File size: 101,603 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 | {
"paper_id": "R13-1015",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T14:56:06.397523Z"
},
"title": "Recognising and Interpreting Named Temporal Expressions",
"authors": [
{
"first": "Matteo",
"middle": [],
"last": "Brucato",
"suffix": "",
"affiliation": {},
"email": "matteo.brucato@studio.unibo.it"
},
{
"first": "Leon",
"middle": [],
"last": "Derczynski",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Hector",
"middle": [],
"last": "Llorens",
"suffix": "",
"affiliation": {},
"email": "hector.llorens@nuance.com"
},
{
"first": "Kalina",
"middle": [],
"last": "Bontcheva",
"suffix": "",
"affiliation": {},
"email": "kalina@dcs.shef.ac.uk"
},
{
"first": "Christian",
"middle": [
"S"
],
"last": "Jensen",
"suffix": "",
"affiliation": {},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This paper introduces a new class of temporal expression-named temporal expressions-and methods for recognising and interpreting its members. The commonest temporal expressions typically contain date and time words, like April or hours. Research into recognising and interpreting these typical expressions is mature in many languages. However, there is a class of expressions that are less typical, very varied, and difficult to automatically interpret. These indicate dates and times, but are harder to detect because they often do not contain time words and are not used frequently enough to appear in conventional temporally-annotated corporafor example Michaelmas or Vasant Panchami. Using Wikipedia and linked data, we automatically construct a resource of English named temporal expressions, and use it to extract training examples from a large corpus. These examples are then used to train and evaluate a named temporal expression recogniser. We also introduce and evaluate rules for automatically interpreting these expressions, and we observe that use of the rules improves temporal annotation performance over existing corpora.",
"pdf_parse": {
"paper_id": "R13-1015",
"_pdf_hash": "",
"abstract": [
{
"text": "This paper introduces a new class of temporal expression-named temporal expressions-and methods for recognising and interpreting its members. The commonest temporal expressions typically contain date and time words, like April or hours. Research into recognising and interpreting these typical expressions is mature in many languages. However, there is a class of expressions that are less typical, very varied, and difficult to automatically interpret. These indicate dates and times, but are harder to detect because they often do not contain time words and are not used frequently enough to appear in conventional temporally-annotated corporafor example Michaelmas or Vasant Panchami. Using Wikipedia and linked data, we automatically construct a resource of English named temporal expressions, and use it to extract training examples from a large corpus. These examples are then used to train and evaluate a named temporal expression recogniser. We also introduce and evaluate rules for automatically interpreting these expressions, and we observe that use of the rules improves temporal annotation performance over existing corpora.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "The ability to express time in language is critical. We require this ability in order to communicate plans, to tell stories, and to describe change in the world around us.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Phrases that explicitly describe certain periods of time, or temporal expressions, are particularly useful. They may be calendar dates, mentions of months, relative expressions like \"tomorrow\", and so on. In-depth accounts of temporal expressions -timexes -are given by Ferro et al. (2005) and Llorens et al. (2012a) .",
"cite_spans": [
{
"start": 270,
"end": 289,
"text": "Ferro et al. (2005)",
"ref_id": "BIBREF7"
},
{
"start": 294,
"end": 316,
"text": "Llorens et al. (2012a)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we discuss a new class of timexes that signify a date or range of dates, but that do not explicitly include information about which dates these are (e.g., October 31 vs. Halloween). Following the description of expressions that clearly identify one entity from a set of others by use of a proper noun as named entities, we call these named temporal expressions (or NTEs).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "As with many linguistic phenomena, the phrases used as timexes have a power law-like frequency distribution in text. A few forms of expression make up for the bulk of occurrences of temporal expressions. However, existing research has been typically evaluated on only a small corpus of hand-annotated temporal expressions. With such resources, it is difficult to build or evaluate tools for recognising or interpreting the lessfrequent temporal expressions, and this is reflected in the performance plateau of recent TempEval exercises (Verhagen et al., 2010; UzZaman et al., 2013) .",
"cite_spans": [
{
"start": 536,
"end": 559,
"text": "(Verhagen et al., 2010;",
"ref_id": "BIBREF28"
},
{
"start": 560,
"end": 581,
