Output-driven phonology: theory and learning
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2014
|
| Edition: | 1. publ. |
| Series: | Cambridge studies in linguistics
139 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIX, 415 S. graph. Darst. |
| ISBN: | 9781107001930 |
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| adam_text | Titel: Output-driven phonology
Autor: Tesar, Bruce
Jahr: 2014
Contents
List of figures page xvii
Acknowledgments xix
1 Characterizing surface orientedness in phonology 1
1.1 Surface orientedness 1
1.1.1 Surface orientedness in phonology 1
1.1.2 Formalizing surface orientedness 3
1.2 Surface orientedness in Optimality Theory 6
1.2.1 Markedness violations as opacity 7
1.2.2 Markedness constraints cause disparities 10
1.2.3 Something more general 11
1.3 Formalizing surface orientedness: output-driven maps 13
1.4 Output drivenness and Optimality Theory 15
1.5 Output drivenness and learning 16
1.6 The relationship between learnability and linguistic theory 18
2 Output-driven maps 20
2.1 The main idea 20
2.1.1 Terminology: candidates and correspondence 20
2.1.2 Inputs of greater similarity yield the same output 21
2.1.3 Unifying surface orientedness 23
2.2 Relative similarity 26
2.2.1 Relating the disparities of two candidates 26
2.2.2 Individuating disparities 28
2.2.3 Relative similarity is a relational notion 30
2.2.4 The importance of input-output correspondence 33
2.3 Output drivenness is not process opacity 37
2.3.1 One map, multiple generalizations 37
2.3.2 Epenthesis and assimilation in Lithuanian 39
2.3.3 Closeness with processes 42
2.4 Formal analysis with segmental IO correspondence 44
x Contents
2.4.1 Maps from inputs to candidates 44
2.4.2 The internal structure of candidates 46
2.4.3 Relating candidates to each other 48
2.4.4 The non-uniqueness of input-input correspondence 53
2.4.5 Removing disparities by changing the input 54
2.4.6 The identical disparity requirement and surface orientedness 55
2.4.7 Individuating disparities (again) 57
2.5 Expanding to other representational theories 59
2.5.1 Non-identical corresponding representational elements 59
2.5.2 Non-unique correspondence 60
2.5.3 Autosegmental representation 60
2.6 The map 61
3 Output-driven maps in Optimality Theory 63
3.1 Background: ERC entailment in Optimality Theory 64
3.1.1 Elementary ranking conditions 64
3.1.2 Single ERC entailment: L-retraction and W-extension 66
3.1.3 Joint ERC entailment: fusion 67
3.2 Relating output-driven maps to Optimality Theory 69
3.2.1 Output-driven maps and optimization 69
3.2.2 A designated competitor: aoy 70
3.2.3 Relationships among the disparities 74
3.2.4 As goes bpy, so goes aoy 78
3.2.5 Output-driven maps and constraints 81
3.3 Sufficient conditions for output-driven maps 83
3.3.1 Properties of Gen: correspondence uniformity 84
3.3.2 Properties of constraints: output-driven preserving 86
3.3.3 Proof of sufficient conditions for output-driven maps 87
3.4 Basic constraints: overview of the results 88
3.4.1 Terminology: faithfulness and input-referring constraints 88
3.4.2 Markedness constraints 89
3.4.3 Value-independent input-referring constraints 89
3.4.4 Value-restricted input-referring constraints 90
3.5 Analysis of relationships between disparities 91
3.5.1 The set-up 91
3.5.2 Deletion disparities 93
3.5.3 Insertion disparities 94
3.5.4 Identity disparities 96
3.5.5 Comments/discussion 101
3.6 Output-driven preserving constraints: the proofs 101
3.6.1 Outline of the proof structure 101
3.6.2 Max 102
3.6.2.1 Partition of the deletion disparities 102
Contents xi
3.6.2.2 Corresponding deletion disparities for aoy and bpy 103
3.6.2.3 Corresponding deletion disparities for akx and bmx 103
