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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Text-to-Text Transformer 2 | 3 | 본 repository에서는 Google의 [T5(T5: Text-To-Text Transfer Transformer)](https://arxiv.org/abs/1910.10683)의 text-to-text 형태로 한국어 QA Task를 위한 Transformer 모델입니다. 전체 모델의 아키텍처는 기본 Transformer 모델을 사용했습니다. 4 | 5 | ``` 6 | pip install transformer-korean 7 | ``` 8 | 9 | * 2019.12.25, version 0.0.3 : load_data_txt, load_data_csv 오류 수정 10 | * 2019.12.23, version 0.0.1 : 최초 릴리즈 11 |
12 | 13 | 14 | ## 0. Pre-training Model 15 | 16 | * Text-to-Text Transformer-Base, Korean Model: 12-layer, 768-hidden, 12-heads(비공개) 17 | * Text-to-Text Transformer-Small, Korean Model: 6-layer, 512-hidden, 8-heads(비공개) 18 | 19 | 20 | > Base This is our baseline model, whose hyperparameters are described in Section 3.1.1. It has roughly 220million parameters. 21 | > Small. We consider a smaller model, which scales the baseline down by using dmodel= 512, dff= 2,048, 8-headed attention, and only 6layers each in the encoder and decoder. This varianthas about 60million parameters. 22 | 23 | 24 | ## 1. Pre-training 25 | 26 | ### 1.1 Unsupervised objective 27 | 28 | T5 논문에서 가장 성능이 잘 나온다고 서술된 BERT Style Objective로 문장을 구성하여, Pre-training 하도록 구성했습니다. BERT와 동일하게 입력 문장의 15%를 Random 하게 마스킹 처리했습니다. 마스킹 대상의 80%는 토큰으로 대체하며, 10%는 사전 내 임의의 토큰으로 나머지 10%는 원래의 단어를 그대로 사용했습니다. 29 | 30 | 31 | 32 | 33 | 34 | ### 1.2 문장 예시 35 | 36 | ``` 37 | Input 문장 : 1900년, 푸치니의 오페라 토스카로 '다양하게' 각색되었다. (BERT Style) 38 | 39 | 40 | Target 문장 : 1900년, 사르두의 연극은 푸치니의 오페라 토스카로 새롭게 각색되었다. (original text) 41 | ``` 42 | 43 | 44 | ### 1.3 Unlabeld dataset 45 | 학습 데이터는 **한국어 위키데이터(2019.01 dump file 기준, 약 350만 문장)** 을 사용하여 학습을 진행했으며, 학습 문장 구성은 아래와 같습니다. 46 | 47 | ~~~ 48 | 라 토스카(La Tosca)는 1887년에 프랑스 극작가 사르두가 배우 사라 베르나르를 위해 만든 작품이다. 49 | 1887년 파리에서 처음 상연되었다. 50 | 1990년 베르나르를 주인공으로 미국 뉴욕에서 재상연되었다. 51 | 1800년 6월 중순의 이탈리아 로마를 배경으로 하며, 당시의 시대적 상황 하에서 이야기가 전개된다. 52 | 1900년, 사르두의 연극은 푸치니의 오페라 토스카로 새롭게 각색되었다. 53 | 베르디는 사드루의 각본에서 "갑작스런 종결" 부분을 수정할 것을 권하지만, 사르루는 이를 거절한다. 54 | 후에, 푸치니 또한 사르두의 각본에서 "갑작스런 종결부분"을 수정할 것을 제안하지만 끝내 사르두를 설득하지 못했다. 55 | ~~~ 56 | 57 | 58 | ### 1.4 학습 예 59 | 60 | ```python 61 | from transformer_korean.run_training import Trainer 62 | from transformer_korean.transformer import Transformer 63 | from transformer_korean.preprocess import DataProcessor 64 | from transformer_korean.custom_scheduler import CustomSchedule 65 | import tensorflow as tf 66 | 67 | path = "ko-wiki_20190621.txt" 68 | # Data Processing 69 | print('Loading Pre-training data') 70 | data_preprocess = DataProcessor(txt_path=path, 71 | batch_size=64, 72 | pre_train=True, 73 | max_length=128) 74 | train = data_preprocess.load_data_txt() 75 | 76 | print('Loading Vocab File') 77 | vocab = data_preprocess.load_vocab_file(vocab_filename="vocab") 78 | 79 | print('Create train dataset') 80 | train_dataset = data_preprocess.preprocess(train) 81 | 82 | EPOCHS = 100 83 | num_layers = 6 84 | d_model = 128 85 | dff = 512 86 | num_heads = 8 87 | vocab_size = vocab.vocab_size 88 | dropout_rate = 0.1 89 | encoder_activation = 'gelu' 90 | decoder_activation = 'relu' 91 | 92 | # Custom Scheduler 93 | learning_rate = CustomSchedule(d_model, warmup_steps=4000) 94 | optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9) 95 | 96 | # Transformer 97 | transformer = Transformer(d_model=d_model, 98 | num_heads=num_heads, 99 | num_layers=num_layers, 100 | vocab_size=vocab_size, 101 | dff=dff, 102 | enc_activation=encoder_activation, 103 | dec_activation=decoder_activation, 104 | rate=dropout_rate) 105 | 106 | # Trainer 107 | trainer = Trainer(train_dataset=train_dataset, 108 | learning_rate=learning_rate, 109 | optimizer=optimizer, 110 | transformer=transformer, 111 | epochs=EPOCHS, 112 | checkpoint_path='./checkpoints/', 113 | load_checkpoints=False, 114 | save_checkpoints_epochs=10) 115 | trainer.train() 116 | ``` 117 | 118 | 119 | ## 2.Fine-Tuning(QA Task) 120 | 121 | ### 2.1 Labeld dataset 122 | QA Task를 위해 한국어 QA Dataset인 [KorQuAD 1.1](https://korquad.github.io/category/1.0_KOR.html)을 사용하여 Fine-Tuning 하도록 구성했습니다. 데이터 구성은 아래와 같습니다. input은 question, target은 answer가 되도록 했습니다. 123 | 124 | ~~~ 125 | Q 126 | 바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가? 127 | 바그너는 교향곡 작곡을 어디까지 쓴 뒤에 중단했는가? 128 | 바그너가 파우스트 서곡을 쓸 때 어떤 곡의 영향을 받았는가? 129 | 1839년 바그너가 교향곡의 소재로 쓰려고 했던 책은? 130 | 파우스트 서곡의 라단조 조성이 영향을 받은 베토벤의 곡은? 131 | ~~~ 132 | 133 | 134 | ~~~ 135 | A 136 | 교향곡 137 | 1악장 138 | 베토벤의 교향곡 9번 139 | 파우스트 140 | 합창교향곡 141 | ~~~ 142 | 143 | 144 | ### 2.2 학습 예 145 | 146 | ```python 147 | from transformer_korean.run_training import Trainer 148 | from transformer_korean.transformer import Transformer 149 | from transformer_korean.preprocess import DataProcessor 150 | from transformer_korean.custom_scheduler import CustomSchedule 151 | 152 | import tensorflow as tf 153 | 154 | question = "KorQuAD_train_q.csv" 155 | answer = "KorQuAD_train_a.csv" 156 | 157 | # Data Processing 158 | print('Loading fine-tuning data') 159 | data_preprocess = DataProcessor(csv_path=[question, answer], 160 | batch_size=64, 161 | pre_train=False, 162 | max_length= 128) 163 | train = data_preprocess.load_data_csv() 164 | 165 | print('Loading Vocab File') 166 | vocab = data_preprocess.load_vocab_file(vocab_filename="vocab") 167 | 168 | print('Create train dataset') 169 | train_dataset = data_preprocess.preprocess(train) 170 | 171 | EPOCHS = 100 172 | num_layers = 6 173 | d_model = 128 174 | dff = 512 175 | num_heads = 8 176 | vocab_size = vocab.vocab_size 177 | dropout_rate = 0.1 178 | encoder_activation = 'gelu' 179 | decoder_activation = 'relu' 180 | 181 | # Custom Scheduler 182 | learning_rate = CustomSchedule(d_model, warmup_steps=4000) 183 | optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9) 184 | 185 | # Transformer 186 | transformer = Transformer(d_model=d_model, 187 | num_heads=num_heads, 188 | num_layers=num_layers, 189 | vocab_size=vocab_size, 190 | dff=dff, 191 | enc_activation = encoder_activation, 192 | dec_activation = decoder_activation, 193 | rate=dropout_rate) 194 | 195 | # Trainer 196 | trainer = Trainer(train_dataset=train_dataset, 197 | learning_rate=learning_rate, 198 | optimizer=optimizer, 199 | transformer=transformer, 200 | epochs=EPOCHS, 201 | checkpoint_path='./checkpoints/', 202 | load_checkpoints=True, 203 | save_checkpoints_epochs=10) 204 | 205 | trainer.train() 206 | ``` 207 | 208 | 209 | ## 3. Activation Function 210 | 기본 relu activation function 외에 4개의 activation function 추가하였으며, Encoder와 Decoder 블럭에 서로 다른 activation function이 사용 가능하도록 했습니다 211 | 212 | 1. gelu 213 | ```python 214 | def gelu(x): 215 | cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))) 216 | return x * cdf 217 | ``` 218 | 219 | 2. swish 220 | ```python 221 | def swish(x): 222 | return x * tf.nn.sigmoid(x) 223 | ``` 224 | 225 | 3. swish_beta 226 | ```python 227 | def swish_beta(x): 228 | beta=tf.Variable(initial_value=1.0,trainable=True, name='swish_beta') 229 | return x * tf.nn.sigmoid(beta * x) #trainable parameter beta 230 | ``` 231 | 232 | 4. [mish](https://github.com/digantamisra98/Mish) 233 | ```python 234 | def mish(x): 235 | return x * tf.math.tanh(tf.math.softplus(x)) 236 | ``` 237 | 238 | 239 | ## 4. Requirement 240 | Python == 3.x
