in children’s and adults’ corpora. Table 3. Comparison of Percentage of Omitted Objects Across Children’s and Adults’ Verbs Note: Only 27 verbs were analyzed. 3 verbs (hit, pack, and pour) were excluded because they were used less than 3 times in the children’s corpus. Participants Table 4. Correlations between Object Similarity Ratings and Verb Selectivity Scores (SPS) to Percentage of Omitted Objects Children (3;5 – 5;2, MLU > 4.00) Sarah, Adam, & Naomi (CHILDES, MacWhinney, 2000): 5 files each, 1289 total utterances containing verbs from Experiment 1 For each verb, percentage of omitted objects was calculated as the ratio of # of Instances of Omitted Objects to Total Verb Use Instances of Omitted Objects: V Mary drew (a picture). V AP Mary drew (a picture) carefully. V PP Mary drew (a picture) for her mother. V NP2 Mary drew her mother (a picture). Adults (from Resnik, 1996) Rate of object omission was calculated over a random sample of 100 instances of each verb extracted from the Brown corpus of American English (Francis & Kucera, 1982) Note: The Brown corpus of American English includes text of written edited English prose. We are currently calculating rate of object omission for parents’ spontaneous speech. Box 2 Proposal Verbs with more predictable objects tend to allow implicit arguments. If learners know this correspondence, then they could use it to predict which verbs take implicit objects and which do not. Measures of Object Predictability:Calculated over a corpus of verbs and NP objects from children’s and adults’ elicited speech. 2df=1 = 1.07 n.s. 2df=1 = 4.92 p < 0.05 Object Similarity How similar are a verb’s direct objects to each other? If a verb’s objects are highly similar to one another, then the verb should be more likely to allow its objects to be implicit. Measure: An average similarity rating of objects to each other for each verb, as rated by 6 independent adult raters. For each group (children and parents), each person rated 3 NP object pairs for each of the 30 verbs, for a total of 90 judgments per person (540 total). Compare pairs of nouns for each verb: Noun 1Noun 2Mean RatingVerb a new VCR a job 2 want a suitcase lunch 3 pack orange juice juice 7 drink Calculate mean similarity rating of pairs for each verb: VerbMean Rating want 2.47 pack 4.33 drink 5.67 Verb Selectivity(Resnik, 1996) How selective is a verb with regard to the arguments that can appear as its direct objects? If a verb is highly selective with regard to the objects it takes, then this should predict the tendency of a verb to have implicit objects. Measure:Selectional Preference Strength (Resnik, 1996) calculates the difference between - the distribution of the nouns semantic classes given a particular verb, Pr(c|pi), and - the overall distribution of nouns semantic classes Pr(c) S(pi) =  Pr(c|pi) log [ Pr(c|pi) / Pr(c) ] Probabilities involving noun classes were approximated in the computational model using WordNet. For example, VerbSelectional Preference Strength want 1.71 low pack 2.46 drink 2.64 high r = 0.45, p < 0.05 Never Omitted Objects: Of the 13 verbs which adults never used without an overt object, it was only the case for 2 of these verbs that children never used them without an overt object. Omitted Objects > 1% of the time: Of 14 verbs for which adults omitted objects at least 1% of the time, children omitted objects at least 1% of the time with Positive correlation suggests that percentage of omitted objects was similar for children and adults. Note that children’s omissions were generally grammatical, but they used omitted objects with more verbs than adults did. Box 5 Box 6 Summary and Conclusion Acknowledgments Special thanks are due to Gitana Chunyo (Lab Coordinator), Eric Hsaio (Undergraduate Research Assistant), and Rosalinda Licona (Undergraduate Research Assistant) in the Landau Lab at Johns Hopkins University. Experiment 1: Elicited Objects from 2 and 3 year olds and parents Discussion If learners recognize the correspondence between object predictability and object omission, they could use this relationship to predict which verbs allow implicit objects and which do not. Identifying which verbs allow implicit objects would allow learners to “recover” underlying arguments, and would therefore give them access to the verb’s underlying verb-argument structure. • Object Similarity and Verb Selectivity vary across verbs in a similar way for children and parents. • Both Object Similarity and Verb Selectivity were correlated with Selectional Preference Strength for parents’ spontaneous speech from Resnik, 1996. • However, children’s verbs appeared to be less selective overall than parents’ verbs. References Fillmore, C.J. 1986. Pragmatically controlled zero anaphora. In Proceedings of the Annual Meeting of the Berkeley Linguistics Society (pp. 95-107). Berkeley, CA: Berkeley Linguistics Society. Francis, W.N. & Kucera, H.. 1982. Frequency analysis of English usage: Lexicon and grammar. Boston: Houghton Mifflin. Gleitman, L. 1990. The structural sources of verb meanings. Language Acquistion, 1(1), 3-55. Jackendoff, R. 1990. Semantic Structures. Cambridge, MA: The MIT Press. Katz, J.J. & Postal, P.M. 1964. An integrated theory of linguistic description. Cambridge, MA: The MIT Press. Mahwah, NJ: Lawrence Erlbaum Associates. MacWhinney, B. 2000. The CHILDES Project: Tools for analyzing talk. Third Edition. Mittwoch, A. 1982. On the difference between eating and eating something: Actvities versus accomplishments. Linguistic Inquiry, 13(1), 113-122. Olsen, M.B. & Resnik, P. 1997. Implicit object constructions and the (in)transitivity continuum. Presented at the 33rd Regional Meeting of the Chicago Linguistics Society. Resnik, P. 1996. Selectional constraints: An information-theoretic model and its computational realization. Cognition, 61, 127-159. Rice, S. 1988. Unlikely lexical entries. In Proceedings of the Annual Meeting of the Berkeley Linguistics Society (pp. 202-212). Berkeley, CA: Berkeley Linguistics Society. Van Hout, 1996. Event semantics of verb frame alternations: A case study Dutch and its acquisition. Tilburg University Dissertation Series. Future Research What is the relationship between Object Similarity and Verb Selectivity, and how do they differ with regard to object omission? There may be artifactual effects due to the implementation of the computational model. We are currently improving the model by more accurately correcting children’s utterances to correspond to the semantic categories in WordNet. Is there a developmental trend towards adult-like Object Similarity and Verb Selectivity? What is the role of aspectual criteria in the possibility of object omission? While children’s object omissions are generally grammatical, do they “overgeneralize”? If so, what characterizes their omissions? Experiment 2: Implicit Objects from CHILDES and Brown corpus of American English • Percentage of omitted objects were correlated for children and adults across verbs. However, children used omitted objects with more verbs than adults did. • Object Similarity was correlated with percentage of omitted objects for both children and adults. • Verb Selectivity was correlated with percentage of omitted objects for adults, but not for children.