1. Automatic Measurement of Syntactic Development in Child Language Kenji Sagae Alon Lavie Brian MacWhinney Carnegie Mellon University

2. 2 Using Natural Language Processing in Child Language Research  CHILDES Database (MacWhinney, 2000)  Several megabytes of child-parent dialog transcripts  Part-of-speech and morphology analysis  Tools available  Recently proposed syntactic annotation scheme  Grammatical Relations (GRs)  POS analysis not enough for many research questions  Very small amount of annotated data  Parsing  Can we use current NLP tools to analyze CHILDES GRs?  Allows, for example, automatic measurement of syntactic development

3. 3 CHILDES GR Scheme (Sagae et al., 2004)  Developed with input from child language community  Grammatical Relations (GRs)  Subject, object, adjunct, etc.  Labeled dependencies

4. 4 CHILDES GR Scheme Includes Important GRs for Child Language Study

5. 5 Automatic Syntactic (GR) Analysis  Input: a sentence  Output: dependency structure (GRs)  Three steps  Text preprocessing  Unlabeled dependency identification  Dependency labeling

6. 6 STEP 1: Text Preprocessing Prepares Utterances for Parsing  CHAT transcription system  Explicitly marks certain extra-grammatical material: disfluency, retracing and repetitions  CLAN tools (MacWhinney, 2000)  Remove extra-grammatical material  Provide POS and Morphological analyses  CHAT and CLAN tools are publicly available http://childes.psy.cmu.edu

7. 7 Step 2: Unlabeled Dependency Identification  Why?  Large training corpus: Penn Treebank (Marcus et al., 1993)  Head-table converts constituents into dependencies  Use an existing parser (trained on the Penn Treebank)  Charniak (2000)  Convert output to dependencies  Alternatively, a dependency parser  For example: MALT parser (Nivre and Scholz, 2004), Yamada and Matsumoto (2003)

8. 8 Unlabeled Dependency Identification

9. 9 Domain Issues  Parser training data is in a very different domain  WSJ vs Parent-child dialogs  Domain specific training data would be better  But would have to be created (manually)  Performance is acceptable  Shorter, simpler sentences  Unlabeled dependency accuracy  WSJ test data: 92%  CHILDES data (2,000 words): 90%

10. 10 Final Step: Dependency Labeling  Training data is required  Labeling dependencies is easier than finding unlabeled dependencies  Less training data is needed for labeling than for full labeled dependency parsing  Use a classifier  TiMBL (Daelemans et al., 2004)  Extract features from unlabeled dependency structure  GR labels are target classes

11. 11 Dependency Labeling

12. 12 Features Used for GR Labeling  Head and dependent words  Also their POS tags  Whether the dependent comes before or after the head  How far the dependent is from the head  The label of the lowest node in the constituent tree that includes both the head and dependent

13. 13 Features Used for GR Labeling Consider the words “we” and “eat” Features: we, pro, eat, v, before, 1, S Class: SUBJ

14. 14 Features Used for GR Labeling Consider the words “we” and “eat” Features: we, pro, eat, v, before, 1, S Class: SUBJ

15. 15 Features Used for GR Labeling Consider the words “we” and “eat” Features: we, pro, eat, v, before, 1, S Class: SUBJ

16. 16 Good GR Labeling Results with Small Training Set  5,000 words for training  2,000 words for testing  Accuracy of dependency labeling (on perfect dependencies): 91.4%  Overall accuracy (Charniak parser + dependency labeling): 86.9%

17. 17 Some GRs Are Easier Than Others  Overall accuracy: 86.9%  Easily identifiable GRs  DET, POBJ, INF, NEG: Precision and recall above 98%  Difficult GRs  COMP, XCOMP: below 65%  Less than 4% of the GRs seen in training and test sets.

18. 18 Precision and Recall of Specific GRs GRPrecisionRecallF-score SUBJ0.940.93 OBJ0.830.910.87 COORD0.680.850.75 JCT0.910.820.86 MOD0.790.920.85 PRED0.800.830.81 ROOT0.910.920.91 COMP0.600.500.54 XCOMP0.580.640.61

19. 19 Index of Productive Syntax (IPSyn) (Scarborough, 1990)  A measure of child language development  Assigns a numerical score for grammatical complexity (from 0 to 112 points)  Used in hundreds of studies

20. 20 IPSyn Measures Syntactic Development  IPSyn: Designed for investigating differences in language acquisition  Differences in groups (for example: bilingual children)  Individual differences (for example: delayed language development)  Focus on syntax  Addresses weaknesses of Mean Length of Utterance (MLU)  MLU surprisingly useful until age 3, then reaches ceiling (or becomes unreliable)  IPSyn is very time-consuming to compute

21. 21 IPSyn Is More Informative Than MLU in Children Over Age 3yrs

22. 22 Computing IPSyn (manually)  Corpus of 100 transcribed utterances  Consecutive, no repetitions  Identify 56 specific language structures (IPSyn Items)  Examples:  Presence of auxiliaries or modals  Inverted auxiliary in a wh-question  Conjoined clauses  Fronted or center-embedded subordinate clauses  Count occurrences (zero, one, two or more)  Add counts

23. 23 Automating IPSyn  Existing state of manual computation  Spreadsheets  Search each sentence for language structures  Use part-of-speech tagging to narrow down the number of sentences for certain structures  For example: Verb + Noun, Determiner + Adjective + Noun  Can’t we just use part-of-speech tagging?  Only one other automated implementation of IPSyn exists, and it uses only words and POS tags