"text": "UzZaman et al., 2013)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Existing temporal expression recognition tools are typically rule-based (Str\u00f6tgen and Gertz, 2010) . These perform reasonably well on existing datasets, achieving F-scores of around 0.90, and improving them is an active area of research. However, as temporal annotation is expensive, existing datasets are not particularly large, and therefore do not contain as challenging a variety of forms of expression as general, unannotated text. Therefore, evaluations using these resources are unlikely to indicate the true variety of forms of temporal expression. This leaves us poorly equipped to handle the long tail of temporal expressions, which is likely to be very long (Steedman, 2011) , in terms of both tools and resources.",
"cite_spans": [
{
"start": 72,
"end": 98,
"text": "(Str\u00f6tgen and Gertz, 2010)",
"ref_id": "BIBREF24"
},
{
"start": 669,
"end": 685,
"text": "(Steedman, 2011)",
"ref_id": "BIBREF23"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "As the most common temporal expressions can be recognised automatically with reasonable accuracy, we propose methods for attacking the long tail of temporal expressions. We address the following questions:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 What share of all temporal expressions is accounted for by existing tools and corpora? \u2022 How can we recognise previously unseen named temporal expressions? \u2022 Having found a named temporal expression, how can we anchor it to a calendar date?",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The remainder of this paper discusses the most closely related work, examines variety in temporal expressions in the available corpora, introduces our approach for named timex recognition, briefly examines their role in information seeking, and discusses the problem of interpreting these unusal temporal expressions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "There is a reasonable amount of prior work on general-purpose timex recognition. The state of the art in temporal expression recognition is extended regularly with TempEval exercises (UzZaman et al., 2013) . Currently, HeidelTime (Str\u00f6tgen and Gertz, 2010) offers strong temporal expression recognition performance, though as it is ruleengineered, it is likely to perform poorly at recognising unseen named timexes. TIPSem (Llorens et al., 2012b) is based on machine learning and, given appropriate training data, has the potential to recognise named timexes. ANNIE (Cunningham et al., 2002) adopts a finite state approach to recognising a commonly-occurring but constrained set of temporal expressions. Han et al. (2006) propose interpreting temporal expressions through iterative constraint satisfaction, which yields some ability to interpret previously unseen timexes. Finally, as opposed to timexes, Shaw et al. (2009) used linked data to aid in event entity recognition. The distinguishing features of our approach are that we concentrate on temporal expressions that do not follow a general, structured format, and that instead of addressing the general timex recognition problem (which has been covered repeatedly in the 10 0 10 2 10 4 10 6 10 8 10 10 10 12 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10",
"cite_spans": [
{
"start": 183,
"end": 205,
"text": "(UzZaman et al., 2013)",
"ref_id": "BIBREF27"
},
{
"start": 230,
"end": 256,
"text": "(Str\u00f6tgen and Gertz, 2010)",
"ref_id": "BIBREF24"
},
{
"start": 423,
"end": 446,
"text": "(Llorens et al., 2012b)",
"ref_id": "BIBREF14"
},
{
"start": 566,
"end": 591,
"text": "(Cunningham et al., 2002)",
"ref_id": "BIBREF4"
},
{
"start": 704,
"end": 721,
"text": "Han et al. (2006)",
"ref_id": "BIBREF10"
},
{
"start": 905,
"end": 923,
"text": "Shaw et al. (2009)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Frequency count Rank 1-grams 2-grams 3-grams 4-grams 5-grams 10 0 10 1 10 2 10 3 10 4",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Frequency count literature, often from scratch), we address unusual expressions which are typically ignored by general purpose approaches.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Our goal is to be able to recognise temporal expressions beyond the scope of current temporal annotation systems, thus extending timex recognition. In order to measure the scope of existing systems, we need to estimate the scale of variety in temporal expressions. Using Google's Web1T n-gram corpus (Brants and Franz, 2006) , we drew the shape of the timex distribution curve. Firstly, we extracted the shape of the general term distribution curve; see Figure 1. Note the characteristic \"knee\" in the curve, after which terms become rarer than a plain Zipf-Mandelbrot distribution would suggest, as per Montemurro (2001) . For timexes, we counted n-grams based on timex strings found in two temporally-annotated corpora; TimeBank (Pustejovsky et al., 2003) , and the AQUAINT TimeML corpus. The resulting curve is shown in Figure 2 .",
"cite_spans": [
{
"start": 300,
"end": 324,
"text": "(Brants and Franz, 2006)",
"ref_id": "BIBREF2"
},
{
"start": 604,
"end": 621,
"text": "Montemurro (2001)",
"ref_id": "BIBREF16"
},
{
"start": 731,