3.6.2.4 Non-corresponding deletion disparities for aoy and
aite 104
3.6.2.5 MAx(fcpy) MAx(bmx) entails MAx(aoy)
MAx(ah) 104
3.6.2.6 M x(bpy) = Max(ìwix) entails MAx(aoy)
MAx(ata) 105
3.6.3 Dep 105
3.6.3.1 Partition of the insertion disparities 105
3.6.3.2 Corresponding insertion disparities for aoy and bpy 106
3.6.3.3 Corresponding insertion disparities for akx and bmx 106
3.6.3.4 Non-corresponding insertion disparities for aoy and
akx 106
3.6.3.5 Proof: DEP(bpy) Dep(2 hix) entails DEP(aoy)
DEp(ata) 107
3.6.3.6 Proof: DEP(f py) = DEP(fcmx) entails DEP(aoy)
DEP(afcc) 107
3.6.4 lDENT[Fin€V] 108
3.6.4.1 Partition of the identity disparities 108
3.6.4.2 Identical corresponding identity disparities for aoy
and bpy 109
3.6.4.3 Corresponding identity disparities for akx and bmx 110
3.6.4.4 Non-Corresponding and non-identical
corresponding identity disparities for aoy and akx 110
3.6.4.5 Proof: lDENT[Fin G V](bpy)
lDENT[Fin G V (bmx) entails
iDENTtFj,, G V](aoy) iDENTÍFi,, € V](öfcc) 110
3.6.4.6 Proof: Ident^ G V](bpy) =
Ident[F„, G V](bmx) entails
lDENT[Fto G V](aoy) lDENT[Fto G V](afct) 111
3.6.5 IdentPw G V] 112
3.6.5.1 Partition of the identity disparities 113
3.6.5.2 Corresponding identity disparities for aoy and bpy 113
3.6.5.3 Corresponding identity disparities for akx and bmx 114
3.6.5.4 Non-corresponding identity disparities for aoy and
akx 114
3.6.5.5 Proof: lDENTrF011t G V](bpy)
Id en T [Fo„t G V](bmx) entails
IdentPw G V](ûoy) lDENT[Fout € V](akx) 114
3.6.5.6 Proof: Ident[Fou, G V](bpy) =
Ident[Fou, G ](bmx) entails
lDENT[F011t G V](aoy) Ident[Fou, G V](afcc) 115
3.7 The map 116
xii Contents
4 Analysis of constraint behavior 117
4.1 Non-ODP constraints and non-output-driven maps 117
4.2 Illustrating the three way s of non-ODP 119
4.2.1 Distinction only at lesser similarity 119
4.2.2 Distinction only at greater similarity 122
4.2.3 Distinction conflict 125
4.3 Relating non-ODP constraint behaviors to non-ODM map patterns 127
4.4 Faithfulness conditioned on output context 129
4.4.1 Positional faithfulness I: position-specific Dep 129
4.4.2 Positional faithfulness II: position-specific Ident[F] 132
4.4.3 Conjoined markedness and faithfulness 134
4.4.4 Value-restricted Dep 137
4.4.5 Summary: independent context 138
4.5 Faithfulness conditioned on input context 139
4.5.1 Value-restricted Max 139
4.5.2 Relation to output-conditioned faithfulness 142
4.6 Multiply conditioned faithfulness 143
4.6.1 Joint input-output value restrictions 143
4.6.2 Conditioning on disparities 144
4.7 Conditioned antifaithfulness 145
4.7.1 Material implication constraints 145
4.7.2 F -*¦ M as output-conditioned antifaithfulness 146
4.8 Reference to other forms: sympathy 148
4.9 Eventual idempotency 150
4.9.1 Classical OT grammars are eventually idempotent 151
4.9.2 Absolute vs. relative satisfaction of constraints 154
4.9.3 Derived environment exchanges 155
4.9.4 Absolute vs. relative characterizations of similarity 159
4.10 The role of relative similarity 161
4.10.1 Distinguishing faithfulness from faithfulness constraints 161
4.10.2 The rotation theorem isn t about faithfulness 162
4.10.3 Relative similarity links grammars to maps 166
4.11 More on relating non-ODP behaviors to non-ODM patterns 167
4.12 Maps and grammars 170
4.13 The map 171
5 Learning phonotactics 173
5.1 An overview of the learning problem 174
5.2 Stress and length: a system for illustration 175
5.3 Constructing a constraint hierarchy from winner-loser pairs 177
5.3.1 Recursive Constraint Demotion 177
5.3.2 Stratified constraint hierarchies 181
5.3.3 Constraint conflict and ranking relations 184
Contents xiii
5.4 Selecting winner-loser pairs 185
5.4.1 Error detection 186
