File size: 78,294 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 |
{
"paper_id": "O14-5003",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:04:15.624246Z"
},
"title": "Automatic Move Analysis of Research Articles for Assisting Writing",
"authors": [
{
"first": "Guan-Cheng",
"middle": [],
"last": "Huang",
"suffix": "",
"affiliation": {},
"email": "hsiang@nlplab.cc"
},
{
"first": "Jian-Cheng",
"middle": [],
"last": "Wu",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Hsiang-Ling",
"middle": [],
"last": "Hsu",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Tzu-Hsi",
"middle": [],
"last": "Yen",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Jason",
"middle": [
"S"
],
"last": "Chang",
"suffix": "",
"affiliation": {},
"email": "cheng@nlplab.cc"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Rhetorical moves are a useful framework for analyzing the hidden rhetorical organization in research papers, in teaching academic writing. We propose a",
"pdf_parse": {
"paper_id": "O14-5003",
"_pdf_hash": "",
"abstract": [
{
"text": "Rhetorical moves are a useful framework for analyzing the hidden rhetorical organization in research papers, in teaching academic writing. We propose a",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "S = s 1 , s 2 , ..., s n \uff0c\u6211\u5011\u7684\u76ee\u6a19\u5c07 S \u6a19\u8a3b \u4e0a\u4e00\u5e8f\u5217\u5c0d\u61c9\u7684\u6587\u6b65\u6a19\u7c64 M = m 1 , m 2 , ..., m n \uff0c\u5176\u4e2d m i \u70ba s i \u7684\u6587\u6b65\u985e\u578b\u3002\u70ba\u6b64\uff0c\u6211\u5011 \u5f9e S \u8a08\u7b97\u51fa\u5e38\u898b\u7684 k \u500b\u53e5\u578b P = p 1 , p 2 , ..., p k \uff0c\u4e26\u4eba\u5de5\u6a19\u8a3b\u5c0d\u61c9\u6587\u6b65 T = t 1 , t 2 , ..., t k \uff0c \u800c\u4eba\u5de5\u6a19\u793a\u53e5\u578b p i \u70ba t i \u6642\uff0c\u5fc5\u9808\u78ba\u8a8d\u7b26\u5408 p i \u53e5\u578b\u7684\u53e5\u5b50\u5927\u90fd\u8868\u9054 t i \u6587\u6b65\u7684\u8cc7\u8a0a\u3002 \u5728\u672c\u7bc0\u7684\u5176\u9918\u5c0f\u7bc0\uff0c\u6211\u5011\u5c07\u63cf\u8ff0\u6211\u5011\u5c0d\u6b64\u4e00\u554f\u984c\u7684\u89e3\u6c7a\u65b9\u6cd5\u3002\u9996\u5148\uff0c\u5728\u7b2c 3.2.1 \u7bc0\uff0c \u6211\u5011\u63cf\u8ff0\u5982\u4f55\u5f9e\u7db2\u8def\u6536\u96c6\u5b78\u8853\u6703\u8b70\u8207\u671f\u520a\u7684\u8ad6\u6587\uff0c\u4e26\u64f7\u53d6\u5176\u4e2d\u7684\u300c\u7c21\u4ecb\u300d\u6b64\u4e00\u7bc0\u3002\u63a5\u8457\uff0c \u6211\u5011\u5728\u7b2c 3.2.2 \u7bc0\u63cf\u8ff0\uff0c\u5982\u4f55\u5f9e\u7c21\u4ecb\u4e2d\uff0c\u7d71\u8a08\u5e38\u898b\u7684\u53e5\u578b\uff0c\u4ee5\u53ca\u4eba\u5de5\u6a19\u8a18\u5e38\u898b\u53e5\u578b\u4e4b\u6587 \u6b65(\u7b2c 3.2.3 \u7bc0)\uff0c\u9032\u800c\u7522\u751f\u6a19\u793a\u6587\u6b65\u4e4b\u8a13\u7df4\u8cc7\u6599(\u7b2c 3.2.4 \u7bc0)\u3002\u6700\u5f8c\uff0c\u6211\u5011\u63cf\u8ff0\u5982 \u4f55\u5728\u8a13\u7df4\u8cc7\u6599\u4e0a\uff0c\u9644\u52a0\u7279\u5fb5\u503c(\u7b2c 3.2.5 \u7bc0)\uff0c\u4ee5\u53ca\u8a13\u7df4\u7d71\u8a08\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b(\u7b2c 3.2.6 \u7bc0)\u3002 3.2 \u5b78\u7fd2\u5c07\u8ad6\u6587\u53e5\u5b50\u6a19\u6ce8\u6587\u6b65 \u5b78\u7fd2\u5c07\u8ad6\u6587\u53e5\u5b50\u6a19\u6ce8\u6587\u6b65 \u5b78\u7fd2\u5c07\u8ad6\u6587\u53e5\u5b50\u6a19\u6ce8\u6587\u6b65 \u5b78\u7fd2\u5c07\u8ad6\u6587\u53e5\u5b50\u6a19\u6ce8\u6587\u6b65 \u6211\u5011\u8a66\u5716\u627e\u5230\u4e00\u7d44\u5404\u7a2e\u6587\u6b65\u7684\u5e38\u898b\u53e5\u578b\uff0c\u85c9\u4ee5\u7522\u751f\u6a19\u793a\u6587\u6b65\u53e5\u5b50\u4e4b\u8a13\u7df4\u8cc7\u6599\uff0c\u4ee5\u8a13\u7df4\u4e00 \u5957\u6587\u6b65\u5206\u985e\u5668\u3002\u6211\u5011\u7684\u8a13\u7df4\u904e\u7a0b\u5982\u5716 4 \u6240\u793a\u3002 36 \u9ec3\u51a0\u8aa0 \u7b49 (1) \u5f9e\u7db2\u8def\u6536\u96c6\u7814\u7a76\u8ad6\u6587\u7c21\u4ecb (\u7b2c 3.2.1 \u7bc0) (2) \u5f9e\u8ad6\u6587\u7c21\u4ecb\u4e2d\u7d71\u8a08\u5e38\u898b\u53e5\u578b (\u7b2c 3.2.2 \u7bc0) (3) \u4eba\u5de5\u6a19\u8a18\u5e38\u898b\u53e5\u578b\u4e4b\u6587\u6b65 (\u7b2c 3.2.3 \u7bc0) (4) \u7522\u751f\u6709\u6587\u6b65\u6a19\u793a\u4e4b\u8a13\u7df4\u8cc7\u6599 (\u7b2c 3.2.4 \u7bc0)",
"eq_num": "(5"
}
],
"section": "",
"sec_num": null
},
{
"text": "http://www.nltk.org 3 http://www.nactem.ac.uk/GENIA/tagger/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Mover: A machine learning tool to assist in the reading and writing of technical papers",
"authors": [
{
"first": "L",
"middle": [],
"last": "Anthony",
"suffix": ""
},
{
"first": "G",
"middle": [
"V"
],
"last": "Lashkia",
"suffix": ""
}
],
"year": 2003,
"venue": "IEEE Trans. Prof. Commun",
"volume": "46",
"issue": "",
"pages": "185--193",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anthony, L., & Lashkia, G. V. (2003). Mover: A machine learning tool to assist in the reading and writing of technical papers. IEEE Trans. Prof. Commun., 46, 185-193.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Linguistic Analysis of Grant Proposals",
"authors": [
{
"first": "U",
"middle": [],
"last": "Connor",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Mauranen",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Connor, U., & Mauranen, A. (1999). Linguistic Analysis of Grant Proposals: European Union Research Grants.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Inducing features of random fields. Pattern Analysis and Machine Intelligence",
"authors": [
{
"first": "S",
"middle": [],
"last": "Della Pietra",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Della Pietra",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Lafferty",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Technol",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Brook",
"suffix": ""
}
],
"year": 1997,
"venue": "IEEE Transactions on",
"volume": "19",
"issue": "4",
"pages": "380--393",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Della Pietra, S., Della Pietra, V., Lafferty, J., Technol, R., & Brook, S. (1997). Inducing features of random fields. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(4), 380-393.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "New Methods in Automatic Extracting",
"authors": [
{
"first": "H",
"middle": [
"P"
],
"last": "Edmundson",
"suffix": ""
}
],
"year": 1969,
"venue": "Journal of the Association for Computing",
"volume": "16",
"issue": "2",
"pages": "264--285",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Edmundson, H. P. (1969). New Methods in Automatic Extracting. Journal of the Association for Computing: Machinery, 16(2), 264-285.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Teaching EFL students to extract structural information from abstracts",
"authors": [
{
"first": "N",
"middle": [],
"last": "Graetz",
"suffix": ""
}
],
"year": 1985,
"venue": "Reading for Professional Purposed: Methods and Materials in Teaching Languages",
"volume": "",
"issue": "",
"pages": "123--135",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Graetz, N. (1985). Teaching EFL students to extract structural information from abstracts. In Jan M. Ulijn and Anthony K. Pugh, editors, Reading for Professional Purposed: Methods and Materials in Teaching Languages, pages 123-135. Acco, Leuven, Belgium.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Identifying Sections in Scientific Abstracts using Conditional Random Fields",
"authors": [
{
"first": "K",
"middle": [],
"last": "Hirohata",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Okazaki",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Ananiadou",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Ishizuka",
"suffix": ""
},
{
"first": "M",
"middle": [
"I"
],
"last": "Biocentre",
"suffix": ""
}
],
"year": 2008,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hirohata, K., Okazaki, N., Ananiadou, S., Ishizuka, M., & Biocentre, M. I. (2008). Identifying Sections in Scientific Abstracts using Conditional Random Fields.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Generative Content Models for Structural Analysis of Medical Abstracts",
"authors": [
{
"first": "J",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Karakos",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Demner-Fushman",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Khudanpur",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of th HLT/NAACL 2006 Workshop on Biomedical Natural Language Processing",
"volume": "",
"issue": "",
"pages": "65--72",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lin, J., Karakos, D., Demner-Fushman, D., & Khudanpur, S. (2006). Generative Content Models for Structural Analysis of Medical Abstracts. In Proceedings of th HLT/NAACL 2006 Workshop on Biomedical Natural Language Processing (BioNLP'06), 65-72.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Categorization of sentence types in medical abstracts",
"authors": [
{
"first": "L",
"middle": [],
"last": "Mcknight",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Srinivasan",
"suffix": ""
}
],
"year": 2003,
"venue": "AMIA Annual Symposium Proceedings",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "McKnight, L., & Srinivasan, P. (2003). Categorization of sentence types in medical abstracts. In AMIA Annual Symposium Proceedings (Vol. 2003, p. 440). American Medical Informatics Association.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Using argumentation to extract key sentences from biomedical bstracts",
"authors": [
{
"first": "P",
"middle": [],
"last": "Ruch",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Boyer",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Chichester",
"suffix": ""
},
{
"first": "I",
"middle": [],
"last": "Tbahriti",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Geissb\u00fchler",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Fabry",
"suffix": ""
},
{
"first": ".",
"middle": [
"."