"end": 757,
"text": "(Pustejovsky et al., 2003)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [
{
"start": 454,
"end": 460,
"text": "Figure",
"ref_id": null
},
{
"start": 823,
"end": 831,
"text": "Figure 2",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Variety in Temporal Expressions",
"sec_num": "3"
},
{
"text": "The sharp falloff of this timex curve is what one might expect to see from a very small corpus. Namely, some of the more common expressions are found, in relatively high frequency (the initial shallow curve). The remaining expressions found in the small sample that this corpus represents are much rarer, as shown by the sharp drop at the lowfrequency end of the curve. This suggests that existing TimeML corpora are so small that they do not include a sufficiently diverse selection of these terms. Indeed, TimeBank has only around 65K tokens. To build and evaluate approaches for recognising NTEs, a new source of data is required.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Variety in Temporal Expressions",
"sec_num": "3"
},
{
"text": "Having described named timexes, we build a named timex resource taking a re-usable, lowsupervision approach, and then construct a tool for automatic named timex discovery.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Automatic Named Timex Recognition",
"sec_num": "4"
},
{
"text": "Current TIMEX3-annotated resources do not account for a representatively broad set of temporal expressions ( Figure 2 ). To supplement these resources, we automatically mined named temporal expressions from Wikipedia.",
"cite_spans": [],
"ref_spans": [
{
"start": 109,
"end": 117,
"text": "Figure 2",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Mining Existing Named Timexes",
"sec_num": "4.1"
},
{
"text": "We started by identifying collections of these terms, for example on pages listing public holidays. The selection criterion was that the page be in English and have a reasonable number of NTE descriptions, marked up in a wiki table (e.g., Figure 3) . The pages used are listed in Figure 4 . We then automatically extracted the terms and their textual descriptions from these collections. An example extract is given in Table 1 . This data was supplemented using the holiday terms given in JollyDay, a Java date-handling library. 1 In total, we found 247 unique terms from 15 manually-selected Wikipedia pages, and 239 from JollyDay (containing an overlap of 54), for a total of 432 named timexes.",
"cite_spans": [
{
"start": 239,
"end": 248,
"text": "Figure 3)",
"ref_id": null
},
{
"start": 529,
"end": 530,
"text": "1",
"ref_id": null
}
],
"ref_spans": [
{
"start": 280,
"end": 288,
"text": "Figure 4",
"ref_id": null
},
{
"start": 419,
"end": 426,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Mining Existing Named Timexes",
"sec_num": "4.1"
},
{
"text": "The resulting list of candidate named temporal expressions contained two types of anomaly. It contained some conventional temporal expressions (e.g., August) which should be removed; these were filtered out using HeidelTime, a ruleengineered timex system. It also contained polysemous named timexes, that were not only used in a temporal sense. For example, Carnival is both a specific festival, a tour operator, and a polysemous common noun indicating a period of revelry or an exciting mixture of something.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Mining Existing Named Timexes",
"sec_num": "4.1"
},
{
"text": "Following Shaw et al. (2009) , we used linked open data to handle ambiguous temporal entities. We discriminated monosemous timexes (e.g., Reformation Day) from polysemous ones (e.g., Easter, which may be both a holiday and part of a compound noun referring to e.g. a chocolate egg) via DBpedia (Bizer et al., 2009) , looking for entities with matching names.",
"cite_spans": [
{
"start": 10,
"end": 28,
"text": "Shaw et al. (2009)",
"ref_id": "BIBREF22"
},
{
"start": 294,
"end": 314,
"text": "(Bizer et al., 2009)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Disambiguating NTEs with Linked Data",
"sec_num": "4.2"
},
{
"text": "After discarding URIs of media that were in film and song titles, NTEs that still had more than one remaining corresponding entity URI were identified as polysemous. The final set comprised 424 expressions, of which 342 were monosemous and 82 were polysemous.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Disambiguating NTEs with Linked Data",
"sec_num": "4.2"
},
{
"text": "Having built a collection of named temporal expressions, we moved on to the task of NTE discovery. Our approach was to first develop a statistical tagger adapted to NTE recognition, and then apply it to new data, to observe what expressions it annotates beyond those in the collection extracted from Wikipedia.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "The collection was used to construct a corpus and then a statistical named temporal expression recogniser. The corpus was constructed as follows. Using our list of monosemous named timexes, we searched the Gigaword corpus to retrieve paragraphs containing the timexes. These paragraphs were split into sentences (Kiss and Strunk, 2006) , and the sentences matching any NTE were extracted; the sentences were then broken down into lists of tokens. We marked all monosemous named timexes in the sentences as target entities.",