241 | tensorflow >=2.0
242 | tensorflow-datasets >= 1.3.2
243 | pandas >= 0.24.2
244 | numpy >= 1.16.3
245 | six>=1.12.0 246 | 247 | 248 | ## 5. To-Do 249 | - [x] TPU, Multi-GPU 지원 예정 250 | - [X] Dropout 수정 예정 251 | - [X] Predict 모듈 추가 예정 252 | 253 | ## 6. Reference 254 | 255 | * [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)
256 | * [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
257 | * [Chatbot using Tensorflow (Model is transformer) ko](https://github.com/changwookjun/Transformer)
258 | * [TensorFlow code and pre-trained models for BERT](https://github.com/google-research/bert) 259 | * [TransformerModel](https://github.com/zbloss/TransformerModel) 260 | * [Transformer model for language understanding](https://www.tensorflow.org/tutorials/text/transformer) 261 | 262 | -------------------------------------------------------------------------------- /sample_vocab.subwords: -------------------------------------------------------------------------------- 1 | ### SubwordTextEncoder 2 | ### Metadata: {} 3 | '' 4 | ', ' 5 | '사카이_' 6 | '있다' 7 | ' "' 8 | '이_' 9 | '오사카_' 10 | '있는_' 11 | '등_' 12 | '그_' 13 | '》(' 14 | '서울지방검찰청_' 15 | '라인_' 16 | '는_' 17 | '구속했다' 18 | '고_' 19 | '같은_' 20 | '하나이다' 21 | '중_' 22 | '전원_' 23 | '안강민은_' 24 | '시의_' 25 | '수사를_' 26 | '센보쿠_' 27 | '선' 28 | '받은_' 29 | '되었다' 30 | '구의_' 31 | '구_' 32 | '구' 33 | '강의_' 34 | '8월_' 35 | '()' 36 | '혹은_' 37 | '일본_' 38 | '이시즈_' 39 | '의해_' 40 | '역_' 41 | '시를_' 42 | '시_' 43 | '사건을_' 44 | '불가능한_' 45 | '부도_' 46 | '때문에_' 47 | '대한_' 48 | '달리고_' 49 | '니시_' 50 | '난카이_' 51 | '구성하는_' 52 | '경작이_' 53 | '강과_' 54 | '강_' 55 | '7개_' 56 | '5월_' 57 | '2월_' 58 | '), 《' 59 | '), ' 60 | ' 《' 61 | '흘러_' 62 | '흔히_' 63 | '홍수_' 64 | '한와_' 65 | '하면서_' 66 | '풍요로운_' 67 | '통해_' 68 | '초이스_' 69 | '지을_' 70 | '조치를_' 71 | '조작_' 72 | '전_' 73 | '적이_' 74 | '있을_' 75 | '있으면서_' 76 | '인해_' 77 | '이러한_' 78 | '의혹_' 79 | '위치하고_' 80 | '예부터_' 81 | '에_' 82 | '없는_' 83 | '수_' 84 | '세계대전_' 85 | '사건_' 86 | '미나미_' 87 | '모젤_' 88 | '말했다' 89 | '많이_' 90 | '많은_' 91 | '라는_' 92 | '두_' 93 | '도시로_' 94 | '대해_' 95 | '당시_' 96 | '농지의_' 97 | '농지로_' 98 | '농업_' 99 | '국가보안법_' 100 | '구가_' 101 | '검사로_' 102 | 'JR_' 103 | '4월_' 104 | '4명을_' 105 | '2004' 106 | '후보_' 107 | '후_' 108 | '홍석현_' 109 | '혐의로_' 110 | '함께_' 111 | '한번_' 112 | '한다' 113 | '한_' 114 | '하이모어는_' 115 | '하며' 116 | '하나_' 117 | '폭파_' 118 | '평민당_' 119 | '평당_' 120 | '펼쳐져_' 121 | '파는_' 122 | '특수부_' 123 | '크리틱스_' 124 | '취락이_' 125 | '출장소_' 126 | '추계_' 127 | '최우수_' 128 | '차례_' 129 | '지정_' 130 | '중심으로_' 131 | '중수부장_' 132 | '줄어들게_' 133 | '주택지가_' 134 | '주의_' 135 | '존재하고_' 136 | '제2차_' 137 | '정령_' 138 | '전국_' 139 | '잠시_' 140 | '있으며' 141 | '있었다' 142 | '있던_' 143 | '있고_' 144 | '일이_' 145 | '일면식도_' 146 | '인구가_' 147 | '인구_' 148 | '이행해_' 149 | '이름으로_' 150 | '이런_' 151 | '의원의_' 152 | '위치한다' 153 | '위반으로_' 154 | '위반_' 155 | '요새가_' 156 | '역임한_' 157 | '없다' 158 | '없고' 159 | '안기부의_' 160 | '안강민_' 161 | '싸게_' 162 | '식물의_' 163 | '식량_' 164 | '시내_' 165 | '시가_' 166 | '수로와_' 167 | '선이_' 168 | '선_' 169 | '서울시_' 170 | '서경원_' 171 | '새로운_' 172 | '사실은_' 173 | '사례들이다' 174 | '사람이_' 175 | '사들여_' 176 | '사건이_' 177 | '비료' 178 | '불구속기소했다' 179 | '북한의_' 180 | '북쪽으로_' 181 | '부회장과는_' 182 | '부른다' 