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Better models with Saussure: Simulating lexical evolution with semantic shifts

Better models with Saussure: Simulating lexical evolution with semantic shifts

Talk by Gereon Kaiping and Johann-Mattis List, presented at the conference "Phylogenetic methods in historical linguistics" (2017/03/27-30, Eberhard-Karls-Universität, Tübingen)

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Johann-Mattis List

March 30, 2017
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Transcript

  1. Better Models With Saussure Simulating Lexical Evolution with Semantic Shifts

    Gereon Kaiping1 Mattis List2 1Leiden University Centre for Linguistics 2MPI for the Science of Human History, Jena 2017-03-30
  2. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References 1 Context and Motivation 2 Our Model 3 What we can and can’t do 4 Closing Remarks Gereon Kaiping, Mattis List Better Models With Saussure
  3. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Context Phylogenetic reconstruction has been enjoying a great popularity of late. Language trees are not only used for genetic subgrouping of language families, but also to address general linguistic questions (typological universals, ...) general anthropological/historical questions (Urheimat, ....) Phylogenetic reconstruction was the driving force for the recent quantitative turn in historical linguistics, and has has been accepted by most scholars in the field Gereon Kaiping, Mattis List Better Models With Saussure
  4. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Context Phylogenetic reconstruction has been enjoying a great popularity of late. Language trees are not only used for genetic subgrouping of language families, but also to address general linguistic questions (typological universals, ...) general anthropological/historical questions (Urheimat, ....) Phylogenetic reconstruction was the driving force for the recent quantitative turn in historical linguistics, and has has been accepted by most scholars in the field Gereon Kaiping, Mattis List Better Models With Saussure
  5. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Context The Reconstruction Dilemma historical linguistics deals with past states and events, investigating research objects and processes which are not directly observable → even falsification is tricky scholars tend to argue in terms of the likelihood of scenarios, but we cannot compare our inferences against inferences in controlled experiments Gereon Kaiping, Mattis List Better Models With Saussure
  6. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Context despite their popularity, methods for phylogenetic reconstruction are rarely tested, neither against gold standards (which do not really exist, the closest we have are phylogenies in databases like Glottolog [3]), nor against the results of simulation studies in the rare cases where phylogenetic methods have been tested with help of simulation studies [5, 2, 7, 6, 1], they were based on very simplistic models of lexical change that assume independent gain/loss of words or replacement of items Gereon Kaiping, Mattis List Better Models With Saussure
  7. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References The usual assumptions t Gereon Kaiping, Mattis List Better Models With Saussure
  8. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References The usual assumptions loss gain t Gereon Kaiping, Mattis List Better Models With Saussure
  9. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References The usual assumptions replacement meaning t Gereon Kaiping, Mattis List Better Models With Saussure
  10. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Lexicostatistical Word List Data Concept ID German ID English ID Italian ID French HAND 1 Hand 1 hand 2 mano 2 main BLOOD 3 Blut 3 blood 4 sangue 4 sang HEAD 5 Kopf 6 head 7 testa 7 tête TOOTH 8 Zahn 8 tooth 8 dente 8 dent TO SLEEP 9 schlafen 9 sleep 10 dormir 10 dormir TO SAY 11 sagen 11 say 12 dire 12 dire … … … … … Gereon Kaiping, Mattis List Better Models With Saussure
  11. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Lexicostatistical Word List Data Concept ID German ID English ID Italian ID French HAND 1 Hand 1 hand 2 mano 2 main BLOOD 3 Blut 3 blood 4 sangue 4 sang HEAD 5 Kopf 6 head 7 testa 7 tête TOOTH 8 Zahn 8 tooth 8 dente 8 dent TO SLEEP 9 schlafen 9 sleep 10 dormir 10 dormir TO SAY 11 sagen 11 say 12 dire 12 dire … … … … … Gereon Kaiping, Mattis List Better Models With Saussure