"cite_spans": [
{
"start": 312,
"end": 335,
"text": "(Kiss and Strunk, 2006)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "Some NTEs are polysemous, having both temporal and non-temporal sense. Observation of a small part of the corpus suggested that these polysemous NTEs generally occurred in a temporal sense when in the same sentence as other temporal phrases. Rather than excluding any sentence containing a polysemous NTE from the corpus on grounds of ambiguity, based on this observation, we adopted a simple heuristic: polysemous NTEs are included if they are collocated with a monosemous NTE. This reduced the considered set of polysemous NTEs by 22 to 60, for a total of 402 unique expressions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "Tokens in each sentence were then labelled according to a simple in-entity/out-of-entity binary format. The sentences were then split into training and evaluation sets, with no named temporal expressions found in both groups, i.e., every NTE is exclusively in either one or the other set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "In total, 3 861 sentences (117 060 tokens) were extracted from English Gigaword v5 (Graff et al., 2003) , containing 4 180 named timex annotations. The training split contained 1 053 of these sentences. The entire corpus construction method requires no human intervention aside from supplying source Wikipedia pages.",
"cite_spans": [
{
"start": 83,
"end": 103,
"text": "(Graff et al., 2003)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "Regarding the NTE recognisers, we adapted three entity recognition approaches to the task by discarding their default models and rebuilding new models based solely on this NTE corpus. The recognition tools were CRF-based: a multipurpose system incorporating non-local information, Stanford NER (Finkel et al., 2005) ; one for temporal entity recognition that uses semantic role information, TIPSem (Llorens et al., 2012b) ; and TIPSem-B, a baseline temporal entity recognition variant of TIPSem.",
"cite_spans": [
{
"start": 294,
"end": 315,
"text": "(Finkel et al., 2005)",
"ref_id": "BIBREF8"
},
{
"start": 398,
"end": 421,
"text": "(Llorens et al., 2012b)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "Recognisers were learned from the training split and evaluated on the test split. As we are attempting to recognise named timexes only, we do not do comparison against tools designed for standard timex recognition, as these are designed for a different task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "A na\u00efve gazetteer-matching baseline was used, based on timex strings found in existing resources (TimeBank and the AQUAINT TimeML annotations). This behaved exactly as a direct caseinsensitive word look-up, matching any whole phrases found within the corpus. Its recall should tell us how broad the range of temporal expressions found in prior TimeML resources is. Evaluation was performed using GATE (Cunningham et al., 2013) ; results are reported in Table 2 .",
"cite_spans": [
{
"start": 401,
"end": 426,
"text": "(Cunningham et al., 2013)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 453,
"end": 460,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "Precision was generally higher than recall, with both at reasonable levels for a first attempt at this new class of entities. This indicates that while our Recogniser % of query texts % of queries HeidelTime 2.90 1.97 NTE gazetteer 0.06 0.14 Table 3 : Temporal intent indicator prevalence in a web search query log approaches do not identify too many non-timexes as being timexes, further work is called for at improving the range of named timexes they recognise. In particular, the temporal expressions used in the TimeBank and AQUAINT corpora have a very small overlap with the named temporal expressions we identified.",
"cite_spans": [],
"ref_spans": [
{
"start": 242,
"end": 249,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Recognising Named Timexes in Text",
"sec_num": "4.3"
},
{
"text": "With a system that is capable of recognising named temporal expressions in our test data, which contains previously-unseen NTEs, it may be possible to discover new NTEs. Unlabelled text can be labelled using statistical NTE recognisers. One may have concerns over using a system with strict recognition precision in the 70s for this purpose; however, lenient recognition precision is in the mid-to high-90s, which indicates that the negative impact of spurious annotations will be low. We attempted to find new NTEs by applying the TIPSem model to another portion of the Gigaword text. Sample results include phrases such as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Finding New NTEs",
"sec_num": "4.4"
},
{
"text": "\u2022 European Cup \u2022 Hamlet Cup \u2022 bank holiday \u2022 Dayton peace agreement",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Finding New NTEs",
"sec_num": "4.4"
},
{