5.4.2 Production-directed parsing with stratified hierarchies 187
5.4.2.1 Mark pooling 188
5.4.2.2 Conflicts Tie 191
5.4.2.3 Variationist EDCD 194
5.4.3 MultiRecursive Constraint Demotion (MRCD) 195
5.4.4 MRCD step by step 197
5.4.4.1 First pair 198
5.4.4.2 Second pair 198
5.4.4.3 Third pair 198
5.4.4.4 Fourth pair 199
5.4.4.5 Fifth pair 199
5.4.4.6 Last pair 200
5.4.5 Limitations of loser production via stratified hierarchies 200
5.5 Assessing computational requirements 203
5.5.1 The computational complexity of MRCD 203
5.5.2 Grammar space vs. language space 205
5.6 Restrictiveness biases 207
5.6.1 Restrictiveness in learning 207
5.6.2 Phonotactic learning 209
5.6.3 Language subsets vs. grammar subsets 211
5.6.4 Biased Constraint Demotion 213
5.6.4.1 RCD and restrictiveness 213
5.6.4.2 Estimating the restrictiveness of grammars: the
r-measure 215
5.6.4.3 A restrictiveness bias for RCD 216
5.6.5 Enforcing restrictiveness in phonotactic learning 220
5.6.5.1 Enforcing restrictiveness with BCD 220
5.6.5.2 Restrictiveness with hierarchy only learning 221
5.6.6 Implicit representation of phonotactic restrictions 222
5.7 Phonotactic contrast 224
5.7.1 Contrast and the nature of phonotactic learning 224
5.7.2 A canonical form for phonotactic contrast 226
5.8 Phonotactic information underdetermines languages 233
5.9 The map 236
5.10 Appendix: the Stress/Length typology 237
6 Learning with paradigmatic information 246
6.1 Paradigmatic information 247
6.2 The explosive growth of lexical hypothesis spaces 248
6.2.1 Now that s big 248
6.2.2 The basic alternant constraint 248
xiv Contents
6.2.3 Selected prior work 250
6.2.4 Combinatorics of the Stress/Length linguistic system 252
6.3 An aside on methodology 254
6.4 Inconsistency detection 256
6.5 Setting underlying features via inconsistency detection 258
6.5.1 Feature setting 259
6.5.2 Setting a single unset feature 260
6.5.3 Multiple unset features 263
6.5.4 Multiple words and local léxica 266
6.6 Non-phonotactic ranking information 269
6.6.1 Ranking information in local léxica 269
6.6.2 The join operation 270
6.6.3 Extracting shared ranking information 272
6.7 The Contrast Pair and Ranking (CPR) algorithm 274
6.8 Computational issues for CPR 276
6.9 The map 277
7 Exploiting output drivenness in learning 279
7.1 Contrast with Richness of the Base 280
7.1.1 Contrastive for an input 280
7.1.2 Contrastive for a morpheme 282
7.1.3 Contrast in output-driven maps 283
7.2 Relative similarity lattices 284
7.3 Limiting lexical search in output-driven maps 287
7.4 Phonotactic contrast and underlying feature values 291
7.5 Morphemic alternation and non-phonotactic ranking information 294
7.6 Contrast pairs 299
7.6.1 When one word isn t enough 299
7.6.2 A disjunction of disparities 302
7.6.3 Restricted rankings create inconsistencies 305
7.6.4 The roles of alternation and contrast in contrast pairs 308
7.6.5 Multiple words and relative similarity 310
7.6.6 Another illustration: setting the stress feature for rl 316
7.6.7 Setting environment morpheme features 317
7.7 Beyond error-driven learning 321
7.7.1 Uncertainty in the lexicon 323
7.7.2 Uncertainty about the ranking 328
7.7.3 Single form learning 332
7.7.4 Contrast pair learning 334
7.8 The Output-Driven Learner (preliminary) 335
7.9 Learning language L20 336
7.9.1 Phonotactic learning 336
7.9.2 Initial single form learning 338
Contents xv
7.9.2.1 risi 338
7.9.2.2 rls4 340
7.9.2.3 r2sl,r4sl 343
7.9.2.4 r3sl,rls3 344
7.9.3 Contrast pair learning 345
7.9.4 Second single form learning 348
7.9.4.1 r3s3 348
7.9.4.2 r4s3, rls3, r2s3 349
7.9.4.3 rls2,r2s4 350
7.9.4.4 The final learned grammar for L20 351
7.10 The map 352
8 Paradigmatic subsets 353