],
"last": "Veuthey",
"suffix": ""
},
{
"first": "A",
"middle": [
"L"
],
"last": "",
"suffix": ""
}
],
"year": 2007,
"venue": "International journal of medical informatics",
"volume": "76",
"issue": "2",
"pages": "195--200",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ruch, P., Boyer, C., Chichester, C., Tbahriti, I., Geissb\u00fchler, A., Fabry, P., ... & Veuthey, A. L. (2007). Using argumentation to extract key sentences from biomedical bstracts. International journal of medical informatics, 76(2), 195-200.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Using sectioning information for text retrieval: a case study with the MEDLINE abstracts",
"authors": [
{
"first": "M",
"middle": [],
"last": "Shimbo",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Yamasaki",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Matsumoto",
"suffix": ""
}
],
"year": 2003,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shimbo, M., Yamasaki, T., & Matsumoto, Y. (2003). Using sectioning information for text retrieval: a case study with the MEDLINE abstracts.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Genre analysis: English in Academic and Research Settings",
"authors": [
{
"first": "J",
"middle": [
"M"
],
"last": "Swales",
"suffix": ""
}
],
"year": 1990,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Swales, J.M. (1990). Genre analysis: English in Academic and Research Settings. Cambridge University Press.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Argumentative Zoning: Information Extraction from Scientific Text",
"authors": [
{
"first": "S",
"middle": [],
"last": "Teufel",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Teufel, S. (1999). Argumentative Zoning: Information Extraction from Scientific Text. PhD thesis, University of Edinburgh.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status",
"authors": [
{
"first": "S",
"middle": [],
"last": "Teufel",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Moens",
"suffix": ""
}
],
"year": 2002,
"venue": "Computational Linguistics",
"volume": "28",
"issue": "4",
"pages": "409--445",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Teufel, S., & Moens, M. (2002). Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status. Computational Linguistics, 28(4), 409-445.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Computational analysis of move structures in academic abstracts",
"authors": [
{
"first": "J",
"middle": [
"C"
],
"last": "Wu",
"suffix": ""
},
{
"first": "Y",
"middle": [
"C"
],
"last": "Chang",
"suffix": ""
},
{
"first": "H",
"middle": [
"C"
],
"last": "Liou",
"suffix": ""
},
{
"first": "J",
"middle": [
"S"
],
"last": "Chang",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wu, J. C., Chang, Y. C., Liou, H. C., & Chang, J. S. (2006). Computational analysis of move structures in academic abstracts.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "A sentence classification system for multi-document summarization in the biomedical domain",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Yamamoto",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Takagi",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of International Workshop on Biomedical Data Engineering",
"volume": "",
"issue": "",
"pages": "90--95",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yamamoto, Y., & Takagi, T. (2005). A sentence classification system for multi-document summarization in the biomedical domain. In Proceedings of International Workshop on Biomedical Data Engineering, 90-95.",
"links": null
}
},
"ref_entries": {
"TABREF0": {
"text": "\u8a73\u898b www.nlm.nih.gov/bsd/policy/structured_abstracts.htmlSwales & Feak, 2004; Glasman-Deal, 2010)\u3002\u4e5f\u6709\u7814\u7a76\u8005\u958b\u767c\u8edf\u9ad4\u7cfb\u7d71(\u4f8b \u5982\uff0cMarking Mate: writingtools.xjtlu.edu.cn:8080/mm/markingmate.html)\uff0c\u5206\u6790\u5b78\u751f\u7684\u4f5c",
"num": null,
"html": null,
"content": "<table><tr><td>32</td><td/><td/><td>\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a</td><td>31 \u9ec3\u51a0\u8aa0 \u7b49</td></tr><tr><td colspan=\"4\">CARS \u6587\u6b65 WriteAhead \u6587\u6b65 \u8cc7\u8a0a\u5167\u5bb9 \u5b50\u6587\u6b65\u8207\u8cc7\u8a0a\u5167\u5bb9 WriteAhead \u80fd\u5920\u63d0\u4f9b\u8207\u6392\u5217\u9019\u4e9b\u63d0\u793a\uff0c\u662f\u56e0\u70ba WriteAhead \u900f\u904e\u5927\u91cf\u7684\u8ad6\u6587\u539f\u59cb\u8cc7 \u5c0d\u61c9\u4e4b CARS \u6587\u6b65</td></tr><tr><td colspan=\"4\">\u6587\u6b65 I \u754c\u5b9a\u7bc4\u570d \u80cc\u666f(BKG) \u6599\u4ee5\u53ca\u5c11\u91cf\u7684\u4eba\u5de5\u6a19\u793a\uff0c\u5b78\u7fd2\u5982\u4f55\u8fa8\u8b58 OWN \u6587\u6b65\u7684\u53e5\u5b50\uff0c\u4e26\u9032\u800c\u7d71\u8a08\u9019\u4e9b\u53e5\u5b50\u5167\u7684\u5e38 1. \u8072\u660e\u7814\u7a76\u9818\u57df\u7684\u91cd\u8981\u6027\uff0c\u53ca/\u6216 2. \u8072\u660e\u7814\u7a76\u8ab2\u984c\u7684\u5ee3\u6cdb\u6027\u8207\u666e\u53ca\u6027\uff0c\u53ca/\u6216 \u9818\u57df\uff1a\u91cd\u8981\u6027\u3001\u8853\u8a9e\u5b9a\u7fa9\u3001\u7f3a\u53e3 \u5f15\u7528\u8207\u8a55\u8ad6\u524d\u4eba\u7814\u7a76 \u6587\u6b65 I-1,2,3, \u6587\u6b65 II-1B \u6587\u6b65 I-3 \u898b\u7247\u8a9e\u53ca\u5176\u983b\u7387\u3002\u6211\u5011\u5c07\u5728\u7b2c\u4e09\u7bc0\u8a73\u8ff0 WriteAhead \u6240\u904b\u7528\u7684\u6587\u6b65\u5206\u985e\u5668\u7684\u8a13\u7df4\u904e\u7a0b\u3002</td></tr><tr><td colspan=\"4\">3. 1A. \u63d0\u51fa\u8207\u524d\u4eba\u4e0d\u540c\u7684\u8072\u660e\uff0c\u6216 \u56de\u9867\u8207\u8a55\u8ad6\u524d\u4eba\u7814\u7a76 1B. \u6307\u51fa\u524d\u4eba\u7814\u7a76\u7684\u7f3a\u53e3(gap) \uff0c\u6216 \u672c\u8ad6\u6587(OWN) \u76ee\u7684\uff1a\u8f38\u5165\u3001\u8f38\u51fa\u3001\u689d\u4ef6 \u6587\u6b65 II \u5efa\u7acb\u5229\u57fa \u65b9\u6cd5\uff1a\u7814\u7a76\u8def\u7dda\u3001\u5178\u7bc4\u3001\u4f9d\u64da\u3001\u6b65\u9a5f \u7d50\u679c\uff1a\u5be6\u4f5c\u3001\u5be6\u9a57\u3001\u8a55\u4f30\u3001\u7d50\u679c\u3001\u767c\u73fe \u672c\u8ad6\u6587\u63a5\u4e0b\u4f86\u7684\u90e8\u5206\uff0c\u5b89\u6392\u5982\u4e0b\u3002\u6211\u5011\u5728\u4e0b\u4e00\u7bc0\u56de\u9867\u76f8\u95dc\u7684\u7814\u7a76\u3002\u63a5\u8457\uff0c\u6211\u5011\u63cf\u8ff0 \u6587\u6b65 III-1A, \u6587\u6b65 II-1C \u5982\u4f55\u5b78\u7fd2\u81ea\u52d5\u5c07\u8ad6\u6587\u7c21\u4ecb\u53e5\u5b50\u6a19\u8a3b\u6587\u6b65 (\u7b2c\u4e09\u7bc0) \u3002\u6211\u5011\u7e7c\u800c\u63cf\u8ff0\u5982\u4f55\u5c07\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\uff0c \u6587\u6b65 III-1B, \u6587\u6b65 II-1D \u5be6\u969b\u88fd\u4f5c\u6210\u4e00\u500b\u8003\u616e\u6587\u6b65\u985e\u5225\u9032\u884c\u5beb\u4f5c\u63d0\u793a\u7684\u96db\u5f62\u7cfb\u7d71\uff0c\u4ee5\u53ca\u76f8\u95dc\u7684\u5be6\u9a57\u8a2d\u5b9a\u3001\u8a55\u4f30\u6307 \u6587\u6b65 III-2 \u6a19\u3001\u4ee5\u53ca\u5be6\u9a57\u7d50\u679c(\u7b2c\u56db\u7bc0)\u3002\u6700\u5f8c\uff0c\u6211\u5011\u6307\u51fa\u672a\u4f86\u7814\u7a76\u65b9\u5411\uff0c\u4e26\u4f5c\u7d50\u8ad6(\u7b2c\u4e94\u7bc0)\u3002</td></tr><tr><td colspan=\"2\">\u8a0e\u8ad6(DIS) 2. \u76f8\u95dc\u6587\u737b \u76f8\u95dc\u6587\u737b \u76f8\u95dc\u6587\u737b \u76f8\u95dc\u6587\u737b</td><td colspan=\"2\">\u9ec3\u51a0\u8aa0 \u9ec3\u51a0\u8aa0 \u9ec3\u51a0\u8aa0 \u9ec3\u51a0\u8aa0 * * * * \u3001 \u3001 \u3001 \u3001\u5433\u9451\u57ce \u5433\u9451\u57ce \u5433\u9451\u57ce \u5433\u9451\u57ce * * * * \u3001 \u3001 \u3001 \u3001\u8a31\u6e58\u7fce \u8a31\u6e58\u7fce \u8a31\u6e58\u7fce \u8a31\u6e58\u7fce * * * * \u3001 \u3001 \u3001 \u3001\u984f\u5b5c\u66e6 \u984f\u5b5c\u66e6 \u984f\u5b5c\u66e6 \u984f\u5b5c\u66e6 * * * * \u3001 \u3001 \u3001 \u3001\u5f35\u4fca\u76db \u5f35\u4fca\u76db \u5f35\u4fca\u76db \u5f35\u4fca\u76db * 1C. \u63d0\u51fa\u672c\u8ad6\u6587\u7684\u7814\u7a76\u8b70\u984c(research question) \uff0c\u6216 1D. \u8aaa\u660e\u672c\u7814\u7a76\u6240\u6839\u64da\u7684\u5178\u7bc4\u8207\u50b3\u7d71 \u6bd4\u8f03\u672c\u8ad6\u6587\u8207\u524d\u4eba\u7814\u7a76\u7684\u76f8\u540c\u4e4b\u8655 \u5c0d\u7167\u672c\u8ad6\u6587\u8207\u524d\u4eba\u7814\u7a76\u7684\u76f8\u7570\u4e4b\u8655 \u6587\u6b65 