|
|
|
|
3 | (10) |
|
|
11 | (2) |
|
|
13 | (28) |
|
2.1 Logos Model Beginnings |
|
|
13 | (3) |
|
2.2 The Advent of Statistical MT |
|
|
16 | (4) |
|
2.2.1 Pattern-Based Processes in SMT and Logos Model |
|
|
17 | (3) |
|
2.3 Overview of Logos Model Translation Process |
|
|
20 | (5) |
|
2.4 Psycholinguistic and Neurolinguistic Assumptions |
|
|
25 | (2) |
|
|
25 | (1) |
|
|
26 | (1) |
|
|
26 | (1) |
|
2.5 On Language and Grammar |
|
|
27 | (5) |
|
2.5.1 The Origin and Nature of Grammar |
|
|
28 | (1) |
|
2.5.2 Language, Grammar and Associative Memory |
|
|
29 | (1) |
|
2.5.3 In Principio Erat Verbum |
|
|
30 | (2) |
|
2.6 A Note About Neural MT (NMT) |
|
|
32 | (1) |
|
|
33 | (8) |
|
|
34 | (1) |
|
|
34 | (3) |
|
|
37 | (1) |
|
|
37 | (1) |
|
|
38 | (3) |
|
3 Language and Ambiguity: Psycholinguistic Perspectives |
|
|
41 | (24) |
|
|
41 | (3) |
|
3.2 Language Acquisition and Translation |
|
|
44 | (7) |
|
3.2.1 Linguistic Processes Involved in Second Language Acquisition |
|
|
45 | (4) |
|
|
49 | (2) |
|
3.3 Psycholinguistic Bases of Language Skills |
|
|
51 | (3) |
|
|
51 | (1) |
|
|
52 | (1) |
|
|
52 | (2) |
|
3.4 Practical Implications for MT |
|
|
54 | (4) |
|
3.4.1 Semantico-Syntactic Solutions to the Problem of Ambiguity in MT |
|
|
55 | (3) |
|
3.5 Psycholinguisitcs in a Machine |
|
|
58 | (5) |
|
3.5.1 Generate Target Translation |
|
|
61 | (1) |
|
3.5.2 OpenLogos and Logos Model |
|
|
61 | (2) |
|
|
63 | (2) |
|
|
63 | (2) |
|
4 Language and Complexity: Neurolinguistic Perspectives |
|
|
65 | (34) |
|
4.1 On Cognitive Complexity |
|
|
65 | (5) |
|
4.2 A Role for Semantic Abstraction and Generalization |
|
|
70 | (3) |
|
4.3 Connectionism and Brain Simulation |
|
|
73 | (3) |
|
4.4 Logos Model As a Neural Network |
|
|
76 | (3) |
|
4.5 Language Processing in the Brain |
|
|
79 | (10) |
|
4.5.1 Cortical Circuits and Logos Model |
|
|
80 | (1) |
|
4.5.2 Hippocampus and Logos Model |
|
|
81 | (8) |
|
4.6 MT Performance and Underlying Competence |
|
|
89 | (2) |
|
|
91 | (8) |
|
|
91 | (1) |
|
|
91 | (1) |
|
|
92 | (2) |
|
|
94 | (1) |
|
|
94 | (1) |
|
|
94 | (1) |
|
|
95 | (1) |
|
|
95 | (1) |
|
|
96 | (1) |
|
|
96 | (3) |
|
5 Syntax and Semantics: Dichotomy Versus Integration |
|
|
99 | (28) |
|
5.1 Syntax Versus Semantics: Is There a Third, Semantico-Syntactic Perspective? |
|
|
99 | (7) |
|
5.2 Recent Views of the Cerebral Process |
|
|
106 | (2) |
|
5.3 Syntax and Semantics: How Do They Relate? |
|
|
108 | (5) |
|
|
113 | (14) |
|
|
114 | (1) |
|
|
114 | (1) |
|
|
115 | (1) |
|
|
115 | (1) |
|
|
116 | (1) |
|
|
117 | (1) |
|
|
118 | (1) |
|
|
119 | (4) |
|
|
123 | (4) |
|
6 Logos Model: Design and Performance |
|
|
127 | (36) |
|
6.1 The Translation Problem |
