Chapter 1  Introduction    1

Part I   Translational Equivalence among Word Tokens    7
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 Chapter 2  A Geometric Approach to Mapping Bitext Correspondence    8
	2.1  Introduction    9
	2.2  Bitext Geometry    11
	2.3  Previous Work    12
	2.4  The Smooth Injective Map Recognizer (SIMR)    19
		2.4.1  Overview    19
		2.4.2  Point Generation    21
		2.4.3  Noise Filter    26
		2.4.4  Point Selection    28
		2.4.5  Reduction of the Search Space    29
		2.4.6  Enhancements    31
	2.5  Parameter Optimization    34
	2.6  Evaluation    35
	2.7  Implementation of SIMR for New Language Pairs    43
		2.7.1  Step 1: Construct Matching Predicate    43
		2.7.2  Step 2: Construct Axis Generators    44
		2.7.3  Step 3: Re-optimize Parameters    47
	2.8  Conclusion    48
 Chapter 3  Application: Alignment    64
	3.1  Introduction    65
	3.2  Correspondence is Richer than Alignment    66
	3.3  The Geometric Segment Alignment (GSA) Algorithm    67
	3.4  Evaluation    70
	3.5  Conclusion    73
 Chapter 4  Application: Automatic Detection of Omissions in Translations    76
	4.1  Introduction    77
	4.2  The Basic Method    78
	4.3  Noise-Free Bitext Maps    80
	4.4  A Translators' Tool    83
	4.5  Noisy Bitext Maps    84
	4.6  ADOMIT    85
	4.7  Simulation of Omissions    88
	4.8  Evaluation    91
	4.9  Conclusion    93

Part II   The Type-Token Interface    103
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 Chapter 5  Models of Co-occurrence    104
	5.1  Introduction    105
	5.2  Relevant Regions of the Bitext Space    106
	5.3  Co-occurrence Counting Methods    108
	5.4  Language-Specific Filters    112
	5.5  Conclusion    114
 Chapter 6  Manual Annotation of Translational Equivalence    119
	6.1  Introduction    120
	6.2  The Gold Standard Bitext    122
	6.3  The Blinker Annotation Tool    125
	6.4  Methods for Increasing Reliability    126
	6.5  Inter-Annotator Agreement    130
	6.6  Conclusion    137

Part III  Translational Equivalence among Word Types    143
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 Chapter 7  Word-to-Word Models of Translational Equivalence    144
	7.1  Introduction    145
	7.2  Translation Model Decomposition    148
	7.3  The One-to-One Assumption    155
	7.4  Previous Work    156
		7.4.1  Non-Probabilistic Translation Lexicons    156
		7.4.2  Re-estimated Sequence-to-Sequence Translation Models    161
		7.4.3  Re-estimated Bag-to-Bag Translation Models    168
	7.5  Parameter Estimation    170
		7.5.1  Method A: The Competitive Linking Algorithm    174
		7.5.2  Method B: Improved Estimation Using an Explicit Noise Model    178
		7.5.3  Method C: Improved Estimation Using Pre-Existing Word Classes    184
	7.6  Effects of Sparse Data    185
	7.7  Evaluation    189
		7.7.1  Evaluation at the Token Level    189
		7.7.2  Evaluation at the Type Level    207
	7.8  Application to MT Lexicon Development    213
	7.9  Conclusion    218
 Chapter 8  Automatic Discovery of Non-Compositional Compounds    228
	8.1  Introduction    229
	8.2  Objective Functions    232
	8.3  Search    234
	8.4  Predictive Value Functions    235
	8.5  Iteration    237
	8.6  Credit Estimation    243
	8.7  Single-Best Translation    247
	8.8  Experiments    249
	8.9  Related Work    256
	8.10  Conclusion    260
 Chapter 9  Sense-to-Sense Models of Translational Equivalence    271
	9.1  Introduction    272
	9.2  Previous Work    274
	9.3  Formulation of the Problem    278
	9.4  A Solution    280
		9.4.1  Noise Filters    280
		9.4.2  The SenseClusters Algorithm    282
	9.5  An Application    285
	9.6  Experiments    288
		9.6.1  Quantitative Results    288
		9.6.2  Qualitative Results    293
	9.7  Conclusion    295
 Chapter 10  Summary and Outlook    305

Appendix A  Annotation Style Guide for the Blinker Project    319
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 A.1  General Guidelines    319
	A.1.1  Omissions in Translation    320
	A.1.2  Phrasal Correspondence    321
 A.2  Detailed Guidelines    323
	A.2.1  Idioms and Near Idioms    324
	A.2.2  Referring Expressions    324
	A.2.3  Verbs    326
	A.2.4  Prepositions    330
	A.2.5  Determiners    332
	A.2.6  Punctuation    334

Bibliography    369