183 | '변한_' 184 | '베이츠_' 185 | '법적_' 186 | '발생했다는_' 187 | '및_' 188 | '밀입북_' 189 | '메마른_' 190 | '맡았으나_' 191 | '많아_' 192 | '많다' 193 | '만난_' 194 | '로_' 195 | '또한_' 196 | '떡값을_' 197 | '때문이다' 198 | '때까지_' 199 | '땅은_' 200 | '등이_' 201 | '등에서_' 202 | '드라마_' 203 | '둑이_' 204 | '되어_' 205 | '동안_' 206 | '독일_' 207 | '대해' 208 | '대한항공_' 209 | '대표_' 210 | '대통령_' 211 | '대사와는_' 212 | '대구지방검찰청_' 213 | '대검찰청_' 214 | '다카이시_' 215 | '다음은_' 216 | '다음_' 217 | '다른_' 218 | '뉴타운이_' 219 | '뉴타운의_' 220 | '높은_' 221 | '농지는_' 222 | '농지가_' 223 | '농사를_' 224 | '논란이_' 225 | '너무_' 226 | '내가_' 227 | '남쪽에서_' 228 | '남서로_' 229 | '김현희를_' 230 | '김대중_' 231 | '기타노다_' 232 | '기타_' 233 | '기원전_' 234 | '그러나_' 235 | '그는_' 236 | '국회의원_' 237 | '국도_' 238 | '구역이_' 239 | '구에_' 240 | '관내의_' 241 | '공장' 242 | '공식석상에서_' 243 | '경우가_' 244 | '경우' 245 | '것은_' 246 | '것으로_' 247 | '검증위원회_' 248 | '간첩_' 249 | '각각_' 250 | '가장_' 251 | '가운데_' 252 | '–' 253 | '·' 254 | '858편_' 255 | '3월에_' 256 | '26호선' 257 | '25일에_' 258 | '2009년_' 259 | '2006년_' 260 | '2006' 261 | '2005' 262 | '1일' 263 | '1999년_' 264 | '1989년_' 265 | '1988년_' 266 | '1987년_' 267 | '1941년_' 268 | '11월_' 269 | ').' 270 | '" ' 271 | ' ~ )' 272 | ' (' 273 | ' '' 274 | '％ ' 275 | '히가시_' 276 | '흐르고_' 277 | '훈' 278 | '후보의_' 279 | '후보로_' 280 | '후로_' 281 | '회장_' 282 | '회의를_' 283 | '회귀를_' 284 | '황무지로_' 285 | '황무지가_' 286 | '황무지' 287 | '활동을_' 288 | '확정되자_' 289 | '확정되었던_' 290 | '확산과_' 291 | '확보하면서_' 292 | '화창한_' 293 | '홍조근정훈장을_' 294 | '홍수로_' 295 | '혹은' 296 | '형성되고_' 297 | '형사문책유보_' 298 | '형사고소하여_' 299 | '협곡으로_' 300 | '혐의에_' 301 | '현재도_' 302 | '현재는_' 303 | '현대에는_' 304 | '행정_' 305 | '행사하기로_' 306 | '했으며' 307 | '했던_' 308 | '햇빛이_' 309 | '핵심_' 310 | '해주겠다며_' 311 | '해안을_' 312 | '해서_' 313 | '해도_' 314 | '항상_' 315 | '합류해_' 316 | '합류한다' 317 | '합류점에서도_' 318 | '합류점에는_' 319 | '합류점에_' 320 | '합격하여_' 321 | '한적한_' 322 | '한마디와_' 323 | '한마디를_' 324 | '한다_' 325 | '한난_' 326 | '한나라당이_' 327 | '한나라당의_' 328 | '한나라당_' 329 | '한국교회사회선교협의회_' 330 | '한계를_' 331 | '한' 332 | '학생단체의_' 333 | '하천의_' 334 | '하천_' 335 | '하지만' 336 | '하이모어' 337 | '하였다' 338 | '하쓰시바_' 339 | '하면서' 340 | '하는데_' 341 | '하나인_' 342 | '하나가_' 343 | '하기_' 344 | '필요한_' 345 | '필요로_' 346 | '필름_' 347 | '피플_' 348 | '피의사실공표죄_' 349 | '피고인_' 350 | '프로이센의_' 351 | '프로이센으로_' 352 | '프레디' 353 | '프랑스에_' 354 | '풍부한_' 355 | '표정_' 356 | '표백_' 357 | '포함해_' 358 | '포함한_' 359 | '포함되어_' 360 | '포섭돼_' 361 | '포상을_' 362 | '평야이다' 363 | '평민당' 364 | '페일리아파트_' 365 | '퍼져있는_' 366 | '패소하였다' 367 | '팔았던_' 368 | '팔아_' 369 | '파괴되었다' 370 | '특히_' 371 | '특별검사_' 372 | '특가법_' 373 | '트리어_' 374 | '통일의_' 375 | '통과한다' 376 | '통과하는_' 377 | '통과하고_' 378 | '토킹_' 379 | '토지의_' 380 | '토지가_' 381 | '토지_' 382 | '토스트' 383 | '토머스' 384 | '텔레비전상_' 385 | '테러로_' 386 | '태어난_' 387 | '클라우디우스_' 388 | '크게_' 389 | '쾌적한_' 390 | '콘센트_' 391 | '코블렌츠' 392 | '코미디_' 393 | '케이블_' 394 | '카스텔룸아푸드콘플루엔테스' 395 | '침적물은_' 396 | '침식에_' 397 | '취할_' 398 | '취하겠다고_' 399 | '취하겠다' 400 | '춥거나' 401 | '출연했다' 402 | '출연했고' 403 | '추위와_' 404 | '추위에' 405 | '추월당했다' 406 | '최종적으로_' 407 | '최열곤_' 408 | '최다_' 409 | '최근에는_' 410 | '총재에_' 411 | '총재' 412 | '초콜릿_' 413 | '초기의_' 414 | '체제수호를_' 415 | '철야_' 416 | '철도는_' 417 | '처리_' 418 | '챙긴_' 419 | '책임자를_' 420 | '찾아서' 421 | '찾겠다_' 422 | '찰리와_' 423 | '착수하고_' 424 | '차지한다' 425 | '차명주식_' 426 | '차량_' 427 | '징역1년에서_' 428 | '집약적인_' 429 | '집안이다' 430 | '집안에서_' 431 | '질에_' 432 | '질소_' 433 | '진행되고_' 434 | '진실인_' 435 | '진실을_' 436 | '진술한_' 437 | '진상규명을_' 438 | '직후_' 439 | '지휘한_' 440 | '지하철' 441 | '지질_' 442 | '지역이_' 443 | '지역의_' 444 | '지역은_' 445 | '지역으로서_' 446 | '지역에서는_' 