  12. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Gain-Loss Coding Concept Proto-Form German English Italian French HAND PGM *xanda- HAND LAT mānus BLOOD PGM *blođa- BLOOD LAT sanguis HEAD PGM *kuppa- HEAD PGM *xawbda- HEAD LAT tēsta TOOTH PIE *h3dont- TO SLEEP PGM slēpan- TO SLEEP LAT dormīre TO SAY PGM *sagjan- TO SAY LAT dīcere … … … … … … Gereon Kaiping, Mattis List Better Models With Saussure
  13. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Gain-Loss Coding Proto-Form German English Italian French PGM *xanda- LAT mānus PGM *blođa- LAT sanguis PGM *kuppa- PGM *xawbda- LAT tēsta PIE *h3dont- PGM slēpan- LAT dormīre PGM *sagjan- LAT dīcere … … … … … Gereon Kaiping, Mattis List Better Models With Saussure
  14. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Motivation gain/loss and replacement approaches are not satisfying linguistically phylogenetic reconstruction is important but insufficiently tested gold standard (controlled) datasets are not available we barely understand the processes underlying lexical change → by working on more realistic simulations, we can learn a lot about the processes of lexical change and also help to evaluate the accuracy of phylogenetic approaches Gereon Kaiping, Mattis List Better Models With Saussure
  15. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Saussure’s model of the linguistic sign arbre form "meaning" Gereon Kaiping, Mattis List Better Models With Saussure
  16. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Saussure’s model of the linguistic sign arbre Gereon Kaiping, Mattis List Better Models With Saussure
  17. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Saussure’s model of the linguistic sign arbre arbre Gereon Kaiping, Mattis List Better Models With Saussure
  18. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Saussure’s model of the linguistic sign arbre arbre Gereon Kaiping, Mattis List Better Models With Saussure
  19. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Saussure’s model of the linguistic sign: Dynamics arbre bois forêt Gereon Kaiping, Mattis List Better Models With Saussure
  20. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Saussure’s model of the linguistic sign: Dynamics arbre bois forêt Gereon Kaiping, Mattis List Better Models With Saussure
  21. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Saussure’s model of the linguistic sign: Dynamics arbre bois forêt Gereon Kaiping, Mattis List Better Models With Saussure
  22. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Preliminaries A A' Gereon Kaiping, Mattis List Better Models With Saussure
  23. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Preliminaries A A' Gereon Kaiping, Mattis List Better Models With Saussure
  24. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Preliminaries B B' Gereon Kaiping, Mattis List Better Models With Saussure
  25. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Preliminaries B B' Gereon Kaiping, Mattis List Better Models With Saussure
  26. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Semantic Network 1 post, pole staff, walking stick doorpost, jamb tree stump mast club firewood root tree trunk woods, forest banana tree tree wood 1http://clics.lingpy.org/browse.php?gloss=wood Gereon Kaiping, Mattis List Better Models With Saussure
  27. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Semantic Network CLICS [4] database of synchronic lexical associations (“colexifications”), currently 221 language varieties 1280 concepts uses network approaches to partition the data into semantic fields web-application at http://clics.lingpy.org allows for quick browsing of the semantic networks Gereon Kaiping, Mattis List Better Models With Saussure
  28. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Semantic Network 684 678 871 1043 6 30 129 196 1243 128 869 853 650 344 1103 150 185 627 232 709 1035 1206 177 97 311 496 606 137 207 444 840 1077 325 222 1063 1138 1204 1258 559 723 495 766 914 38 1101 652 865 891 872 633 291 980 700 144 410 430 1025 406 464 787 622 131 242 918 275 1159 99 1174 671 1038 786 705 641 760 1259 356 391 197 10 214 299 63 191 619 644 792 1205 897 67 1231 213 226 747 681 399 841 439 773 123 800 16 1067 1227 696 417 550 68 76 108 360 1244 339 500 81 867 79 1097 98 96 833 771 715 455 380 1268 1186 1046 39 252 1228 66 23 1112 133 676 336 739 1150 1071 986 485 112 372 1109 830 721 1053 1057 601 573 556 527 1248 614 488 908 499 1002 309 442 814 1193 569 458 258 563 653 682 774 70 1151 948 801 1082 243 47 71 83 153 1265 934 85 1215 1199 523 581 422 21 358 1261 111 354 219 759 15 890 261 1222 141 158 74 806 1031 845 770 850 903 1224 419 754 433 798 188 1256 613 528 208 539 323 981 132 1055 1001 790 804 844 1118 907 640 446 815 923 498 201 1184 578 566 427 532 452 151 750 598 1094 345 735 777 978 599 492 390 286 1107 742 1015 1202 1210 1257 1275 859 988 69 752 596 