"text": "Although these are difficult to evaluate directly, they can readily applied in semi-supervised approaches to temporal annotation, e.g., in part of a bootstrapping approach to NTE recognition and general timex recognition.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Finding New NTEs",
"sec_num": "4.4"
},
{
"text": "This section contains a brief investigation of named temporal expressions (and general temporal expression recognition) in information retrieval query interpretation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Intent Queries",
"sec_num": "5"
},
{
"text": "In classical information retrieval with a textual query over a document collection, the query represents the lexicalisation of a searcher's information need. To identify a temporal information need, one must recognise signals in the query that reflect this (Jones and Diaz, 2007; Metzler et al., 2009) . Detecting temporal intent in queries may benefit from linguistic approaches to query understanding and decomposition (Campos et al., 2012) .",
"cite_spans": [
{
"start": 257,
"end": 279,
"text": "(Jones and Diaz, 2007;",
"ref_id": "BIBREF11"
},
{
"start": 280,
"end": 301,
"text": "Metzler et al., 2009)",
"ref_id": "BIBREF15"
},
{
"start": 421,
"end": 442,
"text": "(Campos et al., 2012)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Intent Queries",
"sec_num": "5"
},
{
"text": "Beyond common formulations of timexes, this is a challenging problem in two regards. As we have already explained, certain forms of temporal expression are not recognised by existing tools. Also, event-related queries (e.g., \"stock market reaction to michael jackson's death\") signify temporal intent but may not contain any temporal expressions at all. While the second class is not covered here, we do address the first.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Intent Queries",
"sec_num": "5"
},
{
"text": "We are interested in the proportion of temporal intent search queries that can be captured with awareness of named temporal expressions. Our method is to examine existing records of text questions and search engine queries, similar to the approach of Nunes et al. (2008) . We used 1 200 000 randomly sampled query strings from the AOL search log (Pass et al., 2006) as a corpus. This corpus comprises 167 794 unique terse query strings.",
"cite_spans": [
{
"start": 251,
"end": 270,
"text": "Nunes et al. (2008)",
"ref_id": "BIBREF17"
},
{
"start": 346,
"end": 365,
"text": "(Pass et al., 2006)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Intent Queries",
"sec_num": "5"
},
{
"text": "We ran HeidelTime (Str\u00f6tgen and Gertz, 2010) over this corpus. We also computed the intersection of query texts with our mined named timexes. Results are given in Table 3 .",
"cite_spans": [],
"ref_spans": [
{
"start": 163,
"end": 170,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Temporal Intent Queries",
"sec_num": "5"
},
{
"text": "While temporal expressions in general are notably frequent in the data, it can be seen that only a relatively small proportion of queries contain named temporal expressions (0.14%). Named temporal expressions are not dominant in queries from this corpus. Indeed, while the data suggests that general temporal expressions are in the long tail (as the proportion of timexes recognised in unique queries is greater than that in all queries), the inverse is true for named temporal expressions. Examining the data, only a few variants of NTE occur in the query log.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Intent Queries",
"sec_num": "5"
},
{
"text": "Once one has recognised that a particular expression is used in a temporal sense, the next step is to interpret the expression. This may entail anchoring it to a calendar or other formal representation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "We consider the task of interpreting timexes to the TimeML/TIMEX3 standard (Ferro et al., 2005) . This produces normalised values from timexes, as shown below.",
"cite_spans": [
{
"start": 75,
"end": 95,
"text": "(Ferro et al., 2005)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "(1) January 2nd, 1980 \u2192 1980-01-02 Summer 2012 \u2192 2012-SU now",
"cite_spans": [
{
"start": 12,
"end": 34,
"text": "2nd, 1980 \u2192 1980-01-02",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "\u2192 PRESENT REF id expression interpretation -- ---------- -------------- 92",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "Autumn_Holiday DATE_WEEK_WEEKNUM(DCT, -1, Monday ,TO_MONTH(\"September\")) 178",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "Liberation_Day DATE_MONTH_DAY(DCT, TO_MONTH(\"April\"), TO_DAY(\"25\")) 179",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "Republic_Day DATE_MONTH_DAY(DCT, TO_MONTH(\"June\"), TO_DAY(\"2\")) 180",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "Ferragosto DATE_MONTH_DAY(DCT, TO_MONTH(\"August\"), TO_DAY(\"15\")) Discovering such interpretations is a difficult task. For example, based on text, it is difficult to automatically learn or infer the link between \"New Year's Day\" and 1 st January, or the associations between north/south hemisphere and which months fall in summer, especially given the cost of temporal annotation and resulting scarcity of annotated resources. This often leaves the task of developing such interpretations to human computation (Sabou et al., 2012) . The closest computational method for solving this problem uses a more flexible compositional approach to timex interpretation (Angeli et al., 2012) , though it is prone to floundering and failing on completely new expressions, such as named timexes.",