24. 24 Automating IPSyn without Syntactic Analysis  Use patterns of words and parts-of-speech to find language structures  Computerized Profiling, or CP (Long, Fey and Channell, 2004)  Works well for many IPSyn items  Det + Adjective + Noun sequence  But does not work very well for several important items  Fronted or center-embedded subordinate clauses  Inverted auxiliary in a wh-question  Cuts down manual work significantly (good)  Fully automatic IPSyn scores only somewhat accurate (not so good)

25. 25 Where POS Tagging is not enough  Sentences with same POS sequence may have different structure (a)Before [,] he told the man he was cold. (b)Before he told the story [,] he was cold.  Some syntactic structures are difficult to recognize using only POS tags and words  Search patterns may under- and over-generate  Using syntactic analysis is easier and more reliable

26. 26 Some IPSyn Items Require Syntactic Analysis for Reliable Recognition (and some don’t)  Determiner + Adjective + Noun  Auxiliary verb  Adverb modifying adjective or nominal  Subject + Verb + Object  Sentence with 3 clauses  Conjoined sentences  Wh-question with inverted auxiliary/modal/copula  Relative clauses  Propositional complements  Fronted subordinate clauses  Center-embedded clauses

27. 27 Automating IPSyn with Grammatical Relation Analyses  Search for language structures using patterns that involve POS tags and GRs (labeled dependencies)  Still room for under- and over-generalization, but patterns are easier to write and more reliable  Examples  Wh-embedded clauses: search for wh-words whose head (or transitive head) is a dependent in a GR of types [XC]SUBJ, [XC]PRED, [XC]JCT, [XC]MOD, COMP or XCOMP  Relative clauses: search for a CMOD where the dependent is to the right of the head

28. 28 From Transcripts to IPSyn Scores Automatically Transcript (in CHAT format) Transcriber POS + Morphology Clean Text CLAN Tools GR Parser Words POS Morphology GRs Search for IPSyn items IPSyn Results (item counts are added into final IPSyn score)

29. 29 From Transcripts to IPSyn Scores Automatically Transcript (in CHAT format) Transcriber POS + Morphology Clean Text CLAN Tools GR Parser Words POS Morphology GRs Search for IPSyn items IPSyn Results (item counts are added into final IPSyn score)

30. 30 Evaluation Data  Two sets of transcripts with IPSyn scoring from two different child language research groups  Set A  Scored fully manually  20 transcripts  Ages: about 3 yrs.  Set B  Scored with CP first, then manually corrected  25 transcripts  Ages: about 8 yrs. (Two transcripts in each set were held out for development and debugging)

31. 31 Evaluation Metrics: Point Difference  Point difference  The absolute point difference between the scores provided by our system, and the scores computed manually  Simple, and shows how close the automatic scores are to the manual scores  Acceptable range  Smaller for older children

32. 32 Evaluation Metrics: Point-toPoint Accuracy  Point-to-point accuracy  Reflects overall reliability over each scoring decision made in the computation of IPSyn scores  Scoring decisions: presence or absence of language structures in the transcript Point-to-Point Acc = C(Correct Decisions) C(Total Decisions)  Commonly used for assessing inter-rater reliability among human scorers (for IPSyn, about 94%).

33. 33 Results  IPSyn scores from  Our GR-based system (GR)  Manual scoring (HUMAN)  Computerized Profiling (CP)

34. 34 GR-based IPSyn Is Quite Accurate SystemAvg. Point Difference to HUMAN Point-to-point Reliability (%) GR (total)3.392.8 CP (total)8.385.4 GR (set A)3.792.5 CP (set A)6.286.2 GR (set B)2.993.0 CP (set B)10.284.8

35. 35 Comparing Our GR-IPSyn and CP-IPSyn

36. 36 Error Analysis: Four Problematic Items Cause Half of Error  Four (of 56) IPSyn items account for about half of all mistakes made by our GR-based system (a)Propositional complement: 16.9% “I said you can go now” (b) Copula/Modal/Aux for emphasis or ellipsis: 12.3% “I thought he ate his cake, but he didn’t.” (c) Relative clause: 10.6% “This is the car I saw.” (d) Bitransitive predicate: 5.8% “I gave her the book.” (a), (c), (d): Incorrect GR analysis (b): Imperfect search pattern

37. 37 Conclusion and Future Work  We can annotate transcripts of child language with Grammatical Relations using current NLP tools and a small amount of manually annotated data  The reliability of an automated version of IPSyn that uses CHILDES GRs is close to that of human scoring  GR analysis still needs work  More training data  Other parsing techniques  Use of GR-based IPSyn by child language researchers should reveal additional problem areas

38. 38 References Charniak, E. 2000. A maximum-entropy-inspired parser. Proceedings of the First Annual Meeting of the North American Chapter of the Association for Computational Linguistics. Seattle, WA. Daelemans, W., Zavrel, J., van der Sloot, K., and van den Bosch. 2004. TiMBL: Tilburg Memory Based Learner, version 5.1, Reference Guide. ILK Research Group Technical Report Series, no. 04-02, 2004. Long, S. H., Fey, M. E., Channell, R. W. 2004. Computerized Profiling (version 9.6.0). Cleveland, OH: Case Western Reserve University. MacWhinney, B. 2000. The CHILDES Project: Tools for Analyzing Talk. Mahwah, NJ: Lawrence Erlbaum Associates. Marcus, M. P., Santorini, B., Marcinkiewics, M. A. 1993. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19. Nivre, J., Scholz, M. 2004. Deterministic parsing of English text. Proceedings of the International Conference on Computational Linguistics (pp. 64-70). Geneva, Switzerland. Sagae, K., MacWhinney, B., Lavie, A. 2004. Adding syntactic annotations to transcripts of parent-child dialogs. Proceedings of the Fourth International Conference on Language Resources and Evaluation. Lisbon, Portugal. Scarborough, H. S. 1990. Index of Productive Syntax. Applied Psycholinguistics, 11, 1- 22.