II-1A</td></tr><tr><td colspan=\"4\">\u6587\u6b65 III \u5b78\u8853\u82f1\u6587\u7814\u7a76\u8207\u6559\u5b78(English for Academic Purpose)\u70ba\u76f8\u7576\u91cd\u8981\u7684\u7814\u7a76\u9818\u57df\u3002\u8fd1\u5e74\u4f86\uff0c 1A. \u6982\u8ff0\u672c\u8ad6\u6587\u7684\u76ee\u7684\uff0c\u6216 \u672a\u4f86\u7814\u7a76\u65b9\u5411</td></tr><tr><td colspan=\"4\">\u4f54\u64da\u5229\u57fa \u6587\u672c\u7d44\u7e54(TEX) \u63d0\u4f9b\u5168\u6587\u7684\u7bc0\u5927\u7db1(\u76ee\u6b21\u8868) 1B. \u6982\u8ff0\u672c\u8ad6\u6587\u7684\u65b9\u6cd5 \u5b78\u8005\u5c0d\u65bc\u7814\u7a76\u8a08\u5283\u66f8\uff0c\u4ee5\u53ca\u5b78\u8853\u6703\u8b70\u8207\u671f\u520a\u8ad6\u6587\uff0c\u90fd\u6709\u6df1\u5165\u7684\u7814\u7a76(Connor & Mauranen, \u6587\u6b65 III-3</td></tr><tr><td colspan=\"4\">2. \u5ba3\u5e03\u672c\u8ad6\u6587\u7684\u4e3b\u8981\u7d50\u679c\u8207\u767c\u73fe \u63d0\u4f9b\u7bc0\u5167\u7d30\u5206\u5b50\u7bc0\u7684\u5927\u7db1 1999; Swales & Feak, 2004) \u3002\u9019\u4e9b\u7814\u7a76\u901a\u5e38\u91dd\u5c0d\u8ad6\u6587\u9010\u53e5\u9010\u6bb5\u9032\u884c\u4eba\u70ba\u5206\u6790\uff0c\u7d93\u904e\u6b78\u7d0d</td></tr><tr><td colspan=\"4\">1. \u7c21\u4ecb \u7c21\u4ecb \u7c21\u4ecb \u5f8c\uff0c\u63d0\u51fa\u4e00\u5957\u8ad6\u6587\u4fee\u8fad\u7684\u5206\u6790\u67b6\u69cb\u3002\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u5247\u91dd\u5c0d\u5b78\u8853\u8ad6\u6587\u7684\u300c\u7c21\u4ecb\u300d\u9019\u4e00 3. \u6307\u51fa\u672c\u8ad6\u6587\u7684\u7d50\u69cb \u6307\u793a\u5716\u8868(\u7de8\u865f) \u7c21\u4ecb \u8fd1\u5e74\u4f86\uff0c\u82f1\u6587\u9010\u6f38\u8b8a\u6210\u5168\u4e16\u754c\u5b78\u8853\u7814\u7a76\u6700\u4e3b\u8981\u7684\u6e9d\u901a\u7684\u5a92\u4ecb\u3002\u800c\u5b78\u8853\u82f1\u6587\u5beb\u4f5c\uff0c\u4e5f\u6210\u70ba \u975e\u5e38\u91cd\u8981\u7684\u7814\u7a76\u8207\u6559\u5b78\u7684\u9818\u57df\u3002\u5b78\u8005\u4e5f\u5f88\u91cd\u8996\uff0c\u5982\u4f55\u900f\u904e\u96fb\u8166\u7684\u8f14\u52a9\uff0c\u5e6b\u52a9\u4e00\u822c\u6027\u7684\u8a9e \u8a00\u5b78\u7fd2\uff0c\u751a\u6216\u7279\u5b9a\u6027\u7684\u5b78\u8853\u5beb\u4f5c\u3002\u5b78\u8853\u5beb\u4f5c\u5305\u542b\u8a31\u591a\u7684\u6587\u7ae0\u985e\u578b\uff0c\u5305\u62ec\u5b78\u8853\u8ad6\u6587\u3001\u8a08\u756b \u7533\u8acb\u66f8\u3001\u56de\u9867\u8207\u8a55\u8ad6\u6587\u7ae0\u7b49(Swales, 1990)\u3002\u5176\u4e2d\uff0c\u7814\u7a76\u8ad6\u6587\u5360\u6709\u6700\u91cd\u8981\u7684\u89d2\u8272\u3002 \u5728\u5b78\u8853\u8ad6\u6587\u4e2d\uff0c\u300c\u7c21\u4ecb\u300d\u662f\u7d55\u5927\u90e8\u5206\u8ad6\u6587\u90fd\u6709\u7684\u7b2c\u4e00\u500b\u5c0f\u7bc0\u3002\u73fe\u4eca\uff0c\u5e7e\u4e4e\u6c92\u6709\u5b78\u8853 \u8ad6\u6587\uff0c\u6c92\u6709\u300c\u6458\u8981\u300d\u8207\u300c\u7c21\u4ecb\u300d\uff0c\u800c\u76f4\u63a5\u8a73\u7d30\u5730\u63cf\u8ff0\u7814\u7a76\u7684\u76ee\u7684\u3001\u65b9\u6cd5\u3001\u7d50\u679c\u3002\u800c\u4e14\uff0c \u5c0d\u5beb\u8005\u548c\u8b80\u8005\u800c\u8a00\uff0c\u300c\u7c21\u4ecb\u300d\u5728\u5b78\u8853\u8ad6\u6587\u4e2d\u90fd\u626e\u6f14\u975e\u5e38\u91cd\u8981\u7684\u89d2\u8272\u3002\u4e00\u7bc7\u597d\u7684\u7c21\u4ecb\uff0c\u8981 \u80fd\u70ba\u6574\u7bc7\u8ad6\u6587\u5b9a\u8abf\uff0c\u6293\u4f4f\u8b80\u8005\u7684\u8208\u8da3\uff0c\u63d0\u4f9b\u8ad6\u6587\u7684\u627c\u8981\u8cc7\u8a0a\u3002\u63db\u8a00\u4e4b\uff0c\u300c\u7c21\u4ecb\u300d \u80a9\u8ca0\u91cd \u5927\u8cac\u4efb\u2500\u2500\u5438\u5f15\u8b80\u8005\u6ce8\u610f\uff0c\u8b80\u5b8c\u5168\u6587\u3002 \u6b64\u4e00\u5206\u985e\u65b9\u5f0f\uff0c\u9664\u7cfb\u7d71\u8f03\u6613\u65bc\u81ea\u52d5\u5206\u985e\u6587\u6b65\u5916\uff0c\u4f7f\u7528\u8005\u4ea6\u6bd4\u8f03\u5bb9\u6613\u638c\u63e1\u4e26\u4f7f\u7528\u65bc\u5beb\u4f5c\u904e \u8a55\u4f30(evaluation)\u3001\u7d50\u8ad6(conclusion)\u7b49\u90e8\u5206\u3002 WriteAhead \u7684\u958b\u767c\u904e\u7a0b\uff0c\u6211\u5011\u63a1\u7528\u4e86\u6bd4 CARS \u66f4\u7c21\u55ae\u7684\u6587\u6b65\u5206\u985e\uff0c\u5982\u5716 2 \u6240\u793a\u3002\u7528\u4e86 \u6587\u7c21\u4ecb\u4f3c\u4e4e\u6709\u5171\u540c\u7684 \u300c\u554f\u984c\u2500\u89e3\u6cd5\u300d \u4fee\u8fad\u7d50\u69cb\uff0c\u4f9d\u5e8f\u5305\u62ec\u554f\u984c (problem) \u3001\u65b9\u6cd5 (solution) \u3001 \u4f86\u9810\u6e2c\u8ad6\u6587\u7c21\u4ecb\u4e2d\u53e5\u5b50\u7684\u6587\u6b65\uff0c\u4e26\u85c9\u4ee5\u958b\u767c\u4e00\u500b\u7dda\u4e0a\u8f14\u52a9\u5beb\u4f5c\u7cfb\u7d71 WriteAhead\u3002\u5728 \u56e0\u6b64\uff0c\u6709\u4e00\u4e9b\u7814\u7a76\u958b\u59cb\u5206\u6790\u8ad6\u6587\u7c21\u4ecb\u5982\u4f55\u9054\u6210\u5176\u6e9d\u901a\u7684\u4efb\u52d9\u3002Graetz (1985) \u767c\u73fe\u8ad6 \u5716 \u5716 \u5716 \u5716 1. Swales (1990) \u63d0\u51fa\u7684 \u63d0\u51fa\u7684 \u63d0\u51fa\u7684 \u63d0\u51fa\u7684 CARS \u6a21\u5f0f\u7684\u6587\u6b65\u8207\u8cc7\u8a0a\u5167\u5bb9 \u6a21\u5f0f\u7684\u6587\u6b65\u8207\u8cc7\u8a0a\u5167\u5bb9 \u6a21\u5f0f\u7684\u6587\u6b65\u8207\u8cc7\u8a0a\u5167\u5bb9 \u6a21\u5f0f\u7684\u6587\u6b65\u8207\u8cc7\u8a0a\u5167\u5bb9 \u76ee\u524d\u5df2\u7d93\u6709\u8a31\u591a\u5b78\u8853\u5beb\u4f5c\u6559\u6750\uff0c\u900f\u904e\u6587\u6b65\u5206\u6790\u4f86\u6559\u5c0e\u82f1\u6587\u975e\u6bcd\u8a9e\u7684\u5b78\u751f\uff0c\u5982\u4f55\u5beb\u4f5c \u500b\u90e8\u5206\uff0c\u63d0\u51fa\u4e00\u5957\u81ea\u52d5\u5316\u7684\u7d50\u69cb\u5206\u6790\u65b9\u6cd5\uff0c\u4e26\u958b\u767c\u4e00\u5957\u80fd\u5920\u8b93\u5b78\u751f\u4e00\u9762\u5beb\u4f5c\uff0c\u4e00\u9762\u7372\u5f97 \u56de\u9867\u4e4b\u524d\u8cc7\u8a0a\u3001\u9810\u544a\u4e4b\u5f8c\u8cc7\u8a0a \u5716 \u5716 \u5716 \u5716 2. WriteAhead \u63a1\u7528\u6587\u6b65\u8207 \u63a1\u7528\u6587\u6b65\u8207 \u63a1\u7528\u6587\u6b65\u8207 \u63a1\u7528\u6587\u6b65\u8207 CARS \u6a21\u5f0f\u6587\u6b65\u4e4b\u5c0d\u7167 \u6a21\u5f0f\u6587\u6b65\u4e4b\u5c0d\u7167 \u6a21\u5f0f\u6587\u6b65\u4e4b\u5c0d\u7167 \u5beb\u4f5c\u63d0\u793a\u7684\u96fb\u8166\u8f14\u52a9\u5beb\u4f5c\u7cfb\u7d71\u3002\u6211\u5011\u4e5f\u8a0e\u8ad6\u5982\u4f55\u5728\u53e5\u5b50\u4e2d\uff0c\u64f7\u53d6\u80fd\u53cd\u61c9\u4fee\u8fad\u7d50\u69cb\u7684\u7279\u5fb5\uff0c \u6a21\u5f0f\u6587\u6b65\u4e4b\u5c0d\u7167 \u4ee5\u6709\u52a9\u65bc\u7522\u751f\u8a13\u7df4\u8cc7\u6599\uff0c\u5c07\u53e5\u5b50\u6b78\u985e\u3002 \u8a31\u591a\u5b78\u8005\u90fd\u6307\u51fa\uff0c\u5728\u8868\u9762\u4e0a\u4ee5\u53ca\u5c0f\u7bc0\u5206\u6bb5\u4e0a\uff0c\u7814\u7a76\u8ad6\u6587\u5927\u81f4\u4e0a\u6709\u5171\u901a\u7684\u7c21\u55ae\u7d50\u69cb\u2500 \u2500IMRD \u7d50\u69cb\uff0c\u5373\u7c21\u4ecb (introduction) \u3001\u65b9\u6cd5 (method) \u3001\u7d50\u679c (results) \u3001\u8a0e\u8ad6 (discussion) \u3002 \u5b78\u8853\u8ad6\u6587(\u5982 \u6587\u4e26\u81ea\u52d5\u7522\u751f\u6279\u6539\u7684\u5efa\u8b70\u8207\u8a55\u5206\u3002\u4f46\u662f\u5f88\u5c11\u6709\u7cfb\u7d71\u80fd\u5920\u5728\u5b78\u751f\u5beb\u4f5c\u4e2d\uff0c\u4f9d\u7167\u6587\u6b65\u7684\u63a8\u9032\uff0c \u4e5f\u6709\u5b78\u8005\u9032\u4e00\u6b65\u95e1\u8ff0 IMRD \u7684\u4fee\u8fad\u7d50\u69cb\uff0c\u5c31\u50cf\u4e0a\u4e0b\u5bec\u5927\uff0c\u4e2d\u9593\u72f9\u7a84\u7684\u6c99\u6f0f\uff1a\u958b\u59cb\u6642\u5148 \u9069\u6642\u5730\u63d0\u4f9b\u5beb\u4f5c\u63d0\u793a\u8207\u8f14\u52a9\u3002\u76f4\u89ba\u4e0a\uff0c\u5982\u679c\u6211\u5011\u80fd\u5c07\u5927\u91cf\u7684\u8ad6\u6587\u7c21\u4ecb\u52a0\u4ee5\u8655\u7406\uff0c\u81ea\u52d5\u5316 \u5ee3\u5f8c\u5c08 (from general to specific) \uff0c\u7d50\u5c3e\u6642\u7531\u5c08\u800c\u5ee3 (from specific to general) \u3002Swales (1990) \u5206\u6790\u5176\u4e2d\u6bcf\u53e5\u7684\u6587\u6b65\uff0c\u7e7c\u800c\u5206\u6790\u7279\u5b9a\u6587\u6b65\u53e5\u5b50\u7684\u5e38\u898b\u7247\u8a9e\u6216\u53e5\u578b\uff0c\u6211\u5011\u5c07\u53ef\u4ee5\u5728\u5beb\u4f5c\u7684 \u66f4\u70ba\u7c21\u4ecb\u9019\u4e00\u500b\u5c0f\u7bc0\uff0c\u63d0\u51fa\u4e86\u6240\u8b02\u7684 CARS \u6a21\u5f0f(\u4ea6\u5373\u300c\u5275\u9020\u7814\u7a76\u7684\u7a7a\u9593\u300d\"Create a \u904e\u7a0b\uff0c\u6709\u6548\u5730\u5354\u52a9\u5b78\u751f\u3002 Research Space\") \u3002CARS \u6a21\u5f0f\u6b78\u7d0d\u4e86\u5178\u578b\u7684\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u4fee\u8fad\u7684\u52d5\u6a5f\u8207\u6a21\u5f0f\u3002CARS \u6a21 \u7136\u800c\uff0c\u904e\u53bb\u6240\u63d0\u51fa\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u65b9\u6cd5\uff0c\u90fd\u9700\u8cbb\u6642\u8cbb\u5de5\u6a19\u8a3b\u5927\u91cf\u8ad6\u6587\u3002\u6709\u9451\u65bc\u6b64\uff0c \u6211\u5011\u63d0\u51fa\u65b0\u65b9\u6cd5\uff0c\u4ee5\u964d\u4f4e\u4eba\u5de5\u6a19\u8a3b\u7684\u5de5\u4f5c\u91cf\uff0c\u4e14\u6a19\u6ce8\u4e4b\u8cc7\u6599\u5c07\u904b\u7528\u65bc\u8a13\u7df4\u7d71\u8a08\u5f0f\u5206\u985e\u5668\uff0c \u5f0f\u63d0\u51fa\u4e4b\u5f8c\uff0c\u5ee3\u6cdb\u5730\u70ba\u5b78\u8005\u63a1\u7528\u4f5c\u70ba\u5206\u6790\u8ad6\u6587\u300c\u7c21\u4ecb\u300d\u7bc0\u7684\u5beb\u4f5c\u4fee\u8fad\u7b56\u7565 (\u4f8b\u5982\uff0cCooper,</td></tr><tr><td>\u7a0b\u3002</td><td colspan=\"3\">Swales (1990) \u5206\u6790\u5927\u91cf\u7684\u8ad6\u6587\u7c21\u4ecb\uff0c\u6b78\u7d0d\u51fa\u4e00\u5957\u4fee\u8fad\u7684\u52d5\u6a5f\u8207\u6a21\u5f0f\uff1a\u300c\u5275\u9020\u7814\u7a76\u7a7a</td></tr><tr><td colspan=\"4\">\u9593\u300d(Create A Research Space, CARS)\u3002Swales \u8a8d\u70ba\u8ad6\u6587\u722d\u53d6\u7814\u7a76\u5f97\u5230\u8b80\u8005\u7684\u8a8d\u540c\uff0c \u6211\u5011\u671f\u671b\u6b64\u4e00\u81ea\u52d5\u6587\u6b65\u5206\u6790\u5de5\u5177\uff0c\u4ee5\u53ca WriteAhead \u7cfb\u7d71\uff0c\u6709\u52a9\u65bc\u63d0\u5347\u82f1\u6587\u975e\u6bcd\u8a9e \u6709\u5982\u74b0\u5883\u4e2d\u751f\u7269\u722d\u53d6\u751f\u5b58\u7a7a\u9593\u3002\u70ba\u6b64\uff0c\u5927\u90e8\u5206\u4f5c\u8005\u4f9d\u5faa\u4e09\u500b\u4fee\u8fad\u7684\u6b65\u9a5f\u2500\u2500\u4e5f\u5c31\u662f\u6587\u6b65 \u8005(non-native speakers, NNS)\u5beb\u4f5c\u5b78\u8853\u8ad6\u6587\u7684\u80fd\u529b\u3002\u5728\u672c\u8ad6\u6587\u4e2d\uff0c\u6211\u5011\u63d0\u51fa\u4e86\u4e00\u5957\u76e3\u7763 (moves)\u2500\u2500\u4f86\u8aaa\u670d\u8b80\u8005\u3002\u5982\u5716 1 \u6240\u793a\uff0c\u9019\u4e09\u500b\u6587\u6b65\u5305\u62ec\u4e86\u300c\u754c\u5b9a\u7814\u7a76\u7bc4\u570d\u300d\u3001\u300c\u5efa \u5f0f\u6a5f\u5668\u5b78\u7fd2\u7684\u65b9\u6cd5\uff0c\u80fd\u5920\u81ea\u52d5\u5730\u5b78\u7fd2\u5982\u4f55\u5c07\u8a9e\u6599\u5eab\u5167\u7684\u7c21\u4ecb\u53e5\u5b50\uff0c\u5927\u7565\u5730\u5206\u985e\u70ba\u5e7e\u500b\u6587 \u5716 \u5716 \u5716 \u5716 3. WriteAhead \u7cfb\u7d71\u64cd\u4f5c\u7bc4\u4f8b \u7cfb\u7d71\u64cd\u4f5c\u7bc4\u4f8b \u7cfb\u7d71\u64cd\u4f5c\u7bc4\u4f8b \u7cfb\u7d71\u64cd\u4f5c\u7bc4\u4f8b \u7acb\u5229\u57fa\u300d\u3001\u300c\u4f54\u64da\u5229\u57fa\u300d\u3002\u5728\u6bcf\u4e00\u500b\u6587\u6b65\u4e0b\uff0c\u53c8\u9700\u8981\u63cf\u8ff0\u82e5\u5e72\u5fc5\u8981\u6216\u9078\u9805\u7684\u5167\u5bb9\u3002\u53e6\u5916\uff0c \u7f8e\u570b\u570b\u5bb6\u91ab\u5b78\u5716\u66f8\u9928\uff0c\u4e5f\u4e3b\u5f35\u91ab\u5b78\u8ad6\u6587\u4f5c\u8005\uff0c\u61c9\u63d0\u4f9b\u5206\u6bb5\u6709\u6a19\u984c(labeled sections)\u7684\u7d50 \u69cb\u5316\u6458\u8981(structured abstract) 1 \u3002 \u6b65\u3002\u6709\u4e86\u5206\u985e\u7684\u53e5\u5b50\u4e4b\u5f8c\uff0c\u6211\u5011\u5c31\u53ef\u4ee5\u7d71\u8a08\u5404\u6587\u6b65\u7684 N \u9023\u8a5e (ngrams) \u8a5e\u983b\u3002\u5728 WriteAhead \u5716 3 \u986f\u793a WriteAhead \u7cfb\u7d71\u7684\u64cd\u4f5c\u5be6\u4f8b\u3002\u5728\u5716\u4e2d\uff0c\u4f7f\u7528\u8005\u5df2\u7d93\u4ecb\u7d39\u4e86\u7814\u7a76\u80cc\u666f \u7cfb\u7d71\uff0c\u5373\u53ef\u53c3\u8003\u4f7f\u7528\u8005\u9078\u64c7\u7684\u6587\u6b65\uff0c\u4ee5\u53ca\u6e38\u6a19\u4e4b\u524d\u7684\u5167\u5bb9\uff0c\u63d0\u793a\u55ae\u5b57\u4ee5\u53ca\u63a5\u7e8c\u7247\u8a9e\u3002 (BKG \u6587\u6b65)\uff0c\u63a5\u8457\u4f7f\u7528\u8005\u9078\u64c7\u4e86\u300c\u672c\u8ad6\u6587\u6587\u6b65\u300d (OWN)\uff0c\u7e7c\u800c\u8f38\u5165\"In this paper\" \u7b49</td></tr><tr><td colspan=\"4\">\u5b57\u3002\u6839\u64da\u9019\u4e9b\u8cc7\u8a0a\uff0cWriteAhead \u986f\u793a\u4e86\u9069\u5408\u6b64\u4e00\u8108\u7d61\u7684\u63d0\u793a\u5982\u4e0b\uff0c\u4f5c\u70ba\u7e7c\u7e8c\u5beb\u4f5c\u7684\u53c3\u8003\uff1a</td></tr><tr><td/><td colspan=\"2\">, we present</td><td>, we describe</td><td>, we explore</td></tr><tr><td/><td colspan=\"2\">, we propose</td><td>, we will</td><td>, we show</td></tr></table>",
"type_str": "table"
},
"TABREF2": {
"text": "Researchers have successfully applied ANN techniques across abroad spectrum of problem domains .",
"num": null,
"html": null,
"content": "<table><tr><td>46</td><td>\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a \u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a \u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a \u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a</td><td>\u9ec3\u51a0\u8aa0 \u7b49 39 \u9ec3\u51a0\u8aa0 \u7b49 41 43 \u9ec3\u51a0\u8aa0 \u7b49 45 \u9ec3\u51a0\u8aa0 \u7b49</td></tr><tr><td colspan=\"3\">) \u8a13\u7df4\u8cc7\u6599\u9644\u52a0\u7279\u5fb5\u503c (6) \u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b \u5716 \u5716 \u5716 \u5716 4. \u8a13\u7df4\u6a21\u7d44\u7684\u6d41\u7a0b (\u7b2c 3.2.5 \u7bc0) (\u7b2c 3.2.6 \u7bc0) \u8a13\u7df4\u6a21\u7d44\u7684\u6d41\u7a0b \u8a13\u7df4\u6a21\u7d44\u7684\u6d41\u7a0b \u8a13\u7df4\u6a21\u7d44\u7684\u6d41\u7a0b 3.2.1 \u5f9e\u7db2\u8def\u6536\u96c6\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb \u5f9e\u7db2\u8def\u6536\u96c6\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb \u5f9e\u7db2\u8def\u6536\u96c6\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb \u5f9e\u7db2\u8def\u6536\u96c6\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb \u5728\u8a13\u7df4\u904e\u7a0b\u7684\u7b2c\u4e00\u6b65\uff0c\u6211\u5011\u6536\u96c6\u5927\u91cf\u7684\u7814\u7a76\u8ad6\u6587\uff0c\u4ee5\u8a13\u7df4\u6587\u6b65\u5206\u985e\u5668\u3002\u70ba\u6b64\uff0c\u6211\u5011\u9078\u64c7 \u6709\u5f59\u6574\u8ad6\u6587\u53ef\u4f9b\u76f4\u63a5\u4e0b\u8f09\u7684\u5b78\u6703\u7db2\u7ad9\uff0c\u4e14\u53d6\u5f97\u7d93\u904e PDF \u6a94\u6848\u8f49\u63db\u6216\u5149\u5b78\u5b57\u5143\u8b58\u5225 (OCR) \u8655\u7406\u7684\u8ad6\u6587\u6587\u5b57\u6a94\u3002\u7136\u800c\uff0c\u901a\u5e38\u6a94\u6848\u90fd\u672a\u6a19\u660e\u7bc0\u8cc7\u8a0a\u3002\u6211\u5011\u5229\u7528\u7c21\u55ae\u898f\u5247\uff0c\u5927\u81f4\u4e0a\u8fa8\u8b58 \u51fa\u7bc0\u6a19\u984c\uff0c\u4e26\u64f7\u53d6\u8ad6\u6587\u300c\u7c21\u4ecb\u300d\u7684\u90e8\u4efd\u3002 3.2.2 \u64f7\u53d6\u7c21\u4ecb\u5e38\u898b\u53e5\u578b \u64f7\u53d6\u7c21\u4ecb\u5e38\u898b\u53e5\u578b \u64f7\u53d6\u7c21\u4ecb\u5e38\u898b\u53e5\u578b \u64f7\u53d6\u7c21\u4ecb\u5e38\u898b\u53e5\u578b \u5728\u8a13\u7df4\u7684\u7b2c\u4e8c\u6b65\uff0c\u6211\u5011\u5229\u7528\u73fe\u6709\u7684\u53e5\u5b50\u5206\u5272\u7a0b\u5f0f\uff0c\u5c07\u524d\u4e00\u6b65\u9a5f\u53d6\u5f97\u7684\u8ad6\u6587\u7c21\u4ecb\uff0c\u5206\u5272\u6210 \u4e00\u53e5\u4e00\u53e5\u3002\u7136\u5f8c\uff0c\u518d\u9010\u53e5\u9032\u884c\u5207\u5272\u8a5e\u5f59(tokenization) \u3001\u6a19\u793a\u8a5e\u6027(part of speech tagging) \u8207\u57fa\u5e95\u7247\u8a9e(base phrases \u6216 chunks)\u64f7\u53d6\u7684\u9810\u8655\u7406\u4f5c\u696d\u3002 \u7531\u65bc\u5c08\u6709\u540d\u8a5e(\u5982\u4f5c\u8005\u540d)\u4ee5\u53ca\u6578\u5b57(\u4f8b\u5982\u5e74\u5ea6\uff0c\u6216\u7bc0\u3001\u5716\u8868\u7de8\u865f)\u8b8a\u5316\u6027\u5927\uff0c\u4ee5 \u53ca\u540d\u8a5e(\u5982 method, approach \u7b49)\u4e4b\u524d\uff0c\u5e38\u6709\u5404\u5f0f\u7684\u5f62\u5bb9\u8a5e(\u5982 new, novel)\u3002\u9019\u4e9b\u73fe \u8c61\u90fd\u6703\u5c0e\u81f4\u53e5\u578b\u767c\u6563\uff0c\u4e0d\u6613\u6b78\u985e\u6210\u5e38\u898b\u53e5\u578b\u3002\u70ba\u4e86\u6709\u6548\u6b78\u7d0d\u5e38\u898b\u53e5\u578b\uff0c\u5c0d\u65bc\u53e5\u5b50\u5167\u7684\u8a5e \u5f59\uff0c\u6211\u5011\u505a\u4ee5\u4e0b\u7684\u8655\u7406\uff1a \u2022 \u5c08\u6709\u540d\u8a5e\u3001\u6578\u5b57\u8a5e\u66ff\u63db\u70ba\u5176\u8a5e\u6027\u6a19\u7c64(\u5373 NE, CD) \u2022 \u540d\u8a5e\u7247\u8a9e\u3001\u52d5\u8a5e\u7247\u8a9e\uff0c\u53bb\u9664\u4fee\u98fe\u8a9e\u7684\u90e8\u4efd\uff0c\u53ea\u7559\u4e0b\u4e2d\u5fc3\u8a9e \u2022 \u8907\u6578\u540d\u8a5e\u66ff\u63db\u70ba\u55ae\u6578\u540d\u8a5e \u2022 \u4e0d\u540c\u6642\u614b\u7684\u52d5\u8a5e\u66ff\u63db\u70ba\u539f\u5f62\u52d5\u8a5e \u4f8b\u5982\uff0c\u6211\u5011\u6703\u5c07\u539f\u59cb\u7684\u53e5\u5b50 (1) \u66ff\u63db\u70ba (2) \u4e4b\u5f8c\uff0c\u64f7\u53d6 N \u9023\u8a5e(ngram)\u3002\u9664\u4e86\u8003 \u616e N \u9023\u8a5e\u983b\u7387\uff0c\u6211\u5011\u4e5f\u8a08\u7b97\u76f8\u9130\u8a5e\u8a9e\u8a5e\u4e4b\u9593\u7684\u76f8\u4e92\u8cc7\u8a0a(mutual information)\uff0c\u7be9\u9078\u6240 \u5f97\u7684\u5e38\u898b\u53e5\u578b\u8207\u7247\u8a9e\uff0c\u5927\u90fd\u6709\u4fee\u8fad\u7684\u529f\u80fd\uff0c\u800c\u4e14\u76f4\u89ba\u4e0a\u5c0d\u5beb\u4f5c\u5f88\u6709\u5e6b\u52a9\u7684\u591a\u5b57\u8a5e\u8a9e (multiword expressions)\u6216\u77ed\u8a5e\u4e32(lexical bundles)\u3002 \u4eba\u5de5\u6a19\u8a18\u5e38\u898b\u53e5\u578b\u4e4b\u6587\u6b65 \u4eba\u5de5\u6a19\u8a18\u5e38\u898b\u53e5\u578b\u4e4b\u6587\u6b65 \u4eba\u5de5\u6a19\u8a18\u5e38\u898b\u53e5\u578b\u4e4b\u6587\u6b65 \u5728\u8a13\u7df4\u7684\u7b2c\u4e09\u6b65\u9a5f\uff0c\u6211\u5011\u6311\u9078\u4e00\u4e9b\u9ad8\u983b\u4e14\u6587\u6b65\u7279\u6027\u660e\u986f\u7684\u7247\u8a9e\u4e26\u624b\u52d5\u5730\u6a19\u8a18\u4e0a\u6587\u6b65\u3002\u5728 \u6b64\u968e\u6bb5\uff0c\u6211\u5011\u5c07\u6587\u6b65\u5206\u70ba\u80cc\u666f(BKG)\u3001\u672c\u8ad6\u6587(OWN)\u3001\u8a0e\u8ad6(DIS)\u3001\u6587\u672c(TEX) \u56db\u7a2e\u985e\u578b\u3002 BKG \u90e8\u5206\u63cf\u8ff0\u9818\u57df\u3001\u8ab2\u984c\u3001\u7f3a\u53e3\u3001\u6587\u737b\uff0cOWN \u90e8\u5206\u63cf\u8ff0\u672c\u8ad6\u6587\u4e4b\u65b9\u6cd5\u3001\u7d50 \u679c\uff0cDIS \u90e8\u5206\u8a0e\u8ad6\u672c\u8ad6\u6587\u8207\u524d\u4eba\u4e4b\u512a\u52a3\u7570\u540c\uff0cTEX \u90e8\u5206\u63cf\u8ff0\u5168\u6587\u6216\u7bc0\u7684\u76ee\u7684\u8207\u7d44\u7e54\u3002 \u8868 1 \u986f\u793a\u6a19\u4e86\u6587\u6b65\u7684\u7247\u8a9e\u7bc4\u4f8b\uff0c\u4ee5\u53ca\u6a19\u7c64\u7684\u7c21\u55ae\u5b9a\u7fa9\u3002\u6240\u4ee5\u9019\u500b\u968e\u6bb5\u7684\u6a19\u8a3b\u5c0d\u8c61\u662f\u8655\u7406\u904e 3.2.5 \u9644\u52a0\u8a13\u7df4\u8cc7\u6599\u4e4b\u7279\u5fb5\u503c \u9644\u52a0\u8a13\u7df4\u8cc7\u6599\u4e4b\u7279\u5fb5\u503c \u9644\u52a0\u8a13\u7df4\u8cc7\u6599\u4e4b\u7279\u5fb5\u503c \u9644\u52a0\u8a13\u7df4\u8cc7\u6599\u4e4b\u7279\u5fb5\u503c \u5728\u8a13\u7df4\u7684\u7b2c\u4e94\u968e\u6bb5\uff0c\u6211\u5011\u8981\u9644\u52a0\u7279\u5fb5\u503c\u5230\u8a13\u7df4\u8cc7\u6599\u4ee5\u7528\u4f86\u8a13\u7df4\u6a19\u8a18\u6587\u6b65\u6a21\u578b\u3002\u6211\u5011\u5f9e\u53e5 \u5b50\u4e2d\u6240\u62bd\u51fa N \u9023\u8a5e\u7279\u5fb5\u503c\u3002\u8868 3 \u70ba N \u9023\u8a5e\u7279\u5fb5\u503c\u7684\u4f8b\u5b50\u3002\u70ba\u4e86\u8b93\u7279\u5fb5\u503c\u66f4\u80fd\u53cd\u61c9\u6587 \u6b65\uff0c\u6211\u5011\u4e5f\u52a0\u5165\u8a5e\u985e\u3001\u8a9e\u610f\u5206\u985e(Word class)\u7684\u7279\u5fb5\u503c\u3002\u6211\u5011\u5229\u7528 Teufel(1999)\u4e2d \u4eba\u5de5\u7de8\u8f2f\u7684\u4e00\u7d44\u5b78\u8853\u8ad6\u6587\u7684\u5206\u985e\u8a5e\u5f59\u3002\u8868 4 \u70ba\u6211\u5011\u6240\u4f7f\u7528\u7684 \u8a9e\u610f\u5206\u985e(Word class)\u7684 \u7279\u5fb5\u503c\u3002 4. \u5be6\u9a57\u8207\u7d50\u679c \u5be6\u9a57\u8207\u7d50\u679c \u5be6\u9a57\u8207\u7d50\u679c \u5be6\u9a57\u8207\u7d50\u679c \u6211\u5011\u8a2d\u8a08 WriteAhead \u7684\u521d\u8877\uff0c\u662f\u70ba\u4e86\u63d0\u793a\u4f7f\u7528\u8005\u63a5\u8457\u53ef\u4ee5\u5beb\u7684\u6578\u500b\u5b57\u8a5e\uff0c\u4ee5\u8f14\u52a9\u5b78\u7fd2 \u8005\u5beb\u4f5c\u5b78\u8853\u8ad6\u6587\u7684\u300c\u7c21\u4ecb\u300d\u3002\u56e0\u6b64\uff0c\u6211\u5011\u64f7\u53d6\u7d93\u904e\u5be9\u67e5\u3001\u7de8\u8f2f\u7684\u7a0b\u5e8f\uff0c\u767c\u8868\u7684\u5b78\u8853\u8ad6\u6587\uff0c \u4f86\u5be6\u4f5c\u6211\u5011\u63d0\u51fa\u7684\u65b9\u6cd5\uff0c\u4ee5\u53ca\u958b\u767c\u5beb\u4f5c\u8f14\u52a9\u7cfb\u7d71\u3002\u672c\u7bc0\u4e2d\uff0c\u6211\u5011\u63cf\u8ff0\u6a21\u7d44\u8a13\u7df4\u7684\u5be6\u9a57\u8a2d \u5b9a(\u7b2c 4.1 \u7bc0)\uff0c\u4ee5\u53ca\u521d\u6b65\u5be6\u9a57\u7684\u6548\u80fd\u8a55\u4f30\u8207\u7d50\u679c(\u7b2c 4.2 \u7bc0)\u3002 \u6211\u5011\u85c9\u7531\u8a13\u7df4\u6240\u5f97\u7684\u6587\u6b65\u6a19\u8a3b\u6a21\u7d44\uff0c\u5c0d\u4e00\u842c\u7bc7\u7c21\u4ecb\u4e2d\u7684\u6bcf\u4e00\u53e5\u9032\u884c\u6587\u6b65\u6a19\u8a3b\u3002\u6700\u5f8c \u6211\u5011\u7d71\u8a08\u5404\u7a2e\u6587\u6b65\u4e2d\u7684 N \u9023\u8a5e\u8cc7\u8a0a\uff0c\u6211\u5011\u7e7c\u800c\u5c07\u4e00\u842c\u591a\u7bc7\u7c21\u4ecb\u5167\u7684\u53e5\u5b50\uff0c\u9010\u53e5\u505a\u6587\u6b65\u7684 \u5206\u985e\uff0c\u904b\u7528\u65bc WriteAhead \u5beb\u4f5c\u8f14\u52a9\u7cfb\u7d71\u3002 4.2 \u8a55\u4f30 \u8a55\u4f30 \u8a55\u4f30 \u8a55\u4f30\u8207\u8a0e\u8ad6 \u8207\u8a0e\u8ad6 \u8207\u8a0e\u8ad6 \u8207\u8a0e\u8ad6 \u5982\u524d\u6240\u8ff0\uff0cWriteAhead \u7684\u8a2d\u8a08\u76ee\u6a19\u662f\u8f14\u52a9\u5b78\u7fd2\u8005\u5beb\u4f5c\u5b78\u8853\u8ad6\u6587\u7684\u300c\u7c21\u4ecb\u300d\uff0c\u6240\u4ee5\u61c9\u8a72\u8a55 5. \u7d50\u8ad6 \u7d50\u8ad6 \u7d50\u8ad6 \u7d50\u8ad6 \u5c0d\u65bc\u5982\u4f55\u6539\u5584\u6211\u5011\u6240\u63d0\u51fa\u7684\u7cfb\u7d71\uff0c\u6211\u5011\u9810\u898b\u8a31\u591a\u53ef\u80fd\u7684\u672a\u4f86\u7814\u7a76\u65b9\u5411\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u904b\u7528 \u65e2\u6709\u7684\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u6280\u8853\uff0c\u64f7\u53d6\u66f4\u5177\u6548\u679c\u7684\u7279\u5fb5\u503c\uff0c\u4f86\u63d0\u5347\u6587\u6b65\u5206\u985e\u7684\u6b63\u78ba\u7387\u3002\u4f8b\u5982\uff0c \u6211\u5011\u53ef\u4ee5\u81ea\u52d5\u7522\u751f\u5beb\u4f5c\u6587\u9ad4\u4e4b\u5206\u985e\u8a5e\u5f59\u7fa4\u3002\u4e26\u4e14\uff0c\u6839\u64da\u5206\u985e\u8a5e\u5f59\u7fa4\uff0c\u64f7\u53d6\u8a5e\u7fa4\u5f0f\u7684\u5e38\u898b \u6a23\u677f(class-based patterns)\uff0c\u7528\u4f86\u5e6b\u52a9\u5206\u985e\u7684\u6b63\u78ba\u6027\uff0c\u4ee5\u53ca\u63d0\u4f9b\u5bcc\u542b\u8cc7\u8a0a\u7684\u5beb\u4f5c\u63d0\u793a\u3002 \u53e6\u5916\u4e00\u500b\u6709\u6f5b\u529b\u7684\u7814\u7a76\u65b9\u5411\uff0c\u662f\u8b93\u4f7f\u7528\u8005\u5728\u53e6\u4e00\u500b\u6587\u5b57\u6846\uff0c\u8f38\u5165\u6bcd\u8a9e(\u5982\u4e2d\u6587\u3001\u65e5\u6587) \u9644\u9304 \u9644\u9304 \u9644\u9304 \u9644\u9304 A \u6574\u5408\u5e38\u898b\u53e5\u578b\u7684\u5beb\u4f5c\u6a23\u677f \u6574\u5408\u5e38\u898b\u53e5\u578b\u7684\u5beb\u4f5c\u6a23\u677f \u6574\u5408\u5e38\u898b\u53e5\u578b\u7684\u5beb\u4f5c\u6a23\u677f \u6574\u5408\u5e38\u898b\u53e5\u578b\u7684\u5beb\u4f5c\u6a23\u677f \u6211\u5011\u64f7\u53d6\u5e38\u898b\u53e5\u578b\u6a19\u793a\u6587\u6b65\u4e4b\u5f8c\uff0c\u767c\u73fe\u8a31\u591a\u53e5\u578b\u5f88\u985e\u4f3c\uff0c\u53ea\u6709\u5c11\u6578\u7684\u5e7e\u500b\u5b57\u8b8a\u52d5\u3002\u6211\u5011 \u53ef\u5c07\u9019\u4e9b\u53e5\u578b\u805a\u96c6\u8d77\u4f86\uff0c\u6b78\u7d0d\u6574\u5408\u6210\u70ba\u6b63\u898f\u5f0f\u6a23\u677f(regular expression patterns)\u3002\u9019\u4e9b \u6a23\u677f\u907f\u514d\u7f85\u5217\u8a31\u591a\u53e5\u578b\u7684\u4e0d\u4fbf\uff0c\u4e00\u76ee\u4e86\u7136\u2500\u2500\u65e2\u4ee3\u8868\u4e86\u5beb\u4f5c\u7684\u5e38\u614b\uff0c\u4e5f\u5448\u73fe\u4e86\u5404\u7a2e\u8b8a\u5316\u3002 \u904b\u7528\u5728\u6559\u5b78\u4e0a\u8b93\u5b78\u751f\u5b78\u7fd2\u5f88\u6709\u6548\u679c\uff0c\u5beb\u4f5c\u6642\u4e5f\u5bb9\u6613\u52a0\u4ee5\u6a21\u4eff\u3001\u6539\u5beb\u3002 \u4f8b\u5982\uff0c\u5f9e\u9644\u9304 B \u4e2d\u6211\u5011\u53ef\u4ee5\u770b\u5230\u4e0b\u9762\u5de6\u908a\u9019\u4e9b\u548c\u6642\u9593\u6709\u95dc\u7684\u53e5\u578b\u3002\u7d93\u904e\u89c0\u5bdf\u8207\u6b78 \u9644\u9304 \u9644\u9304 \u9644\u9304 \u9644\u9304 B \u5404\u7a2e \u5404\u7a2e \u5404\u7a2e \u5404\u7a2e\u6587\u6b65\u7684\u5e38\u898b\u53e5\u578b \u6587\u6b65\u7684\u5e38\u898b\u53e5\u578b \u6587\u6b65\u7684\u5e38\u898b\u53e5\u578b \u6587\u6b65\u7684\u5e38\u898b\u53e5\u578b B.1 \u80cc\u666f\u6587\u6b65 \u80cc\u666f\u6587\u6b65 \u80cc\u666f\u6587\u6b65 \u80cc\u666f\u6587\u6b65 follow NE ( CD ) , NE ( CD ) show that NE ( CD ) demonstrate on hand , approach currently , there be this , however , it know that as alternative , over year , however , since in decade , however , study method in paper , we argue that in paper , we propose model in paper we focus on in paper , we present in paper we show