8.1 The phenomenon: paradigmatic subsets 353
8.1.1 Language L8, the subset language 353
8.1.2 Language L7, the superset language 355
8.1.3 L8 is a paradigmatic subset of L7 355
8.2 The problem: attempting to learn L8 358
8.3 Paradigmatic restrictiveness and the lexicon 360
8.4 The solution: Fewest Set Features 363
8.4.1 The Fewest Set Features procedure 363
8.4.2 Algorithmic details of Fewest Set Features 370
8.4.3 The relation to maximum likelihood 372
8.4.4 Summary: restrictiveness in the lexicon 376
8.5 Evaluating the Output-Driven Learner 376
8.5.1 The Output-Driven Learner (revised) 376
8.5.2 Simulation results 377
8.5.2.1 No contrast languages 378
8.5.2.2 Single form learning, no contrast
pairs 378
8.5.2.3 Contrast pairs 379
8.5.2.4 Fewest Set Features 380
8.5.2.5 A little perspective 381
8.5.3 Issues 381
8.6 The map 383
9 Linguistic theory and language learnability 385
9.1 Contrast and the final lexicon 385
9.2 Forms of restrictiveness enforcement 390
9.3 Evaluation metrics 392
9.4 What is stored by the learner (and why) 394
9.4.1 Ranking information 394
9.4.2 Lexical information 395
xvi Contents
9.4.3 Structural ambiguity and multiple hypotheses 396
9.5 Beyond output drivenness in learning 398
9.6 What has been accomplished 399
References 401
Index 410
|
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| spelling | Tesar, Bruce Verfasser aut Output-driven phonology theory and learning Bruce Tesar 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2014 XIX, 415 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Cambridge studies in linguistics 139 Includes bibliographical references and index Grammar, Comparative and general / Phonology Optimality theory (Linguistics) Learning ability Grammatik Optimalitätstheorie (DE-588)4468124-0 gnd rswk-swf Phonologie (DE-588)4045836-2 gnd rswk-swf Lerntheorie (DE-588)4114402-8 gnd rswk-swf Phonologie (DE-588)4045836-2 s Optimalitätstheorie (DE-588)4468124-0 s Lerntheorie (DE-588)4114402-8 s DE-604 Cambridge studies in linguistics 139 (DE-604)BV000000639 139 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026252445&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Tesar, Bruce Output-driven phonology theory and learning Cambridge studies in linguistics Grammar, Comparative and general / Phonology Optimality theory (Linguistics) Learning ability Grammatik Optimalitätstheorie (DE-588)4468124-0 gnd Phonologie (DE-588)4045836-2 gnd Lerntheorie (DE-588)4114402-8 gnd |
| subject_GND | (DE-588)4468124-0 (DE-588)4045836-2 (DE-588)4114402-8 |
| title | Output-driven phonology theory and learning |
| title_auth | Output-driven phonology theory and learning |
| title_exact_search | Output-driven phonology theory and learning |
| title_full | Output-driven phonology theory and learning Bruce Tesar |
| title_fullStr | Output-driven phonology theory and learning Bruce Tesar |
| title_full_unstemmed | Output-driven phonology theory and learning Bruce Tesar |
| title_short | Output-driven phonology |
| title_sort | output driven phonology theory and learning |
| title_sub | theory and learning |
| topic | Grammar, Comparative and general / Phonology Optimality theory (Linguistics) Learning ability Grammatik Optimalitätstheorie (DE-588)4468124-0 gnd Phonologie (DE-588)4045836-2 gnd Lerntheorie (DE-588)4114402-8 gnd |
| topic_facet | Grammar, Comparative and general / Phonology Optimality theory (Linguistics) Learning ability Grammatik Optimalitätstheorie Phonologie Lerntheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026252445&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000000639 |
| work_keys_str_mv | AT tesarbruce outputdrivenphonologytheoryandlearning |