290 126 110 950 922 1047 741 253 347 385 620 966 221 431 3 224 1194 999 953 1029 852 301 389 318 530 1048 1032 175 701 544 1119 241 94 745 835 1270 62 107 159 20 767 512 331 248 549 1013 946 974 1022 1100 477 302 233 1168 1003 1211 570 307 40 945 1269 784 546 437 901 350 238 305 1191 482 1012 977 906 783 524 117 457 603 836 1181 880 229 124 216 1113 1074 72 586 647 447 2 113 1179 7 1006 665 397 502 610 1274 707 327 659 667 824 917 985 1089 346 1229 101 542 1042 727 782 733 967 462 592 468 1106 440 478 308 577 698 776 75 1155 51 145 517 359 938 1157 1160 1183 947 1102 1135 1252 343 608 537 103 634 251 383 506 25 829 396 686 679 574 516 42 250 379 809 602 660 780 765 697 856 899 594 1008 393 179 114 1140 11 100 1209 618 600 192 1277 896 1142 1278 762 421 713 182 521 861 672 297 1116 1190 1192 140 1212 46 493 1187 157 1225 212 403 519 616 173 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314 1041 1009 799 674 848 1201 1004 689 1085 1218 1145 1170 228 911 279 73 104 690 1254 402 340 169 693 868 893 1018 78 1092 194 555 198 834 1249 997 932 237 1176 666 956 624 1262 541 520 795 866 702 4 734 1095 1180 728 964 1079 271 842 1241 1056 154 751 353 905 1136 504 909 910 1133 362 583 670 1124 381 1216 215 178 571 470 142 376 1154 172 296 533 364 963 152 797 1213 803 1051 738 426 1036 1153 637 823 915 428 1075 560 547 1137 35 882 89 511 1122 805 494 1130 1188 1086 1236 669 588 930 703 942 18 655 335 155 710 1156 1028 465 147 183 414 1221 273 166 1054 278 55 460 812 1090 810 180 768 143 156 404 367 1182 231 288 136 456 82 529 970 1016 729 395 187 604 408 330 1064 34 1267 847 726 543 677 642 940 645 958 683 695 864 1058 605 1084 451 443 699 1167 959 925 1198 227 886 628 1178 337 991 813 657 1185 1039 769 1081 484 712 1189 944 1207 322 33 685 424 80 270 937 1177 283 1237 816 130 161 189 77 300 1026 463 1104 326 589 60 983 474 1093 744 748 554 292 41 267 984 373 1214 957 1024 969 507 37 874 1030 630 579 962 535 706 688 122 497 1060 1083 1027 102 510 405 1134 658 617 936 929 363 1175 361 536 534 1219 181 386 884 418 558 8 479 979 551 505 316 298 26 315 761 202 1144 176 473 348 134 639 663 717 885 924 149 49 1078 1040 57 167 764 1173 673 280 1152 277 1272 1065 272 827 531 607 1123 257 996 436 9 826 234 1096 875 525 304 1108 475 1132 714 846 540 716 1005 1105 357 1162 694 920 743 28 994 1200 168 1266 420 515 568 755 895 218 916 730 807 210 375 854 1010 879 1125 268 1129 1114 1255 1158 1279 487 486 398 597 661 135 565 621 193 321 1230 513 654 265 612 737 855 211 1196 246 1264 584 338 749 1271 434 121 423 509 839 1147 656 230 239 489 14 469 22 1044 351 448 282 329 961 254 989 371 284 223 843 821 24 1023 643 819 285 514 746 757 791 138 186 849 93 951 127 877 1088 518 1164 1260 501 54 190 95 43 205 1276 116 146 662 217 461 883 204 1033 310 472 12 412 332 817 649 794 1037 943 927 481 968 425 109 195 857 1121 564 687 664 724 87 1120 88 449 429 255 987 992 1111 591 575 491 720 851 328 941 990 1019 993 1087 955 580 1226 975 1099 732 235 779 365 1234 441 609 247 334 91 1251 1131 913 691 52 274 1017 435 90 407 480 1239 13 623 0 266 626 295 954 1059 552 898 858 772 526 1115 48 1161 125 590 454 1020 1141 203 740 1146 342 820 1220 56 320 416 27 401 476 19 120 1203 445 789 775 888 567 378 1076 160 162 409 731 631 374 538 837 Gereon Kaiping, Mattis List Better Models With Saussure
  29. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Design Goals A more realistic model of lexical evolution is based on a bipartite graph structure of word forms and word meanings builds on a dynamic representation of reference potentials instead of Saussure’s inseparable dichotomy of the linguistic sign feeds on (ideally, weighted and directed) networks of semantic associations to account for the fact that semantic shift and lexical replacement follow certain preference laws Gereon Kaiping, Mattis List Better Models With Saussure
  30. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Design Goals A more realistic model of lexical evolution is based on a bipartite graph structure of word forms and word meanings builds on a dynamic representation of reference potentials instead of Saussure’s inseparable dichotomy of the linguistic sign feeds on (ideally, weighted and directed) networks of semantic associations to account for the fact that semantic shift and lexical replacement follow certain preference laws Gereon Kaiping, Mattis List Better Models With Saussure
  31. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Design Goals A more realistic model of lexical evolution is based on a bipartite graph structure of word forms and word meanings builds on a dynamic representation of reference potentials instead of Saussure’s inseparable dichotomy of the linguistic sign feeds on (ideally, weighted and directed) networks of semantic associations to account for the fact that semantic shift and lexical replacement follow certain preference laws Gereon Kaiping, Mattis List Better Models With Saussure
