Knowledge (XXG)

STUDENT

Source 📝

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for pattern transformation. At first, sentences in English are converted into kernel sentences, which each contain a single piece of information. Next, the kernel sentences are converted into mathematical expressions. The knowledge base that supports the transformation contains 52 facts.
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as his PhD thesis in 1964 (Bobrow 1964). It was designed to read and solve the kind of word problems found in high school algebra books. The program is often cited as an early accomplishment of AI in
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If the number of customers Tom gets is twice the square of 20% of the number of advertisements he runs, and the number of advertisements is 45, then what is the number of customers Tom gets?
273:. International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015-Special Issue on Selected Papers from ROCLING XXVII. 269:
Lin, Yi-Chung and Liang, Chao-Chun and Hsu, Kuang-Yi and Huang, Chien-Tsung and Miao, Shen-Yun and Ma, Wei-Yun and Ku, Lun-Wei and Liau, Churn-Jung and Su, Keh-Yih (2015).
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Dongxiang Zhang and Lei Wang and Luming Zhang and Bing Tian Dai and Heng Tao Shen (2019). "The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers".
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STUDENT accepts an algebra story written in the English language as input, and generates a number as output. This is realized with a layered pipeline that consists of
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were realized with 1960s hardware and software as well: for example, the Philips, Baseball and Synthex systems.
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In the 1960s, mainframe computers were only available within a research context at the university. Within
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with logic inference. The rules are pre-programmed by the software developer and are able to
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Designing a tag-based statistical math word problem solver with reasoning and explanation
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Shuming Shi and Yuehui Wang and Chin-Yew Lin and Xiaojiang Liu and Yong Rui (2015).
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This article is about the artificial intelligence program. For other uses, see
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Automatically Solving Number Word Problems by Semantic Parsing and Reasoning
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Paradigms of artificial intelligence programming:case studies in Common Lisp
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More powerful techniques for natural language processing, such as
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software, which uniquely involved natural language processing and
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Natural Language Input for a Computer Problem Solving System
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Natural language input for a computer problem solving system
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program that solves algebra word problems. It is written in
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SIR: A computer program for semantic information retrieval
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AI: The Tumultuous Search for Artificial Intelligence
114: 69:, the STUDENT system was an early example of a 448: 192:(PhD). Massachusetts Institute of Technology. 177:(PhD). Massachusetts Institute of Technology. 8: 283:: CS1 maint: multiple names: authors list ( 455: 441: 366:Artificial Intelligence: A Modern Approach 311: 245: 212: 135: 133: 129: 276: 152:: Morgan Kaufmann. pp. 109–149. 7: 484:Lisp (programming language) software 409: 407: 77:. Other early attempts for solving 479:History of artificial intelligence 427:. You can help Knowledge (XXG) by 25: 411: 247:10.1111/j.1756-8765.2010.01083.x 1: 391:. New York, NY: BasicBooks. 234:Topics in Cognitive Science 51:natural language processing 505: 489:Computer programming stubs 406: 322:10.1109/tpami.2019.2914054 228:Kenneth D. Forbus (2010). 26: 18:STUDENT (computer program) 188:Raphael, Bertram (1964). 173:Bobrow, Daniel G (1964). 120:(extracted from Norvig) 29:Student (disambiguation) 39:artificial intelligence 423:-related article is a 352:, Bobrow's PhD thesis. 140:Norvig, Peter (1992). 118: 79:algebra story problems 240:(3). Wiley: 345–356. 57:Technical description 421:computer-programming 214:10.18653/v1/d15-1135 75:symbolic programming 357:Russell, Stuart J. 100:natural language. 71:question answering 436: 435: 94:rule-based system 16:(Redirected from 496: 457: 450: 443: 415: 408: 402: 379: 342: 341: 315: 295: 289: 288: 282: 274: 266: 260: 259: 249: 225: 219: 218: 216: 200: 194: 193: 185: 179: 178: 170: 164: 163: 137: 105:machine learning 47:Daniel G. Bobrow 21: 504: 503: 499: 498: 497: 495: 494: 493: 464: 463: 462: 461: 399: 385:Crevier, Daniel 383: 377: 355: 346: 345: 297: 296: 292: 279:cite conference 275: 268: 267: 263: 227: 226: 222: 202: 201: 197: 187: 186: 182: 172: 171: 167: 160: 139: 138: 131: 126: 113: 92:STUDENT uses a 59: 32: 23: 22: 15: 12: 11: 5: 502: 500: 492: 491: 486: 481: 476: 466: 465: 460: 459: 452: 445: 437: 434: 433: 416: 405: 404: 397: 381: 375: 353: 344: 343: 290: 261: 220: 195: 180: 165: 158: 128: 127: 125: 122: 112: 109: 58: 55: 24: 14: 13: 10: 9: 6: 4: 3: 2: 501: 490: 487: 485: 482: 480: 477: 475: 474:1964 software 472: 471: 469: 458: 453: 451: 446: 444: 439: 438: 432: 430: 426: 422: 417: 414: 410: 400: 398:0-465-02997-3 394: 390: 386: 382: 378: 376:0-13-790395-2 372: 368: 367: 362: 361:Norvig, Peter 358: 354: 351: 348: 347: 339: 335: 331: 327: 323: 319: 314: 309: 305: 301: 294: 291: 286: 280: 272: 265: 262: 257: 253: 248: 243: 239: 235: 231: 224: 221: 215: 210: 206: 199: 196: 191: 184: 181: 176: 169: 166: 161: 159:1-55860-191-0 155: 151: 147: 146:San Francisco 143: 136: 134: 130: 123: 121: 117: 110: 108: 106: 101: 99: 95: 90: 87: 82: 80: 76: 72: 68: 64: 56: 54: 52: 48: 44: 40: 36: 30: 19: 429:expanding it 418: 388: 365: 303: 299: 293: 270: 264: 237: 233: 223: 204: 198: 189: 183: 174: 168: 141: 119: 115: 102: 91: 83: 60: 37:is an early 34: 33: 403:, pp. 76–79 63:Project MAC 468:Categories 313:1808.07290 150:California 124:References 86:heuristics 387:(1993). 363:(2003), 338:52066980 330:31056490 256:25163864 380:, p. 19 111:Example 35:STUDENT 395:  373:  336:  328:  254:  156:  419:This 334:S2CID 308:arXiv 98:parse 425:stub 393:ISBN 371:ISBN 326:PMID 285:link 252:PMID 154:ISBN 43:Lisp 318:doi 242:doi 209:doi 67:MIT 65:at 45:by 470:: 359:; 332:. 324:. 316:. 304:42 302:. 281:}} 277:{{ 250:. 236:. 232:. 148:, 144:. 132:^ 53:. 456:e 449:t 442:v 431:. 401:. 340:. 320:: 310:: 287:) 258:. 244:: 238:2 217:. 211:: 162:. 31:. 20:)

Index

STUDENT (computer program)
Student (disambiguation)
artificial intelligence
Lisp
Daniel G. Bobrow
natural language processing
Project MAC
MIT
question answering
symbolic programming
algebra story problems
heuristics
rule-based system
parse
machine learning


San Francisco
California
ISBN
1-55860-191-0
doi
10.18653/v1/d15-1135
"AI and Cognitive Science: The Past and Next 30 Years"
doi
10.1111/j.1756-8765.2010.01083.x
PMID
25163864
cite conference
link

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