447 | '지역에_' 448 | '지역과_' 449 | '지소_' 450 | '지소' 451 | '지배를_' 452 | '지목한_' 453 | '지명의_' 454 | '지명_' 455 | '지대로_' 456 | '지낸_' 457 | '지금_' 458 | '지구로_' 459 | '증가하기도_' 460 | '증가하고_' 461 | '중형이_' 462 | '중턱에서_' 463 | '중앙수사부장으로_' 464 | '중앙부를_' 465 | '중심지이며' 466 | '중심지였고' 467 | '중심지역은_' 468 | '중심지가_' 469 | '중류_' 470 | '중동부에_' 471 | '줄인다' 472 | '죽게_' 473 | '주택지_' 474 | '주택지' 475 | '주택가로서_' 476 | '주택_' 477 | '주적을_' 478 | '주장했다' 479 | '주요_' 480 | '주었으나_' 481 | '주변이다' 482 | '주변이_' 483 | '주변에는_' 484 | '주변에_' 485 | '주변도_' 486 | '주모자_' 487 | '주도였다' 488 | '주도가_' 489 | '주기도_' 490 | '좋아하는_' 491 | '종류의_' 492 | '종결됐다' 493 | '좁게_' 494 | '졸업하고_' 495 | '존재의의마저_' 496 | '존재를_' 497 | '존경합니다' 498 | '조치' 499 | '조절을_' 500 | '조절_' 501 | '조작이라는_' 502 | '조사했다' 503 | '조밀한_' 504 | '제약을_' 505 | '제방과_' 506 | '제기되고_' 507 | '제국_' 508 | '제8회_' 509 | '제37대_' 510 | '제2한와_' 511 | '제1차_' 512 | '제1부장검사로_' 513 | '제1부_' 514 | '제17대_' 515 | '정치적_' 516 | '정치보복으로_' 517 | '정비된_' 518 | '정부가_' 519 | '정밀_' 520 | '정면_' 521 | '정기적으로_' 522 | '점재한다' 523 | '젊은_' 524 | '절정인_' 525 | '절연은_' 526 | '절연_' 527 | '전철이_' 528 | '전직_' 529 | '전역이_' 530 | '전민련' 531 | '전매해_' 532 | '전대협_' 533 | '전답도_' 534 | '전답_' 535 | '전노협' 536 | '전기제품을_' 537 | '전기고_' 538 | '적운으로_' 539 | '적어도_' 540 | '저지가_' 541 | '쟁기질하다' 542 | '재판이_' 543 | '재직하던_' 544 | '재조사를_' 545 | '재조사_' 546 | '재야단체_' 547 | '재야' 548 | '재심에서_' 549 | '재수사하는_' 550 | '재벌총수_' 551 | '재배하는_' 552 | '재무국장으로_' 553 | '재동한_' 554 | '장치' 555 | '작은_' 556 | '자체의_' 557 | '자원_' 558 | '자신을_' 559 | '자료는_' 560 | '자동차등록관계_' 561 | '자급자족_' 562 | '잉글랜드의_' 563 | '있은_' 564 | '있으면서' 565 | '있었으며_' 566 | '있었던_' 567 | '있도록_' 568 | '있다가_' 569 | '있는데' 570 | '임용되었다' 571 | '임야와_' 572 | '임야에_' 573 | '임야_' 574 | '일체를_' 575 | '일어나는_' 576 | '일본의_' 577 | '일반적인_' 578 | '일반미와_' 579 | '일대는_' 580 | '인한_' 581 | '인식을_' 582 | '인사청탁_' 583 | '인사를_' 584 | '인기가_' 585 | '인구에서는_' 586 | '인구에서_' 587 | '인구를_' 588 | '인간의_' 589 | '인간과_' 590 | '이후에는_' 591 | '이학수_' 592 | '이첩받아_' 593 | '이철용_' 594 | '이즈미_' 595 | '이전에_' 596 | '이유로_' 597 | '이유는_' 598 | '이와_' 599 | '이명박_' 600 | '이름을_' 601 | '이르게_' 602 | '이루어지고_' 603 | '이루어_' 604 | '이라며' 605 | '이라고도_' 606 | '이라고_' 607 | '이건희_' 608 | '의혹을_' 609 | '의해서_' 610 | '의한_' 611 | '의원이_' 612 | '의원으로_' 613 | '의미하는_' 614 | '을_' 615 | '은_' 616 | '육지와_' 617 | '육군_' 618 | '유지를_' 619 | '유일한_' 620 | '유인물' 621 | '유원호를_' 622 | '유원엔지니어링_' 623 | '유수의_' 624 | '유산으로_' 625 | '유목은_' 626 | '유명한_' 627 | '유래' 628 | '유네스코_' 629 | '유공자에게_' 630 | '위해_' 631 | '위조' 632 | '위원장을_' 633 | '위원에_' 634 | '위상_' 635 | '위반' 636 | '위민_' 637 | '위계에_' 638 | '원내총무' 639 | '운전사_' 640 | '운영위원회_' 641 | '우물' 642 | '우리_' 643 | '용어이다' 644 | '요즘_' 645 | '요인_' 646 | '요구한다' 647 | '왼쪽에_' 648 | '외의_' 649 | '외에_' 650 | '외부의_' 651 | '외교관_' 652 | '완수한_' 653 | '와다_' 654 | '와_' 655 | '와' 656 | '올림픽선수촌_' 657 | '온실은_' 658 | '오토리_' 659 | '오염되었거나_' 660 | '오미노처럼_' 661 | '오미노_' 662 | '오랫동안_' 663 | '오래_' 664 | '오동철_' 665 | '오구리_' 666 | '옛_' 667 | '영화상_' 668 | '영화' 669 | '영향을_' 670 | '영양은_' 671 | '영양분이_' 672 | '열이_' 673 | '열어_' 674 | '열과_' 675 | '연합국_' 676 | '연합_' 677 | '연세대_' 678 | '연설_' 679 | '연못이_' 680 | '연락부_' 681 | '연기_' 682 | '연구한다' 683 | '연구원들은_' 684 | '연간_' 685 | '역할로_' 686 | '역을_' 687 | '역에서_' 688 | '역삼투_' 689 | '역대_' 690 | '여부를_' 691 | '에크라_' 692 | '에서_' 693 | '에게_' 694 | '없으면_' 695 | '없기_' 696 | '없거나_' 697 | '업적을_' 698 | '업자_' 699 | '언덕이_' 700 | '언덕을_' 701 | '언급할_' 702 | '어워드_' 703 | '어머니가_' 704 | '어떻게_' 705 | '어거스트_' 706 | '얘기하고_' 707 | '양호한_' 708 | '양이_' 709 | '양과_' 710 | '양곡상에_' 711 | '양곡관리법_' 