  32. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Design Goals A more realistic model of lexical evolution is based on a bipartite graph structure of word forms and word meanings builds on a dynamic representation of reference potentials instead of Saussure’s inseparable dichotomy of the linguistic sign feeds on (ideally, weighted and directed) networks of semantic associations to account for the fact that semantic shift and lexical replacement follow certain preference laws Gereon Kaiping, Mattis List Better Models With Saussure
  33. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Questions of Implementation How should the model drive the change of edge weights in the bipartite graph? How to choose the underlying semantic network? How do we see whether the model has any chance of realism? How can we select realistic parameters for the model? Gereon Kaiping, Mattis List Better Models With Saussure
  34. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Questions of Implementation How should the model drive the change of edge weights in the bipartite graph? How to choose the underlying semantic network? How do we see whether the model has any chance of realism? How can we select realistic parameters for the model? Gereon Kaiping, Mattis List Better Models With Saussure
  35. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Questions of Implementation How should the model drive the change of edge weights in the bipartite graph? How to choose the underlying semantic network? How do we see whether the model has any chance of realism? How can we select realistic parameters for the model? Gereon Kaiping, Mattis List Better Models With Saussure
  36. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Questions of Implementation How should the model drive the change of edge weights in the bipartite graph? How to choose the underlying semantic network? How do we see whether the model has any chance of realism? How can we select realistic parameters for the model? Gereon Kaiping, Mattis List Better Models With Saussure
  37. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Framework A B C D E Gereon Kaiping, Mattis List Better Models With Saussure
  38. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Framework A B C D E intention, purpose woods, forest tree wood post, pole Gereon Kaiping, Mattis List Better Models With Saussure
  39. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Framework A B C D E intention, purpose woods, forest tree wood post, pole [1] [2] [3] 6 1 1 5 2 Gereon Kaiping, Mattis List Better Models With Saussure
  40. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Framework A B C D E intention, purpose woods, forest tree wood post, pole [1] [2] [3] 6 1 1 5 2 Gereon Kaiping, Mattis List Better Models With Saussure
  41. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Evolution Inspiration: Discrimination Game and Guessing Game Choose two random concepts ci (P ∝ deg2) Score each word w for each ci: wi = wt(w; ci) + 0.1 c neighbor of ci wt(w; c) Increase wt(w, ci) where w¬i = 0 and wi max; Or create a new word meaning ci with wt 1. Decrease wt(w, ci) where 0 < w¬i < wi max; Or a random connection (∝ wt) Decrease wt of a random connection (∝ wt) intention, purpose woods, forest tree wood post, pole [1] [2] [3] 6 1 1 5 2 ↓ intention, purpose woods, forest tree wood post, pole [1] [2] [3] 7 0 1 5 2 1 Gereon Kaiping, Mattis List Better Models With Saussure
  42. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Long-Term Behaviour Proposition Behaviour (vocabulary size, polysemy, synonymy) should stabilize over long time scales at reasonable values. Test: Run the simulation along a branch with 2 000 000 time steps, using CLICS (see above) as semantic network. Gereon Kaiping, Mattis List Better Models With Saussure
  43. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Long-Term Behaviour Proposition Behaviour vocabulary size, polysemy, synonymy should stabilize over long time scales at reasonable values 100 101 102 103 104 105 106 time steps t 800 900 1000 1100 1200 1300 Vocabulary size 100 101 102 103 104 105 106 time steps t 0 1 2 3 4 5 Average Polysemy/Synonymity Polysemy Synonymity Gereon Kaiping, Mattis List Better Models With Saussure
  44. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Calibration Proposition Years:Replacement-Steps scaling parameter has an optimum Test: Run the simulation along a known dated tree (Chinese dialects from the Cíhuì) and compare with cross-semantically cognate coded data. Compare pairwise shared cognate proportion between real and simulated data. Gereon Kaiping, Mattis List Better Models With Saussure