"cite_spans": [
{
"start": 510,
"end": 530,
"text": "(Sabou et al., 2012)",
"ref_id": "BIBREF21"
},
{
"start": 659,
"end": 680,
"text": "(Angeli et al., 2012)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "As the named timexes mined from Wikipedia were generally accompanied by a textual description of the time (e.g., as in Figure 3 ), we used these descriptions to work out how to interpret the expression. We created a custom parser that worked well with the majority of uncurated, natural language descriptions of named timex dates. Having gathered information from Wikipedia, we then encoded it as rules in a popular timex interpretation system, TIMEN (Llorens et al., 2012a) .",
"cite_spans": [
{
"start": 451,
"end": 474,
"text": "(Llorens et al., 2012a)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [
{
"start": 119,
"end": 127,
"text": "Figure 3",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "TIMEN operates using expression capture rules over a language-specific knowledge base that contains information on temporal primitives such as weekday and month names. Rules chosen for normalisation are those that match the timex's pattern, in order of priority, highest first. If a rule has conditions, it can only be applied if the timex satisfies them. Matched rules operate on a priority and constraint-satisfaction basis.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "The rules in TIMEN allow the linking of contextual temporal information not explicit in the expression (such as document creation year) with time information in the expression. This expression-based information is often qualitative (i.e., text), and so TIMEN also includes rules for rendering it quantitative. For example, there are built-in functions that convert language-specific terms such as Monday, lunes or the second into quantitative offsets that operate over an internal knowledge base provided for that language. The result is a numeric representation of the temporal expression. This representation can be underspecified. For example, in the scope of NTEs, often the year is not mentioned, as it is documentdependent. As a result, the TIMEN rules for handling NTEs often do not declare any information about years, leaving this to TIMEN's management of reference time (Reichenbach, 1947) . Example rules for NTEs are shown in Figure 5 . In total, we successfully extracted interpretation rules for 298 of the previously-identified named timexes (70.3% of the NTEs in our inventory).",
"cite_spans": [
{
"start": 880,
"end": 899,
"text": "(Reichenbach, 1947)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [
{
"start": 938,
"end": 946,
"text": "Figure 5",
"ref_id": "FIGREF4"
}
],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "To evaluate this approach, we did timex interpretation only, using reference annotations of timex bounds. We ran the standard and augmented TIMEN over recent existing corpora (the TempEval-3 corpus and the TIMEN test data);results are in Table 4 . The additional rules improved TIMEN's ability to interpret named timexes. The error reduction figures demonstrate that improvements can be achieved by accounting for these timexes.",
"cite_spans": [],
"ref_spans": [
{
"start": 238,
"end": 245,
"text": "Table 4",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "Note the small improvement over the small TempEval-3 corpus (0.7%); upon examination, we found that this newswire corpus' content not only contained few named timexes, but in fact seemed to take pains to avoid mentioning festivals, possibly as part of areligious journalist guidelines.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "In any event, the indication is that newswire is a poor genre for the evaluation of timex annotation systems, due to its limited forms of expression. The TimenEval corpus was designed to be difficult to process, and it is over this data that we see the greatest improvement. The real contribution here is increasing the range of expressions that can be recognised and interpreted.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Temporal Expression Interpretation",
"sec_num": "6"
},
{
"text": "While recognising and interpreting named timexes is useful in many scenarios, and while it is possible to perform this task automatically, we encountered some interesting problems during our work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "7"
},
{