that in paper we describe work present in paper in paper , we we also show that paper propose method for in paper we discuss in paper we investigate in paper we propose to achieve goal , thus , method finally , result experiment show that work focus on goal be to claim be that result indicate that therefore , method evaluation show that result show that we evaluate approach we show that remainder of paper organise as follow in CD , we describe system rest of paper organize as follow outline of paper be as follow paper organize as follow : CD structure of paper be as in CD we present result for example , CD show in section of paper , paper organize as follow : in CD we show that we conclude paper in CD in rest of paper , finally , we present result approach describe in CD in CD we introduce paper structure as follow in CD we describe conclusion draw in CD result give in CD after that , CD present evaluation CD describe experiment CD describe setup in section , CD show how CD describe work CD describe approach CD give result CD discuss work (1) 3.2.3 \u4eba\u5de5\u6a19\u8a18\u5e38\u898b\u53e5\u578b\u4e4b\u6587\u6b65 \u5f8c\u7684\u7247\u8a9e\u3002\u4eba\u5de5\u6a19\u8a3b\u7684\u904e\u7a0b\u4e2d\uff0c\u5f88\u96e3\u63a7\u5236\u6a19\u8a3b\u7684\u54c1\u8cea\uff0c\u56e0\u6b64\u6a19\u8a3b\u8005\u4e4b\u9593\u7684\u4e00\u81f4\u6027\uff0c\u9700\u7d93 \u53cd\u8986\u7684\u6838\u5c0d\uff0c\u8abf\u89e3\u6709\u885d\u7a81\u7684\u6a19\u8a18 \u3002 \u8868 \u8868 \u8868 \u8868 1. \u6709\u6587\u6b65\u6a19\u8a18\u4e4b\u53e5\u578b\u7bc4\u4f8b \u6709\u6587\u6b65\u6a19\u8a18\u4e4b\u53e5\u578b\u7bc4\u4f8b \u6709\u6587\u6b65\u6a19\u8a18\u4e4b\u53e5\u578b\u7bc4\u4f8b \u6709\u6587\u6b65\u6a19\u8a18\u4e4b\u53e5\u578b\u7bc4\u4f8b \u6587\u6b65 \u53e5\u578b \u89e3\u91cb TEX in section , we review work \u6587\u672c\uff1a\u63cf\u8ff0\u5168\u6587\u6216\u7bc0\u7684\u76ee\u7684\u8207\u7d44\u7e54 BKG research support in part by NE \u80cc\u666f\uff1a\u63cf\u8ff0\u9818\u57df\u3001\u8ab2\u984c\u3001\u7f3a\u53e3\u3001\u6587\u737b DIS it be important to note that \u8a0e\u8ad6\uff1a\u8a0e\u8ad6\u672c\u8ad6\u6587\u8207\u524d\u4eba\u4e4b\u512a\u52a3\u7570\u540c TEX rest of paper structure as follow OWN in paper , we propose approach \u672c\u6587\uff1a\u63cf\u8ff0\u672c\u8ad6\u6587\u4e4b\u65b9\u6cd5\u3001\u7d50\u679c BKG follow NE ( CD ) , 3.2.4 \u7522\u751f\u6709\u6587\u6b65\u6a19\u793a\u4e4b\u8a13\u7df4\u8cc7\u6599 \u7522\u751f\u6709\u6587\u6b65\u6a19\u793a\u4e4b\u8a13\u7df4\u8cc7\u6599 \u7522\u751f\u6709\u6587\u6b65\u6a19\u793a\u4e4b\u8a13\u7df4\u8cc7\u6599 \u7522\u751f\u6709\u6587\u6b65\u6a19\u793a\u4e4b\u8a13\u7df4\u8cc7\u6599 \u5728\u8a13\u7df4\u7684\u7b2c\u56db\u6b65\u9a5f\uff0c\u6211\u5011\u5229\u7528\u6709\u6a19\u8a18\u7684\u53e5\u578b\u53bb\u5339\u914d\u5927\u91cf\u8ad6\u6587\u7c21\u4ecb\u53e5\u5b50\uff0c\u4e26\u5c07\u53e5\u578b\u7684\u6587\u6b65 \u6a19\u8a3b\u5230\u53e5\u5b50\u4e0a\u9762\u3002\u5339\u914d\u7684\u539f\u5247\u662f\u6108\u9577\u7684\u53e5\u578b\u6108\u512a\u5148\u3002\u6211\u5011\u5229\u7528\u53e5\u578b\u4f86\u7522\u751f\u5927\u91cf\u6709\u6a19\u8a18\u6587 \u6b65\u7684\u53e5\u5b50\uff0c\u7528\u4ee5\u505a\u70ba\u4e4b\u5f8c\u6a21\u7d44\u7684\u8a13\u7df4\u8cc7\u6599\u3002\u8868 2 \u70ba\u5339\u914d\u6210\u529f\u7684\u53e5\u5b50\u7684\u7bc4\u4f8b\u3002\u9019\u500b\u968e\u6bb5\u7684 \u6a19\u8a3b\u7bc4\u570d\u662f\u55ae\u53e5\u3002 \u8868 \u8868 \u8868 \u8868 2.\u53e5\u578b \u53e5\u578b \u53e5\u578b \u53e5\u578b\u5c0d\u61c9\u53e5\u5b50\u7684\u7bc4\u4f8b \u5c0d\u61c9\u53e5\u5b50\u7684\u7bc4\u4f8b \u5c0d\u61c9\u53e5\u5b50\u7684\u7bc4\u4f8b \u6587\u6b65 \u53e5\u578b \u5339\u914d\u53e5\u5b50 TEX in section , we review work In the next section, we will first review some related works. BKG in year , there be In recent years, there has been a rapid growth of interest in the sociological study of childhood. OWN in paper , we propose approach In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. 3.2.6 \u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b \u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b \u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b \u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b \u76ee\u524d\u6709\u8a31\u591a\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u53ef\u4ee5\u8655\u7406\u5206\u985e\u7684\u554f\u984c\u3002\u57fa\u672c\u7684\u76e3\u7763\u5f0f\u7684\u65b9\u6cd5\u9700\u8981\u6b63\u78ba\u7684\u5206\u985e\u8cc7 \u8a0a\uff0c\u975e\u76e3\u7763\u5f0f\u65b9\u6cd5\u5247\u4e0d\u9700\u8981\u6709\u6b63\u78ba\u7b54\u6848\u3002\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u63a1\u7528\u76e3\u7763\u5f0f\u8a13\u7df4\u65b9\u6cd5\uff0c\u4f46\u662f \u53e5\u5b50\uff0c\u4f5c\u70ba\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u6240\u9700\u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u4e26\u4f7f\u7528\u6700\u5927\u71b5\u6a21\u578b(Maximum Entropy, ME)\u4f86\u8a13\u7df4\u6587\u6b65\u5206\u985e\u5668\u3002 \u8a13\u7df4\u5b8c\u6210\u5f8c\uff0c\u6211\u5011\u5c31\u904b\u7528\u6b64\u4e00\u5206\u985e\u5668\uff0c\u5c07\u8a9e\u6599\u5eab\u5167\u6240\u6709\u7684\u8ad6\u6587\u53e5\u5b50\uff0c\u52a0\u4ee5\u5206\u985e\uff0c\u6a19 \u8a3b\u4e0a\u9069\u7576\u7684\u6587\u6b65\u3002\u4e4b\u5f8c\uff0c\u6211\u5011\u5c31\u53ef\u4ee5\u904b\u7528\u9019\u4e9b\u9644\u6709\u6587\u6b65\u6a19\u7c64\u7684\u53e5\u5b50\uff0c\u4f86\u7d71\u8a08\u5404\u7a2e\u6587\u6b65\u7684 \u5e38\u898b N \u9023\u8a5e\u3002\u4e4b\u5f8c\uff0cWriteAhead \u7cfb\u7d71\u5728\u8f14\u52a9\u5beb\u4f5c\u6642\uff0c\u5c07\u53c3\u7167\u4f7f\u7528\u8005\u8a2d\u5b9a\u7684\u6587\u6b65\uff0c\u4e26\u6839 \u64da\u8f38\u5165\u7684\u5167\u5bb9\uff0c\u67e5\u8a62\u9069\u7576\u7684\u7247\u8a9e\u63d0\u4f9b\u7d66\u5b78\u7fd2\u8005\u53c3\u8003\u3002 \u7684\u53e5\u578b\u3002\u6211\u5011\u4eba\u5de5\u7684\u6311\u9078\u4e86\u4e94\u767e\u500b\u53e5\u578b\u5f8c\uff0c\u624b\u52d5\u6ffe\u6389\u6587\u6b65\u7279\u6027\u4e0d\u660e\u986f\u5f97\u7684\u7247\u8a9e\u4e26\u628a\u5269\u4e0b \u7684\u53e5\u578b\u90fd\u6a19\u4e0a\u6587\u6b65\uff0c\u5269\u4e0b\u8fd1\u7d04\u56db\u767e\u500b\u6709\u6587\u6b65\u6a19\u8a18\u7684\u53e5\u578b\u3002\u6211\u5011\u5728\u5229\u7528\u9019\u4e9b\u6a19\u8a18\u904e\u7684\u53e5\u578b \u53bb\u5339\u914d\u4e00\u842c\u7bc7\u7684\u8ad6\u6587\u7c21\u4ecb\u3002\u6211\u5011\u5f97\u5230\u5927\u7d04\u4e00\u842c\u516b\u5343\u500b\u53e5\u5b50\uff0c\u5176\u6587\u6b65\u7684\u5206\u4f48\u5982\u8868 5 \u6240\u793a\u3002 \u6a19\u8a3b\u6a21\u7d44\u3002 system goal in paper be to solution be to paper provide in CD , we present approach finally , we draw conclusion paper organize as follow CD present work \u65bc\u8cc7\u6599\uff0c\u672c\u7cfb\u7d71\u61c9\u8a72\u5c0d\u975e\u8cc7\u8a0a\u9818\u57df(\u4f8b\u5982\u6587\u5b78\u3001\u7ba1\u7406\u5b78\u3001\u6559\u80b2\u5b78)\u7684\u9069\u7528\u6027\u61c9\u8a72\u4e0d\u662f\u5f88 in paper , we describe goal of paper be to therefore , we we demonstrate follow experiment discussion present in CD CD present result \u696d\u9818\u57df\u7279\u6b8a\u6027\u7684\u5f71\u97ff\u3002\u4f46\u662f\uff0c\u500b\u5225\u9818\u57df\u8868\u9054\u7684\u65b9\u5f0f\u5728\u7528\u5b57\u9063\u8a5e\u4ecd\u7136\u6709\u4e0d\u5c0f\u7684\u5dee\u7570\uff0c\u53d7\u9650 in paper , we show how in paper , we investigate purpose be to part of paper organize as CD present result of work discuss in CD CD describe system paper describe CRFs \u3002\u672c\u8ad6\u6587\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\uff0c\u662f\u57fa\u65bc\u8de8\u9818\u57df\u7684\u8ad6\u6587\u4fee\u8fad\u7814\u7a76\uff0c\u61c9\u8a72\u4e0d\u6703\u53d7\u4e0d\u540c\u5b78\u8853\u5c08 in study , we focus on goal of work be to paper describe system follow result finally , in CD finally , we aim of \u672c \u8ad6 \u6587 \u6240 \u4f7f \u7528 \u7684 \u5206 \u985e \u5668 \u662f Maximal Entropy \uff0c \u672a \u4f86 \u4e5f \u5c07 \u8003 \u616e \u63a1 \u7528 SVM \u6216 \u662f in paper , we focus on in paper , we propose in paper we focus be rest of paper organise as section present and discuss in rest of paper plan