  45. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Cíhuì Collection of Chinese dialects created in the late 1950s and published in 1964 (Běijīng University 1964) Contains lexical data, as the short title suggests (cíhuì means “lexical inventory” or Wortschatz in German) Based on a questionnaire consisting of 905 concepts (daily life and basic vocabulary) Offers data for 18 dialect varieties, including varieties from each of the seven largest dialect groups of Chines (Mǐn, Cantonese, Mandarin, Hakka, Wú, Xiāng, and Gàn) Data was prepared during List’s research project (2015-2016), digitized, and partial cognate coding was extracted automatically, based on annotations in the original source Gereon Kaiping, Mattis List Better Models With Saussure
  46. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Cíhuì Collection of Chinese dialects created in the late 1950s and published in 1964 (Běijīng University 1964) Contains lexical data, as the short title suggests (cíhuì means “lexical inventory” or Wortschatz in German) Based on a questionnaire consisting of 905 concepts (daily life and basic vocabulary) Offers data for 18 dialect varieties, including varieties from each of the seven largest dialect groups of Chines (Mǐn, Cantonese, Mandarin, Hakka, Wú, Xiāng, and Gàn) Data was prepared during List’s research project (2015-2016), digitized, and partial cognate coding was extracted automatically, based on annotations in the original source Gereon Kaiping, Mattis List Better Models With Saussure
  47. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Cíhuì Collection of Chinese dialects created in the late 1950s and published in 1964 (Běijīng University 1964) Contains lexical data, as the short title suggests (cíhuì means “lexical inventory” or Wortschatz in German) Based on a questionnaire consisting of 905 concepts (daily life and basic vocabulary) Offers data for 18 dialect varieties, including varieties from each of the seven largest dialect groups of Chines (Mǐn, Cantonese, Mandarin, Hakka, Wú, Xiāng, and Gàn) Data was prepared during List’s research project (2015-2016), digitized, and partial cognate coding was extracted automatically, based on annotations in the original source Gereon Kaiping, Mattis List Better Models With Saussure
  48. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Cíhuì Collection of Chinese dialects created in the late 1950s and published in 1964 (Běijīng University 1964) Contains lexical data, as the short title suggests (cíhuì means “lexical inventory” or Wortschatz in German) Based on a questionnaire consisting of 905 concepts (daily life and basic vocabulary) Offers data for 18 dialect varieties, including varieties from each of the seven largest dialect groups of Chines (Mǐn, Cantonese, Mandarin, Hakka, Wú, Xiāng, and Gàn) Data was prepared during List’s research project (2015-2016), digitized, and partial cognate coding was extracted automatically, based on annotations in the original source Gereon Kaiping, Mattis List Better Models With Saussure
  49. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Cíhuì Collection of Chinese dialects created in the late 1950s and published in 1964 (Běijīng University 1964) Contains lexical data, as the short title suggests (cíhuì means “lexical inventory” or Wortschatz in German) Based on a questionnaire consisting of 905 concepts (daily life and basic vocabulary) Offers data for 18 dialect varieties, including varieties from each of the seven largest dialect groups of Chines (Mǐn, Cantonese, Mandarin, Hakka, Wú, Xiāng, and Gàn) Data was prepared during List’s research project (2015-2016), digitized, and partial cognate coding was extracted automatically, based on annotations in the original source Gereon Kaiping, Mattis List Better Models With Saussure
  50. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Excursus: Cíhuì Xian Jinan Beijing Shenyang Chengdu Kunming Hefei Yangzhou Changsha Nanchang Wenzhou Suzhou Meixian Guangzhou Yangjiang Fuzhou Chaozhou Xiamen 100.0 Gereon Kaiping, Mattis List Better Models With Saussure
  51. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Calibration Proposition Years:Replacement-Steps scaling parameter has an optimum Values around 1.5 look best, and even reasonable, given the ad-hoc nature of the tree Gereon Kaiping, Mattis List Better Models With Saussure
  52. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Calibration Proposition Years:Replacement-Steps scaling parameter has an optimum Values around 1.5 look best, and even reasonable, given the ad-hoc nature of the tree Gereon Kaiping, Mattis List Better Models With Saussure