712 | '약_' 713 | '야마토_' 714 | '앨프리드_' 715 | '앞질러_' 716 | '앞두고_' 717 | '알려져있다' 718 | '알려져_' 719 | '않은_' 720 | '않으면_' 721 | '않다가_' 722 | '않다' 723 | '않는다' 724 | '안재휴와_' 725 | '안씨_' 726 | '안성수' 727 | '안삼환이_' 728 | '안대희_' 729 | '안기부에서_' 730 | '안기부로부터_' 731 | '안기부_' 732 | '안그래도_' 733 | '안강민은' 734 | '안강민에_' 735 | '안강민도_' 736 | '안강민' 737 | '악영향을_' 738 | '아이들' 739 | '아역연기상을_' 740 | '아라레' 741 | '아닌가_' 742 | '아니라_' 743 | '아는_' 744 | '썩어_' 745 | '쌓인_' 746 | '쌀_' 747 | '심하지는_' 748 | '심하게_' 749 | '심재륜' 750 | '심는_' 751 | '실트를_' 752 | '실트는_' 753 | '실트가_' 754 | '실체적_' 755 | '신축용으로_' 756 | '식품의_' 757 | '식물은_' 758 | '식량을_' 759 | '시즌_' 760 | '시중으로_' 761 | '시점으로_' 762 | '시절부터_' 763 | '시절_' 764 | '시작했다' 765 | '시인해서_' 766 | '시멘트_' 767 | '시리즈_' 768 | '시대부터_' 769 | '시대를_' 770 | '시대_' 771 | '시국사건_' 772 | '승소했으나_' 773 | '스파이더위크가의_' 774 | '스릴러' 775 | '숲을_' 776 | '순순히_' 777 | '수해지구에_' 778 | '수입_' 779 | '수원이_' 780 | '수상했다' 781 | '수상하였다' 782 | '수사하면서_' 783 | '수사_' 784 | '수로' 785 | '수경법' 786 | '쇼와_' 787 | '송치받아서_' 788 | '송창섭' 789 | '송씨일가_' 790 | '소송이든_' 791 | '소금기가_' 792 | '소규모의_' 793 | '센난_' 794 | '세워졌으며' 795 | '세워_' 796 | '세대들이_' 797 | '세계적인_' 798 | '세계_' 799 | '세_' 800 | '성명_' 801 | '성명' 802 | '설립단체의_' 803 | '선임한_' 804 | '선임되었다' 805 | '선의_' 806 | '선을_' 807 | '선배를_' 808 | '선두였던_' 809 | '선단에_' 810 | '선고되더라도_' 811 | '선거에서_' 812 | '서초구청_' 813 | '서쪽을' 814 | '서쪽으로_' 815 | '서울지방검찰청에서_' 816 | '서울시교육감을_' 817 | '서울대학교_' 818 | '서울고검장이' 819 | '서울_' 820 | '서부에_' 821 | '서부_' 822 | '서류_' 823 | '생성된_' 824 | '생산의_' 825 | '생산력이_' 826 | '생산력에서_' 827 | '생겼다' 828 | '생겨났는데' 829 | '새_' 830 | '상태가_' 831 | '상임운영위와_' 832 | '상업지의_' 833 | '상업의_' 834 | '상실된_' 835 | '상부에서_' 836 | '상부' 837 | '상류_' 838 | '상당한_' 839 | '삼성그룹_' 840 | '삶을_' 841 | '살충제' 842 | '산업의_' 843 | '산_' 844 | '산' 845 | '사회정화에_' 846 | '사회에_' 847 | '사항을_' 848 | '사학재단운영과_' 849 | '사퇴하겠다' 850 | '사용되는_' 851 | '사업승인을_' 852 | '사야마_' 853 | '사법시험에_' 854 | '사면할_' 855 | '사망에_' 856 | '사막화' 857 | '사막의_' 858 | '사막을_' 859 | '사람을_' 860 | '사람들을_' 861 | '사건에서_' 862 | '사건에_' 863 | '사건' 864 | '뿐이학수_' 865 | '뿐' 866 | '빼지_' 867 | '빼돌려_' 868 | '빠지고_' 869 | '빛이_' 870 | '빛과_' 871 | '빌헬름_' 872 | '빈약했기_' 873 | '비자금_' 874 | '비율이_' 875 | '비율도_' 876 | '비용이_' 877 | '비밀' 878 | '비료를_' 879 | '비록_' 880 | '비례대표_' 881 | '비나_' 882 | '불신풍조_' 883 | '불법_' 884 | '불모지' 885 | '불리며_' 886 | '불리는_' 887 | '불량_' 888 | '불구하고_' 889 | '불구속_' 890 | '불고지' 891 | '분석하고_' 892 | '북한이_' 893 | '북한_' 894 | '북쪽을_' 895 | '북서에서_' 896 | '북부' 897 | '북동에서_' 898 | '북동부에_' 899 | '북극의_' 900 | '부친은_' 901 | '부정입시사건이_' 902 | '부정등록한_' 903 | '부장검사에_' 904 | '부장검사로_' 905 | '부인하면서' 906 | '부산지방검찰청_' 907 | '부산문화사장을_' 908 | '부분을_' 909 | '부부장_' 910 | '부문_' 911 | '부를_' 912 | '부대를_' 913 | '부당이득을_' 914 | '부근으로부터_' 915 | '본선이_' 916 | '본선과_' 917 | '보호해야_' 918 | '보유_' 919 | '보수_' 920 | '보관해온_' 921 | '보고서를_' 922 | '병합되었으나_' 923 | '별장지로서_' 924 | '변화에_' 925 | '변호사는' 926 | '변호사_' 927 | '변하고_' 928 | '변동이_' 929 | '법학과를_' 930 | '법조인이다' 931 | '법정에_' 932 | '법적인_' 933 | '법원에서_' 934 | '법사위에서' 935 | '법무관을_' 936 | '범람한_' 937 | '범람하였으며' 938 | '벌채' 939 | '벌었던_' 940 | '번성하더라도_' 941 | '배제_' 942 | '배우이다' 943 | '배급된_' 944 | '밭' 945 | '방향의_' 946 | '방향을_' 947 | '방향에는_' 948 | '방침이_' 949 | '방북했던_' 950 | '방법을_' 951 | '방면으로_' 952 | '밝히기에_' 953 | '밝혔다' 954 | '밝혔고' 955 | '발행한_' 956 | '발표한_' 957 | '발탁되었다' 958 | '발전해_' 959 | '발전한_' 960 | '발원하는_' 961 | '발생하여_' 962 | '발달되어_' 963 | '받지만' 964 | '받았다고_' 965 | '받았다' 966 | '받아_' 967 | '받게_' 968 | '반씩_' 969 | '밖에_' 970 | '박씨의_' 971 | '바탕으로_' 972 | '바위나_' 973 | '바람에_' 974 | '바람부는대로' 975 | '바다의_' 976 | '바다_' 977 | '바뀌어_' 978 | '바꿀_' 979 | '바꾼_' 980 | '민수용_' 981 | '미하라_' 982 | '미도스지_' 983 | '미국이라고_' 984 | '물흐르는대로_' 985 | '물이_' 986 | '물의_' 987 | '물었으며' 988 | '물_' 989 | '문화_' 990 | '문익환_' 991 | '무코가오카_' 992 | '무죄판결이_' 993 | '무죄가_' 994 | '못받았다' 995 | '목사와_' 996 | '모퉁이라는_' 997 | '모텔' 998 | '모처에서_' 999 | '모즈_' 1000 | '모래요정과_' 1001 | '모두_' 1002 | '몇_' 1003 | '명예훼손_' 1004 | '명목으로_' 1005 | '명단을_' 1006 | '면세_' 1007 | '며_' 1008 | '며' 1009 | '멀지_' 1010 | '먼_' 1011 | '맨션이_' 1012 | '매입해_' 1013 | '매립해_' 1014 | '매립지' 1015 | '맡아_' 1016 | '맡겨_' 1017 | '맑은_' 1018 | '말한_' 1019 | '말까지_' 1020 | '말_' 1021 | '많거나_' 1022 | '만큼_' 1023 | '만을_' 1024 | '만에_' 1025 | '만들어진_' 1026 | '만들어_' 1027 | '만들기도_' 1028 | '마찮가지로_' 1029 | '마산에_' 1030 | '마나미_' 1031 | '를_' 1032 | '를' 1033 | '로마_' 1034 | '러쉬' 1035 | '란_' 1036 | '라틴어_' 1037 | '라인란트팔츠주에_' 1038 | '라인란트팔츠_' 1039 | '라고_' 1040 | '뜻하는_' 1041 | '뜻의_' 1042 | '뛰어난_' 1043 | '또는_' 1044 | '또_' 1045 | '떡값' 1046 | '떠나며_' 1047 | '때문' 1048 | '때는_' 1049 | '땅이_' 1050 | '땅의_' 1051 | '땅을_' 1052 | '땅' 1053 | '따라서_' 1054 | '따라_' 1055 | '등을_' 1056 | '등으로_' 1057 | '등에_' 1058 | '등록되었다' 1059 | '등과_' 1060 | '들어가고_' 1061 | '든다' 1062 | '드루수스가' 1063 | '뒤에_' 1064 | '될_' 1065 | '된다' 1066 | '된_' 1067 | '되파는_' 1068 | '되면_' 1069 | '되며' 1070 | '되기도_' 1071 | '되고_' 1072 | '됐던_' 1073 | '동서_' 1074 | '동사인' 1075 | '동부' 1076 | '독일의_' 1077 | '독립과_' 1078 | '도키하마_' 1079 | '도청_' 1080 | '도중에_' 1081 | '도이체스_' 1082 | '도시이다' 1083 | '도시의_' 1084 | '도시로' 1085 | '도시가_' 1086 | '도시_' 1087 | '도로는_' 1088 | '도난_' 1089 | '데에_' 1090 | '덥거나' 1091 | '더티' 1092 | '더이상_' 1093 | '더_' 1094 | '대하여_' 1095 | '대표하는_' 1096 | '대통령과_' 1097 | '대지의_' 1098 | '대주교령의_' 1099 | '대선자금_' 1100 | '대북송금_' 1101 | '대법원에서_' 1102 | '대량_' 1103 | '대남공작부서인_' 1104 | '대개_' 1105 | '당한_' 1106 | '당초_' 1107 | '당시에도_' 1108 | '당시는_' 1109 | '담수화_' 1110 | '담당했던_' 1111 | '단행된_' 1112 | '단지_' 1113 | '다양한_' 1114 | '다시_' 1115 | '다섯_' 1116 | '니와_' 1117 | '니시요케_' 1118 | '니기다_' 1119 | '느낌은_' 1120 | '느낀다' 1121 | '느꼈다' 1122 | '뉴스거리' 1123 | '눈이_' 1124 | '놓았다' 1125 | '높다' 1126 | '높고_' 1127 | '농지도_' 1128 | '농지' 1129 | '농작물을_' 1130 | '농작물에_' 1131 | '농경지' 1132 | '농경기의_' 1133 | '논란은_' 1134 | '녹취록을_' 1135 | '녹음테이프를_' 1136 | '노후화' 1137 | '노회찬이_' 1138 | '노태우_' 1139 | '노선을_' 1140 | '노먼_' 1141 | '노동상담소에_' 1142 | '노동당_' 1143 | '노동' 1144 | '네버랜드를_' 1145 | '네로_' 1146 | '넘어갔으며' 1147 | '널리_' 1148 | '내용을_' 1149 | '내사에_' 1150 | '내부의_' 1151 | '내리는_' 1152 | '내리거나_' 1153 | '내_' 1154 | '낮다' 1155 | '납품해온_' 1156 | '남자배우상을_' 1157 | '남을_' 1158 | '남우주연상에_' 1159 | '남북으로_' 1160 | '남북_' 1161 | '남기고_' 1162 | '날_' 1163 | '나카_' 1164 | '나일_' 1165 | '나오자_' 1166 | '나무를_' 1167 | '나라의_' 1168 | '나뉘어_' 1169 | '끊이지_' 1170 | '김주만이_' 1171 | '김원기_' 1172 | '김용갑_' 1173 | '길이_' 1174 | '기획계장과_' 1175 | '기하학에_' 1176 | '기원이_' 1177 | '기여한_' 1178 | '기아와_' 1179 | '기아를_' 1180 | '기슈_' 1181 | '기소했던_' 1182 | '기소했다' 1183 | '기본_' 1184 | '기복은_' 1185 | '기록을_' 1186 | '기념하는_' 1187 | '기념비가_' 1188 | '기구가_' 1189 | '금품을_' 1190 | '근무하면서_' 1191 | '극히_' 1192 | '그의_' 1193 | '그동안_' 1194 | '그다지_' 1195 | '그늘에_' 1196 | '그는' 1197 | '규정하였다' 1198 | '규명해_' 1199 | '귀가_' 1200 | '굶어_' 1201 | '국회에서_' 1202 | '국회_' 1203 | '국지적' 1204 | '국제_' 1205 | '국민_' 1206 | '국도' 1207 | '구형했다' 1208 | '구축되었다' 1209 | '구청이_' 1210 | '구와_' 1211 | '구속하였다_' 1212 | '구속_' 1213 | '구성원에_' 1214 | '구릉에서_' 1215 | '구릉부는_' 1216 | '구름으로부터_' 1217 | '구를_' 1218 | '구내를_' 1219 | '교통의_' 1220 | '교육감이었던_' 1221 | '교역의_' 1222 | '교양강좌와_' 1223 | '교수를_' 1224 | '광합성에_' 1225 | '광주_' 1226 | '관한_' 1227 | '관리지역이_' 1228 | '관련한_' 1229 | '관련하여_' 1230 | '관련자들의_' 1231 | '관련_' 1232 | '관광호텔을_' 1233 | '관광과에_' 1234 | '관광' 1235 | '관공서_' 1236 | '관공서' 1237 | '관개용_' 1238 | '관개_' 1239 | '관개' 1240 | '관' 1241 | '과정은_' 1242 | '과거사_' 1243 | '과거_' 1244 | '과_' 1245 | '공학박사를_' 1246 | '공천에서_' 1247 | '공천심사위원회_' 1248 | '공천심사위원을_' 1249 | '공천심사위원으로서' 1250 | '공장이_' 1251 | '공작에_' 1252 | '공업지의_' 1253 | '공업지대의_' 1254 | '공업이_' 1255 | '공안부_' 1256 | '공안_' 1257 | '공안1부로_' 1258 | '공안1부_' 1259 | '공무집행방해로_' 1260 | '공무원을_' 1261 | '공로가_' 1262 | '공급해_' 1263 | '공권력을_' 1264 | '공개하면서_' 1265 | '곳을_' 1266 | '곳은_' 1267 | '곳으로' 1268 | '곳에_' 1269 | '곳부터_' 1270 | '골짜기에_' 1271 | '골짜기는_' 1272 | '곧_' 1273 | '곡으로_' 1274 | '곡' 1275 | '고야_' 1276 | '고속도로와_' 1277 | '고사했음에도_' 1278 | '고령화' 1279 | '고등검찰관으로_' 1280 | '고급차_' 1281 | '고급_' 1282 | '고가에_' 1283 | '계획적으로_' 1284 | '계획은_' 1285 | '계속되었다' 1286 | '계단식_' 1287 | '경우는_' 1288 | '경우_' 1289 | '경상북도_' 1290 | '경상남도_' 1291 | '경력을_' 1292 | '경기고등학교' 1293 | '결코_' 1294 | '결정하면서_' 1295 | '결정이_' 1296 | '결국_' 1297 | '것인가' 1298 | '것이든_' 1299 | '것이_' 1300 | '것을_' 1301 | '것에_' 1302 | '것' 1303 | '검토했는데_' 1304 | '검토를_' 1305 | '검토' 1306 | '검찰을_' 1307 | '검찰은_' 1308 | '검찰에_' 1309 | '검찰사에_' 1310 | '검찰_' 1311 | '검사장을_' 1312 | '검사에_' 1313 | '검사를_' 1314 | '검사들과_' 1315 | '걸쳐_' 1316 | '건축업자에게_' 1317 | '건축업자로부터_' 1318 | '건설하여_' 1319 | '거쳐_' 1320 | '거주자의_' 1321 | '거의_' 1322 | '거론되었던_' 1323 | '거라고_' 1324 | '개의치_' 1325 | '개업했다' 1326 | '개설한_' 1327 | '개발된_' 1328 | '강해야_' 1329 | '강이_' 1330 | '강을_' 1331 | '강은_' 1332 | '강습소_' 1333 | '강력한_' 1334 | '강력하게_' 1335 | '강남구청_' 1336 | '강가에_' 1337 | '강' 1338 | '감소로_' 1339 | '감소가_' 1340 | '간혹' 1341 | '간첩단_' 1342 | '간선도로가_' 1343 | '간사이_' 1344 | '각지_' 1345 | '각종_' 1346 | '각_' 1347 | '가치도_' 1348 | '가진_' 1349 | '가지의_' 1350 | '가와치_' 1351 | '가야지' 1352 | '가로챈_' 1353 | '가도나_' 1354 | '가도가_' 1355 | '가_' 1356 | '가' 1357 | '農地' 1358 | '安剛民' 1359 | 'land' 1360 | 'arare' 1361 | ']]' 1362 | 'TV_' 1363 | 'PET_' 1364 | 'KAL기_' 1365 | 'E의_' 1366 | 'Arable_' 1367 | '9월_' 1368 | '9년_' 1369 | '8월에_' 1370 | '888' 1371 | '800원에_' 1372 | '7천만원을_' 1373 | '7월_' 1374 | '7명을_' 1375 | '700만원을_' 1376 | '6월_' 1377 | '6억_' 1378 | '62세' 1379 | '60km_' 1380 | '5일에_' 1381 | '5억여원의_' 1382 | '5명을_' 1383 | '59개_' 1384 | '55회' 1385 | '50여개_' 1386 | '49일만에_' 1387 | '450만원을_' 1388 | '4500만원의_' 1389 | '3일에_' 1390 | '3일' 1391 | '3년까지_' 1392 | '34호_' 1393 | '31일에_' 1394 | '30호_' 1395 | '30일에_' 1396 | '30역억원을_' 1397 | '30여만_' 1398 | '3000평을_' 1399 | '2심에서는_' 1400 | '29일에_' 1401 | '29일_' 1402 | '28호_' 1403 | '26일에_' 1404 | '26일_' 1405 | '26일' 1406 | '25년_' 1407 | '24일_' 1408 | '21일에_' 1409 | '20여만평을_' 1410 | '20억원대의_' 1411 | '204호_' 1412 | '2017년_' 1413 | '2017' 1414 | '2013' 1415 | '2010' 1416 | '2008' 1417 | '2007년_' 1418 | '2007' 1419 | '2004년_' 1420 | '2003년_' 1421 | '2002년_' 1422 | '1호선' 1423 | '1일을_' 1424 | '1일에_' 1425 | '1월_' 1426 | '1심에서는_' 1427 | '1세를_' 1428 | '1만원씩_' 1429 | '1년동안_' 1430 | '1999' 1431 | '1995년말_' 1432 | '1992년_' 1433 | '1990년도_' 1434 | '1982년_' 1435 | '1972년_' 1436 | '1969년_' 1437 | '1967년_' 1438 | '18세기_' 1439 | '18대를_' 1440 | '15일에_' 1441 | '14일' 1442 | '13명은_' 1443 | '12월_' 1444 | '11일에_' 1445 | '11세기에_' 1446 | '11대와_' 1447 | '105' 1448 | '1018년부터_' 1449 | '100' 1450 | '..."' 1451 | '.(' 1452 | ', '' 1453 | ', "' 1454 | '), 《》(' 1455 | ') ' 1456 | '", "' 1457 | '")' 1458 | '"()' 1459 | '"("' 1460 | ' “' 1461 | ' ‘' 1462 | ' ]]' 1463 | ' ( , ' 1464 | --------------------------------------------------------------------------------