of paper \u518d\u5c07\u6a19\u8a18\u597d\u7684\u53e5\u5b50\u9644\u52a0\u4e0a\u7279\u5fb5\u503c N-gram\u3001\u8a5e\u8a9e\u5206\u985e\u5f8c\uff0c\u8b93 ME \u6a21\u7d44\u505a\u8a13\u7df4\uff0c\u7372\u5f97\u6587\u6b65 that in paper , we study we demonstrate that purpose of as follow finally , CD conclude paper paper organise as follow CD show result \u8005\u7684\u6548\u679c\u3002\u4e0d\u904e\u6211\u5011\u8a8d\u70ba\uff0c\u9ad8\u983b N \u9023\u8a5e\u7684\u7cbe\u78ba\u7387\u53ef\u80fd\u9060\u9ad8\u65bc\u6587\u6b65\u6a19\u793a\u7684\u7cbe\u78ba\u7387\u3002 in particular , we show in work , we use paper focus on paper present remainder of paper structure in CD we present experiment in section CD , CD describe method \u5408\u7406\u3002\u53d7\u9650\u65bc\u6642\u9593\uff0c\u6211\u5011\u5c1a\u672a\u8a55\u4f30 WriteAhead \u904b\u7528\u5404\u5206\u985e\u9ad8\u983b N \u9023\u8a5e\uff0c\u5c0d\u65bc\u63d0\u793a\u4f7f\u7528 system in work we focus on in study , in CD , we present model next , in CD , finally CD conclude paper CD review work in work \u96d6\u7136\u500b\u5225\u53e5\u5b50\u7684\u5206\u985e\u6b63\u78ba\u6027\u4e0d\u7406\u60f3\uff0c\u6211\u5011\u89c0\u5bdf\u7d71\u8a08\u5f8c\u7684\u5404\u5206\u985e\u4e4b\u9ad8\u983b N \u9023\u8a5e\u9084\u7b97 in paper , we present in paper , we address we propose that follow we discuss work in CD article organize as follow CD discuss result in paper \u6211\u5011\u4e26\u4e0d\u76f4\u63a5\u4eba\u5de5\u6a19\u8a3b\u6b63\u78ba\u7b54\u6848\u3002\u6211\u5011\u900f\u904e\u6a19\u8a3b\u5c11\u91cf\u53e5\u578b\uff0c\u9593\u63a5\u5730\u81ea\u52d5\u7522\u751f\u5927\u91cf\u7684\u6a19\u8a18 \u7684\u5de5\u5177\uff0c\u5c07\u4e00\u7bc7\u7bc7\u8ad6\u6587\u5206\u5272\u6210\u53e5\u5b50\uff0c\u518d\u5c07\u53e5\u5b50\u5206\u5272\u6210\u8a5e\u5f59\u8207\u6a19\u9ede(tokens)\u3002\u6709\u4e86\u53e5\u5b50 \u8207\u8a5e\u5f59\u5f8c\uff0c\u6211\u5011\u63a5\u8457\u4f7f\u7528 Genia Tagger 3 \u6a19\u8a3b\u8a5e\u6027\u8207\u57fa\u5e95\u7247\u8a9e (base phrase \u6216 chunks) \u3002 \u4e4b\u5f8c\uff0c\u7576\u6240\u6709\u7684\u7dd2\u8ad6\u55ae\u5b57\u90fd\u88ab\u65b7\u8a5e\u548c\u6a19\u8a18\u8a5e\u6027\u4ee5\u53ca\u5340\u584a\u5f8c\uff0c\u6211\u5011\u5229\u7528\u7d71\u8a08\u65b9\u6cd5\u7372\u5f97\u82e5\u5e72 mothed in paper , we describe focus of paper be in study rest of paper structure as in CD we discuss work finally , CD present CD describe result \u5f88\u96e3\u7528\u6709\u9650\u7684\u8cc7\u6599\u4f86\u638c\u63e1\uff0c\u76f8\u53cd\u5730\u5b57\u8a5e\u4e5f\u6709\u4e0d\u5c0f\u7684\u8a5e\u5f59\u8a9e\u610f\u6b67\u7fa9\u3002 in paper , we present in paper , we consider in paper we present motivation for in CD , we describe model in remainder of paper , result show in CD CD present method \u4e14\u6a19\u8a3b\u7684\u6b63\u78ba\u6027\u4e5f\u4e0d\u662f\u975e\u5e38\u7406\u60f3\u3002\u53e6\u5916\uff0c\u8868\u9054\u540c\u4e00\u985e\u7684\u6587\u6b65\uff0c\u7528\u5b57\u9063\u8a5e\u7684\u5dee\u7570\u6027\u5f88\u5927\uff0c approach purpose of paper be to result show that approach as follow paper structure as follow : in what follow , CD describe model goal be \u500b\u5225\u53e5\u5b50\u7684\u5206\u985e\u6b63\u78ba\u7387\u4e26\u4e0d\u9ad8\uff0c\u9019\u53ef\u80fd\u6b78\u548e\u65bc\u5e7e\u500b\u539f\u56e0\u3002\u9996\u5148\uff0c\u6a19\u8a3b\u8cc7\u6599\u592a\u5c11\uff0c\u800c in paper , we present in paper , we use to address problem , remainder of paper organize in CD we describe how in CD we discuss we then present hypothesis be that \u6211\u5011\u9010\u7bc7\u8655\u7406\u9019\u4e00\u842c\u7bc7\u8ad6\u6587\u7c21\u4ecb\u3002\u6211\u5011\u5229\u7528 Python/NLTK 2 \u7684\u5206\u5272\u82f1\u6587\u53e5\u5b50\u3001\u8a5e\u5f59 \u4f86\u52a0\u4ee5\u638c\u63e1\uff0c\u672a\u4f86\u53ef\u80fd\u9084\u9700\u8981\u767c\u6398\u6bd4\u8f03\u6709\u6548\u7684\u7279\u5fb5\u503c\u3002 in paper , we show that in paper , we introduce result show that method we argue that in section , we review work we discuss result in CD in CD we present in section that \u5c0d\u61c9\u53e5\u5b50\u7684\u7bc4\u4f8b \u8868 \u8868 \u8868 \u8868 3. \u8f38\u5165\u53e5 \u8f38\u5165\u53e5 \u8f38\u5165\u53e5 \u8f38\u5165\u53e5\" \" \" \"In this paper , we will describe a method \u2026\"\u7684 \u7684 \u7684 \u7684 N \u9023\u8a5e\u7279\u5fb5\u503c \u9023\u8a5e\u7279\u5fb5\u503c \u9023\u8a5e\u7279\u5fb5\u503c 4.1 \u5be6\u9a57\u8a2d\u5b9a \u5be6\u9a57\u8a2d\u5b9a \u5be6\u9a57\u8a2d\u5b9a \u5be6\u9a57\u8a2d\u5b9a \u4f30\u5404\u7a2e\u5beb\u4f5c\u60c5\u5883\u4e0b\uff0c\u4f7f\u7528\u8005\u89ba\u5f97 WriteAhead \u7684\u63d0\u793a\uff0c\u662f\u5426\u6709\u52a9\u65bc\u5beb\u4f5c\u51fa\u66f4\u597d\u7684 \u300c\u7c21\u4ecb\u300d \u3002 \u8349\u7a3f\uff0c\u800c\u7cfb\u7d71\u53c3\u8003\u9019\u4e9b\u6bcd\u8a9e\u8349\u7a3f\uff0c\u4f86\u8abf\u6574\u63d0\u793a\u7684\u82f1\u6587\u53e5\u578b\u8207\u7247\u8a9e\u3002\u53e6\u5916\uff0c\u6211\u5011\u4e5f\u53ef\u4ee5\u8b93 \u7d0d\uff0c\u6211\u5011\u53ef\u4ee5\u5f97\u5230\u4e0b\u9762\u53f3\u908a\u7684\u6a23\u677f\u53ca\u5176\u8b8a\u5316\u578b\uff1a that first of all , this be important however , approach system in paper , i in work , follow in section , we describe structure of paper in section \u9023\u8a5e\u7279\u5fb5\u503c N-gram Features Surface unigram in this paper we will describe a method Surface bigram \u6211 \u5011 \u5f9e \u5bc6 \u897f \u6839 \u5927 \u5b78 \u7684 \u8a08 \u7b97 \u8a9e \u8a00 \u5b78 \u53ca \u8cc7 \u8a0a \u6aa2 \u7d22 \u7d44 ( Computational Linguistics And Information Retrieval Group, CLAIR) \u8a2d \u8a08 \u7dad \u8b77 \u7684 \u8a08 \u7b97 \u8a9e \u8a00 \u5b78 \u6703 ( Association for \u7136\u800c\uff0c\u4e00\u822c\u800c\u8a00\uff0c\u51e1\u662f\u6d89\u53ca\u4f7f\u7528\u8005\u7684\u8a55\u4f30\u90fd\u662f\u975e\u5e38\u56f0\u96e3\u3002\u9000\u800c\u6c42\u5176\u6b21\uff0c\u6211\u5011\u76ee\u524d\u50c5\u91dd\u5c0d \u6587\u6b65\u5206\u985e\u5668\u90e8\u5206\uff0c\u8a55\u4f30\u5176\u5206\u985e\u6b63\u78ba\u6027\u3002\u7531\u65bc\u8ad6\u6587\u7684\u6587\u6b65\u662f\u4f9d\u5e8f\u63a8\u79fb\uff0c\u6240\u4ee5\u6211\u5011\u91dd\u5c0d\u300c\u7c21 NE ( CD ) propose model however , for language much of work unfortunately , in CD , we describe method CD show example of CD conclude paper CD describe \u4f7f\u7528\u8005\u9078\u53d6\u90e8\u5206\u6c92\u6709\u628a\u63e1\u7684 2-5 \u500b\u5b57\uff0c\u7cfb\u7d71\u63d0\u793a\u6b63\u78ba\u6216\u932f\u8aa4\u7684\u6a5f\u7387\uff0c\u4ee5\u53ca\u5176\u4ed6\u53ef\u4ee5\u66ff\u63db \u7684\u8868\u9054\u65b9\u5f0f\u3002 recently , al ( CD ) it be , however , there be , however , approach , however , research support by NE over decade , however , if difficulty be problem with B.3\u300c \u300c \u300c \u300c\u8a0e\u8ad6 \u8a0e\u8ad6 \u8a0e\u8ad6 \u8a0e\u8ad6\u300d \u300d \u300d \u300d\u6587\u6b65 \u6587\u6b65 \u6587\u6b65 paper organize as follow : in finally , CD conclude CD report result CD introduce \u6587\u6b65 finally , we conclude in CD as we see , CD describe algorithm CD conclude \u4ecb\u300d \u7684\u6574\u500b\u7bc0\uff0c\u4f86\u8a55\u4f30\u6587\u6b65\u7684\u6a19\u8a3b\u662f\u5426\u6b63\u78ba\u3002 \u7e3d\u800c\u8a00\u4e4b\uff0c\u6211\u5011\u4ecb\u7d39\u4e86\u4e00\u5957\u65b9\u6cd5\uff0c\u80fd\u8655\u7406\u6240\u641c\u96c6\u5230\u7684\u5b78\u8853\u8ad6\u6587\uff0c\u5c07\u6bcf\u4e00\u500b\u53e5\u5b50\u6a19\u793a currently , there be to knowledge , there be however , there be however , unlike challenge be it be important to note to overcome problem , for reason , in short , in CD , we describe corpus CD give overview of CD present algorithm CD show in_this this_paper paper_, ,_we we_will will_describe describe_a a_method Lemma unigram in this paper we will describe a method Lemma bigram in_this this_paper paper_, ,_we we_will will_describe describe_a a_method Chunk head unigram Chunk head bigram in_paper paper_, ,_we we_describe describe_method \u8868 \u8868 \u8868 \u8868 4. \u5206\u985e\u8a5e\u985e\u96c6\u7bc4\u4f8b \u5206\u985e\u8a5e\u985e\u96c6\u7bc4\u4f8b \u5206\u985e\u8a5e\u985e\u96c6\u7bc4\u4f8b \u5206\u985e\u8a5e\u985e\u96c6\u7bc4\u4f8b \u8a5e\u985e\u540d\u7a31 \u8a5e\u6027 \u8a5e\u5f59 AFFECT v afford, believe, decide, feel, hope, imagine, regard, trust, think COMPARISON v compare, compete, evaluate, test TEXT n paragraph, section, subsection, chapter \u90e8\u5206\u4f86\u505a\u70ba\u7814\u7a76\u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u4ee5\u53ca\u7cfb\u7d71\u958b\u767c\u7684\u8cc7\u6599\u3002 \u8868 \u8868 \u8868 \u8868 5. \u6709\u5339\u914d\u53e5\u578b\u4e4b\u53e5\u5b50\u6587\u6b65\u5206\u5e03\u60c5\u5f62 \u6709\u5339\u914d\u53e5\u578b\u4e4b\u53e5\u5b50\u6587\u6b65\u5206\u5e03\u60c5\u5f62 \u6709\u5339\u914d\u53e5\u578b\u4e4b\u53e5\u5b50\u6587\u6b65\u5206\u5e03\u60c5\u5f62 \u6709\u5339\u914d\u53e5\u578b\u4e4b\u53e5\u5b50\u6587\u6b65\u5206\u5e03\u60c5\u5f62 \u6587\u6b65 \u53e5\u6578 BKG 3,333 OWN DIS TEX 5,687 \u7e3d\u8a08 17,791 \u6587\u6b65\u7684\u7cbe\u78ba\u7387\u50c5\u50c5\u7565\u9ad8\u65bc 50%\uff0c\u9019\u7576\u7136\u662f\u56e0\u70ba\u8868\u9054\u7684\u65b9\u5f0f\u6bd4\u8f03\u5206\u6b67\uff0c\u4e0d\u6613\u900f\u904e\u5e38\u898b\u53e5\u578b in work , we focus on in paper , we report on aim of paper be to in paper , we explore result show that model in work , we in paper , B.4\u300c \u300c \u300c \u300c\u7d44\u7e54 \u7d44\u7e54 \u7d44\u7e54 \u7d44\u7e54\u300d \u300d \u300d \u300d\u6587\u6b65 \u6587\u6b65 \u6587\u6b65 \u6587\u6b65 aim be to \u6587\u8108\u6587\u6b65\u5169\u8005\u90fd\u6709\u6bd4\u8f03\u56fa\u5b9a\u7684\u8868\u9054\u65b9\u5f0f\u3002\u76f8\u5c0d\u7684\uff0c\u672c\u8ad6\u6587(OWN)\u3001\u8a0e\u8ad6( DIS)\u5169\u7a2e approach goal of research be to paper address problem of we hypothesize that \u666f\u6587\u6b65 ( BKG)\u7684\u6b63\u78ba\u7387\u9054 86% \u800c\u6587\u8108\u6587\u6b65(TEX)\u9054 76%\uff0c\u9019\u53ef\u80fd\u662f\u56e0\u70ba\u80cc\u666f\u3001 in paper , we propose focus of paper be on in study , we we start with contribution of paper be : in comparison , difference be that 1,572 it be worth note that by contrast , example show that \u793a\u8a55\u4f30\u7684\u7d50\u679c\u3002\u6574\u9ad4\u7684\u6587\u6b65\u9810\u6e2c\u6b63\u78ba\u7387 67%\uff0c\u9084\u6709\u6539\u5584\u7684\u7a7a\u9593\u3002\u5c31\u500b\u5225\u7684\u6587\u6b65\u4f86\u770b\uff0c\u80cc 7,199 DIS 312 461 241 .52 TEX 117 98 75 .76 \u7e3d\u8a08 1,288 1,288 862 .67 Network \u4e2d\u96a8\u6a5f\u6311\u9078\u4e94\u5341\u7bc7\u8ad6\u6587\u7c21\u4ecb\u7684\u53e5\u5b50\uff0c\u505a\u70ba\u6211\u5011\u6587\u6b65\u6a19\u8a3b\u6a21\u7d44\u7684\u8a55\u4f30\u8cc7\u6599\u3002\u8868 6 \u986f \u53c3\u8003\u6587\u737b \u53c3\u8003\u6587\u737b \u53c3\u8003\u6587\u737b B.2 \u300c \u300c \u300c \u300c\u672c\u8ad6\u6587 \u672c\u8ad6\u6587 \u672c\u8ad6\u6587 \u672c\u8ad6\u6587\u300d \u300d \u300d \u300d\u6587\u6b65 \u6587\u6b65 \u6587\u6b65 \u6587\u6b65 reason for this be that contribution be : on contrary , although approac \u53c3\u8003\u6587\u737b as it turn out , as result of in principle , we believe \u70ba\u4e86 \u9054\u6210 \u80fd\u81ea \u52d5 \u7684\u70ba \u8ad6 \u6587 \u7c21 \u4ecb\u53e5 \u5b50\u6a19 \u8a3b \u6587 \u6b65 \u6b64\u4e00 \u76ee \u6a19 \uff0c \u6211\u5011 \u5f9e ACL Anthology \u81f4\u8b1d\u8a5e \u81f4\u8b1d\u8a5e \u81f4\u8b1d\u8a5e \u81f4\u8b1d\u8a5e \u672c\u7814\u7a76\u627f\u8499\u79d1\u6280\u90e8\u88dc\u52a9\u7814\u7a76\u7d93\u8cbb\uff0c\u8a08\u756b\u6a19\u865f NSC 100-2511-S-007 -005 -MY3\u3002 to date , as matter of fact , in general , it recognize that sponsor first , it we believe that observation be recently , NE ( CD ) propose at present , this be task view express endorse by in contrast , system this mean that advantage of traditionally , recognition ( NE ) be however , they to knowledge , in order to do this we also show how analysis show that contribution be currently , in practice , however , recently , model most of system recently contribution of work be : to knowledge , work intuition be that this suggest that in decade , collection comprise CD challenge be that to date , recently , that reason for this be that be why we note that in paper we describe method )\u6240 \u5f97\u5230\u7684\u6587\u5b57\u6a94\u6848\u3002\u56e0\u6b64\uff0c\u9019\u4e9b\u6a94\u6848\u6709\u8457\u5404\u5f0f\u7684\u96dc\u8a0a\uff0c\u50cf\u662f\u6b98\u7559\u7684\u63db\u884c\u9023\u5b57\u7b26\u865f\u3001\u55ae\u5b57\u8fa8 \u8b58\u932f\u8aa4\u7b49\u3002 \u6211\u5011\u900f\u904e\u8a2d\u8a08\u53ca\u5206\u6790\u898f\u5247\uff0c\u8a2d\u5b9a\u7c21\u55ae\u7684\u689d\u4ef6\uff0c\u8fa8\u8b58\u51fa\u7bc0\u7684\u6a19\u984c\uff0c \u4e26\u6311\u9078\u4e86 \u6a19\u793a\u5f88\u6e05\u695a\u7684\u8ad6\u6587\u5c07\u8fd1\u4e00\u842c\u7bc7\u3002\u4e4b\u5f8c\uff0c\u6211\u5011\u6839\u64da\u6a19\u984c\u7684\u7de8\u865f\uff0c\u6a19\u984c\u7684\u5167\u5bb9\uff0c\u62bd\u53d6\u300c\u7c21\u4ecb\u300d \u8868 \u8868 \u8868 \u8868 6. \u7e3d\u5171 \u7e3d\u5171 \u7e3d\u5171 \u7e3d\u5171 50 \u7bc7\u7c21\u4ecb\u4e4b \u7bc7\u7c21\u4ecb\u4e4b \u7bc7\u7c21\u4ecb\u4e4b \u7bc7\u7c21\u4ecb\u4e4b\u53e5\u5b50\u6a19\u793a\u6587\u6b65\u8207\u9810\u6e2c\u6587\u6b65\u8207\u9810\u6e2c\u6b63\u78ba\u7387 \u53e5\u5b50\u6a19\u793a\u6587\u6b65\u8207\u9810\u6e2c\u6587\u6b65\u8207\u9810\u6e2c\u6b63\u78ba\u7387 \u53e5\u5b50\u6a19\u793a\u6587\u6b65\u8207\u9810\u6e2c\u6587\u6b65\u8207\u9810\u6e2c\u6b63\u78ba\u7387 \u53e5\u5b50\u6a19\u793a\u6587\u6b65\u8207\u9810\u6e2c\u6587\u6b65\u8207\u9810\u6e2c\u6b63\u78ba\u7387 \u6587\u6b65 \u6a19\u793a\u53e5\u6578 \u9810\u6e2c\u53e5\u6578 \u6b63\u78ba\u53e5\u6578 \u7cbe\u78ba\u7387 BKG 621 470 402 .86 OWN 238 259 144 \u793a\u5b78\u7fd2\u8005\uff0c\u5982\u4f55\u5beb\u4f5c\u5404\u7a2e\u6587\u6b65\u7684\u53e5\u5b50\u3002 there be work to knowledge , this be most of method however , when unlike method advantage of approach be contribution of paper be as consequence , this be because finally , in CD , result discuss in CD CD describe how .56 at present , to good of knowledge , however , while recently , method they describe that for example , name in practice , we then discuss in CD , we review work result report in CD CD present experiment CD detail \u4e0a\u9069\u7576\u7684\u6587\u6b65(move)\uff0c\u4e26\u7d71\u8a08\u5404\u985e\u6587\u6b65\u7684\u5e38\u898b\u7247\u8a9e\uff0c\u85c9\u4ee5\u5e6b\u52a9\u82f1\u6587\u975e\u5176\u6bcd\u8a9e\u5b78\u751f\uff0c\u5beb over year , in case , however , study show that in year , currently , this be due to fact that in particular , it reason be that unlike NE , organization of paper be as result present in CD CD introduce model CD explain \u4f5c\u5b78\u8853\u8ad6\u6587\u3002 \u6211\u5011\u7684\u65b9\u6cd5\u6d89\u53ca\u64f7\u53d6\u5e38\u898b\u5beb\u4f5c\u53e5\u578b\u3001\u6a19\u793a\u53e5\u578b\u7684\u6587\u6b65\u3001\u7522\u751f\u5927\u91cf\u5df2\u6a19\u793a\u6587 over decade , NE ( CD ) present difficulty be that it observe that traditionally , contribution of paper be in contrast , model specifically , it it note that follow as we show , CD introduce method CD present \u6b65\u7684\u53e5\u5b50\u4ee5\u53ca\u7279\u5fb5\u503c\uff0c\u4f5c\u70ba\u8a13\u7df4\u8cc7\u6599\u4f86\u958b\u767c\u6587\u6b65\u5206\u985e\u5668\u3002\u6211\u5011\u85c9\u7531\u6b64\u4e00\u5206\u985e\u5668\uff0c\u9810\u6e2c\u53e5 in year , however , in case , there be also there be work while approach however , we believe that it turn out that this lead to in sum , conclusion we conclude in CD CD show example \u5b50\u7684\u6587\u6b65\u3002\u6211\u5011\u63d0\u51fa\u4e00\u500b\u96db\u578b\u7cfb\u7d71 WriteAhead\uff0c\u61c9\u7528\u5206\u985e\u7684\u53e5\u5b50\u8207\u5e38\u898b\u7247\u8a9e\u7684\u8cc7\u6599\uff0c\u63d0 recently , method NE ( CD ) describe currently , system they show that in year as follow it be obvious that this be problem among them , finally , CD present paper proceed as follow CD present model CD discuss</td></tr><tr><td colspan=\"2\">\u7406\u60f3\uff0c\u9700\u8981\u53e6\u5916\u8490\u96c6\u8cc7\u6599\uff0c\u4f9d\u7167\u5b78\u79d1\u5efa\u7f6e\u4e0d\u540c\u7684\u7cfb\u7d71\u3002 in paper , we propose in paper we describe idea be to</td><td>we evaluate</td></tr></table>",
"type_str": "table"
}
}
}
} |