  53. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Semantic Shift Proposition The model shows reasonable amounts of semantic shift Test: Visually compare distribution of Meaning Classes/Cognate Class for simulated and real Cíhuì data Gereon Kaiping, Mattis List Better Models With Saussure
  54. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Semantic Shift Proposition The model shows reasonable amounts of semantic shift Note: Simulation results are not filtered to exclude synonyms Gereon Kaiping, Mattis List Better Models With Saussure
  55. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Missing Bits More comparable data for better calibration A better model of the semantics – and calibration of that (frequent pathways, etc.) Support for language contact (borrowings) Partial cognate support/Compositionality/Derivations Needs severe change in vocabulary representation, and some serious quantitative data Might help with language contact Population level modeling, for rate variation/punctuated evolution to emerge Gereon Kaiping, Mattis List Better Models With Saussure
  56. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Conclusions We have a realistic model of semantic shift. With more dated, cross-semantic-cognate-coded trees we can calibrate it more confidently. We (and you, it’s Open Source1!) can already use it to run and compare different tree building methods. 1http://github.com/Anaphory/simuling Gereon Kaiping, Mattis List Better Models With Saussure
  57. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Thanks Thank you for listening Gereon Kaiping, Mattis List Better Models With Saussure
  58. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Parameter Robustness: Concept Selection Weight degree_squared one preferential degree 0 200 400 600 800 1000 1200 1400 Vocabulary size degree_squared one preferential degree 1 2 3 4 5 6 7 8 Average Polysemy/Synonymity Polysemy Synonymity Gereon Kaiping, Mattis List Better Models With Saussure
  59. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Parameter Robustness: Neighbor factor 0.0 0.2 0.4 0.6 0.8 1.0 neighbor factor 0 200 400 600 800 1000 1200 1400 Vocabulary size 0.0 0.2 0.4 0.6 0.8 1.0 neighbor factor 1 2 3 4 5 6 Average Polysemy/Synonymity Polysemy Synonymity Gereon Kaiping, Mattis List Better Models With Saussure
  60. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Sources and Further Reading I Quentin Atkinson et al. “From Words to Dates: Water into Wine, Mathemagic or Phylogenetic Inference?” In: Transactions of the Philological Society 103.2 (Aug. 1, 2005), pp. 193–219. issn: 1467-968X. doi: 10.1111/j.1467-968X.2005.00151.x. url: http: //onlinelibrary.wiley.com/doi/10.1111/j.1467- 968X.2005.00151.x/abstract (visited on 03/23/2017). Gereon Kaiping, Mattis List Better Models With Saussure
  61. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Sources and Further Reading II François Barbançon et al. “An Experimental Study Comparing Linguistic Phylogenetic Reconstruction Methods”. In: Diachronica 30.2 (2013), pp. 143–170. doi: 10.1075/dia.30.2.01bar. url: http://www.ingentaconnect.com/content/jbp/dia/ 2013/00000030/00000002/art00001 (visited on 10/15/2016). Harald Hammarström et al. Glottolog. Version 2.5. URL: http://glottolog.org. Leipzig, 2015. Gereon Kaiping, Mattis List Better Models With Saussure
  62. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Sources and Further Reading III J.-M. List et al., eds. CLICS: Database of Cross-Linguistic Colexifications. Marburg: Forschungszentrum Deutscher Sprachatlas, 2014. Archived at: http://www.webcitation.org/6ccEMrZYM. url: http://clics.lingpy.org. Andrew D. M. Smith. “Models of Language Evolution and Change”. In: Wiley Interdisciplinary Reviews-Cognitive Science 5.3 (May 1, 2014). WOS:000334511800004, pp. 281–293. issn: 1939-5078. doi: 10.1002/wcs.1285. url: http://onlinelibrary.wiley.com/doi/10.1002/ wcs.1285/abstract. Gereon Kaiping, Mattis List Better Models With Saussure
  63. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Sources and Further Reading IV S.A. Starostin. “Computer-Based Simulation of the Glottochronological Process”. In: [Works on Linguistics]. Moscow: , 2007, pp. 854–862. Tandy Warnow et al. “A Stochastic Model of Language Evolution That Incorporates Homoplasy and Borrowing”. In: (). url: http://statistics.berkeley.edu/sites/ default/files/tech-reports/673.pdf (visited on 03/22/2017). Gereon Kaiping, Mattis List Better Models With Saussure
  64. Context and Motivation Our Model What we can and can’t

    do Closing Remarks References Gereon Kaiping, Mattis List Better Models With Saussure