"text": "Spatial Variations: Many expressions are interpreted differently depending on the locale. For example, Labor Day is May 1 in much of the world, but is the first Monday in May in parts of Australia (Queensland and the Northern Territories) and the first Monday in September in the USA. While TIMEN can process variations in named timex interpretation over time (e.g., Washington Day is February 22 until 1971, after which it falls on the third Monday in February), this locale-based information is not always available and is not considered for the interpretation task. This may be possible as a future extension: separate modules can assess the origin or subject locale of the input text (based on, e.g., newswire lead-in, spelling variation, or location mentions, the last of which also requires spatial grounding or entity disambiguation) and pass this region information to rules for normalising, e.g., Summer.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "7"
},
{
"text": "Easter: Easter is difficult to interpret. 2 Its time is based on locale, year, which equinox is to be used (astronomical vs. religious), and many other factors. Also, many other named timexes depend on Easter, such as Pentecost, Lent, and Pancake Day. Being able to use Easter as an offset in date calculus will improve the coverage of named timex interpretation. The liturgical origins of the named timexes associated with the date provide some indication of the frequency of texts associated with named temporal expressions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "7"
},
{
"text": "Multiple Calendars: Not all named timexes can be calculated with one calendar. When building interpretation rules, demand for, e.g., lunar, astronomical, and Hebrew calendars emerges quickly. Even conventional dates require different calendars when one goes far back enough. A comprehensive timex interpretation tool must account for multiple calendars (Urgun et al., 2007) .",
"cite_spans": [
{
"start": 353,
"end": 373,
"text": "(Urgun et al., 2007)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "7"
},
{
"text": "Forms of Expression: Finally, diversity of expression may impair named timex recognition. The NTE Martin Luther King day, for example, may also be expressed as MLK day. In a sufficiently long text, one may use co-reference resolution to link and resolve the two. A statistical",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "7"
},
{
"text": "In this paper, we have introduced a new class of entities: named temporal expressions. These are hard to deal with because they do not resemble conventional temporal expressions, they can be expressed in a wide range of ways, they occur infrequently, and they cannot readily be interpreted to calendar dates.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "8"
},
{
"text": "We developed an approach for automatically extracting these named temporal expressions from Wikipedia, and we developed a named temporal expression corpus using linked data. This then helped train classifiers for automatically recognising (and thus discovering) named temporal expressions, with reasonable success (64.7% F1 measure). We also extracted interpretation rules for these expressions, allowing them to be converted to calendar dates, and used these to extend an existing state-of-the-art system. This augmented system had improved performance on existing temporally-annotated corpora.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Summary",
"sec_num": "8.1"
},
{
"text": "The mined expressions and the annotated sentences extracted from Gigaword are made available via an author's website. 3 Further, the TIMEN rules for normalising named timexes are also released, to be included in TIMEN.",
"cite_spans": [
{
"start": 118,
"end": 119,
"text": "3",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Resources",
"sec_num": "8.2"
},
{
"text": "Building basic approaches to timex normalisation is no longer an interesting or useful task. Multiple actively-maintained, state-of-the art tools address this problem, achieving good performance. However, as with many natural language processing problems, diminishing returns are being seen in the field. Therefore, next efforts must address the temporal expressions that we cannot yet already detect and interpret.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Future Work",
"sec_num": "8.3"
},
{
"text": "It is of interest to consider the automatic extraction of named timex resolution rules, perhaps using the most important timexes (Str\u00f6tgen et al., 2012) from articles describing the corresponding occasion. It is also relevant to merge our named timex corpus with existing timex corpora (e.g. Derczynski et al. (2012) ), after annotating the conventional timexes in our named timex training data. Such a corpus could be extended by extracting sentences that cite the Wikipedia or DBpedia entries corresponding to named timexes. Evaluation against such a resource is less likely to overreport the variety of expressions recognised by timex annotation systems, and can provide a solid base for future wide-coverage approaches to temporal expression recognition.",
"cite_spans": [
{
"start": 129,
"end": 152,
"text": "(Str\u00f6tgen et al., 2012)",
"ref_id": "BIBREF25"
},
{
"start": 292,
"end": 316,
"text": "Derczynski et al. (2012)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Future Work",
"sec_num": "8.3"
},
{
"text": "Decomposing the complex temporal annotation task so that it can be reliably crowdsourced would enable the construction of more resources. Using human computation like this is also likely to be useful in named timex sense disambiguation and interpretation, making it a promising source of more and better data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Future Work",