|
|
127 | (3) |
|
6.1.1 Five Fundamental Design Decisions |
|
|
129 | (1) |
|
6.2 How Do You Represent Natural Language? |
|
|
130 | (3) |
|
6.2.1 Effectiveness of SAL for Deterministic Parsing |
|
|
131 | (2) |
|
6.3 How Do You Store Linguistic Knowledge? |
|
|
133 | (4) |
|
|
133 | (1) |
|
6.3.2 Logos Model Lexicon |
|
|
133 | (1) |
|
6.3.3 The Pattern-Rule Database |
|
|
134 | (3) |
|
6.4 How Do You Apply Stored Knowledge to the Input Stream? |
|
|
137 | (16) |
|
6.4.1 Modules RES 1 and RES2 (R1 and R2) |
|
|
138 | (2) |
|
|
140 | (2) |
|
|
142 | (3) |
|
|
145 | (4) |
|
|
149 | (4) |
|
6.5 How Do You Effect Target Generation? |
|
|
153 | (1) |
|
|
153 | (1) |
|
6.6 How Do You Cope with Complexity? |
|
|
153 | (4) |
|
6.6.1 A Final Illustration |
|
|
154 | (3) |
|
|
157 | (6) |
|
|
157 | (1) |
|
|
157 | (1) |
|
|
158 | (4) |
|
|
162 | (1) |
|
7 Some Limits on Translation Quality |
|
|
163 | (10) |
|
|
164 | (2) |
|
|
166 | (1) |
|
7.3 Other Translation Examples |
|
|
167 | (1) |
|
7.4 Balancing the Picture |
|
|
168 | (1) |
|
|
169 | (4) |
|
|
171 | (2) |
|
8 Deep Learning MT and Logos Model |
|
|
173 | (32) |
|
8.1 Points of Similarity and Differences |
|
|
174 | (5) |
|
8.2 Deep Learning, Logos Model and the Brain |
|
|
179 | (2) |
|
|
181 | (5) |
|
8.4 The Hippocampus and Continual Learning |
|
|
186 | (7) |
|
|
193 | (3) |
|
8.6 A Final Demonstration |
|
|
196 | (9) |
|
|
200 | (5) |
|
|
|
9 The SAL Representation Language |
|
|
205 | |
|
|
205 | (1) |
|
|
206 | (1) |
|
9.2.1 Open Classes (Table 9.1) |
|
|
206 | (1) |
|
9.2.2 Closed Classes (Table 9.2) |
|
|
206 | (1) |
|
|
207 | (11) |
|
|
209 | (2) |
|
|
211 | (1) |
|
|
212 | (1) |
|
|
213 | (1) |
|
|
214 | (1) |
|
|
215 | (1) |
|
|
216 | (1) |
|
9.3.8 Information and Time Nouns |
|
|
217 | (1) |
|
|
218 | (15) |
|
9.4.1 The Intransitive-Transitive Verb Spectrum |
|
|
219 | (3) |
|
|
222 | (1) |
|
9.4.3 Subjective Transitive Verbs |
|
|
223 | (1) |
|
9.4.4 Reciprocal Transitive Verbs |
|
|
224 | (1) |
|
|
225 | (1) |
|
9.4.6 Objective Transitive Verbs |
|
|
226 | (1) |
|
|
227 | (1) |
|
9.4.8 Simple Preverbal Verbs |
|
|
228 | (1) |
|
9.4.9 Preverbal Complex Verbs |
|
|
229 | (1) |
|
9.4.10 Preverbal-Preclausal Verbs |
|
|
230 | (1) |
|
|
231 | (2) |
|
9.5 SAL Adjectives (WC 4) |
|
|
233 | (5) |
|
9.5.1 Preclausal/Preverbal Adjectives |
|
|
234 | (1) |
|
9.5.2 Preverbal Adjectives |
|
|
235 | (1) |
|
9.5.3 Adverbial Adjectives |
|
|
236 | (1) |
|
9.5.4 Non-adverbial Adjectives |
|
|
237 | (1) |
|
9.6 SAL Adverbs (WC 3 and WC 6) |
|
|
238 | |
|
9.6.1 Locative Adverbs have the Following Supersets |
|
|
238 | (1) |
|
9.6.2 Non-locative Adverb have the Following Supersets |
|
|
239 | (1) |
|
|
240 | (1) |
|
|
240 | |