"sec_num": "8.3"
},
{
"text": "See http://jollyday.sourceforge.net/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "See https://en.wikipedia.org/wiki/Computus approach like our named timex recogniser (Section 4.3) may help here.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "See http://derczynski.com/sheffield/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This work was partially supported by funding from CHIST-ERA grant No. EP/K017896/1, uComp (www.ucomp.eu). The authors thank Barry Norton of Ontotext for helpful discussions on resolving events on the semantic web. The first two authors would also like to thank Aarhus University for their support.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Parsing time: Learning to interpret time expressions",
"authors": [
{
"first": "G",
"middle": [],
"last": "Angeli",
"suffix": ""
},
{
"first": "C",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Jurafsky",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "446--455",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G. Angeli, C. D. Manning, and D. Jurafsky. 2012. Parsing time: Learning to interpret time expressions. In Proceedings of the Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 446-455. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "DBpedia-A crystallization point for the Web of Data",
"authors": [
{
"first": "C",
"middle": [],
"last": "Bizer",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Lehmann",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Kobilarov",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Auer",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Becker",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Cyganiak",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Hellmann",
"suffix": ""
}
],
"year": 2009,
"venue": "Web Semantics: Science, Services and Agents on the World Wide Web",
"volume": "7",
"issue": "3",
"pages": "154--165",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann. 2009. DBpedia-A crystallization point for the Web of Data. Web Se- mantics: Science, Services and Agents on the World Wide Web, 7(3):154-165.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "The Google Web 1T 5-gram corpus version 1.1. LDC2006T13",
"authors": [
{
"first": "T",
"middle": [],
"last": "Brants",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Franz",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "T. Brants and A. Franz. 2006. The Google Web 1T 5-gram corpus version 1.1. LDC2006T13.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Enriching temporal query understanding through date identification: how to tag implicit temporal queries?",
"authors": [
{
"first": "R",
"middle": [],
"last": "Campos",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Dias",
"suffix": ""
},
{
"first": "A",
"middle": [
"M"
],
"last": "Jorge",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Nunes",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2nd Temporal Web Analytics Workshop",
"volume": "",
"issue": "",
"pages": "41--48",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. Campos, G. Dias, A. M. Jorge, and C. Nunes. 2012. Enriching temporal query understanding through date identification: how to tag implicit temporal queries? In Proceedings of the 2nd Temporal Web Analytics Workshop, pages 41-48. ACM.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications",
"authors": [
{
"first": "H",
"middle": [],
"last": "Cunningham",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Maynard",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Bontcheva",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Tablan",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan. 2002. GATE: A Framework and Graph- ical Development Environment for Robust NLP Tools and Applications. In Proceedings of the An- nual Meeting of the Association for Computational Linguistics. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Getting More Out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics",
"authors": [
{
"first": "H",
"middle": [],
"last": "Cunningham",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Tablan",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Roberts",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Bontcheva",
"suffix": ""
}
],
"year": 2013,
"venue": "PLoS computational biology",
"volume": "9",
"issue": "2",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Cunningham, V. Tablan, A. Roberts, and K. Bontcheva. 2013. Getting More Out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics. PLoS computational biology, 9(2):e1002854.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Massively Increasing TIMEX3 Resources: A Transduction Approach",
"authors": [
{
"first": "L",
"middle": [],
"last": "Derczynski",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Llorens",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Saquete",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the international Conference on Language Resources and Evaluation",
"volume": "",
"issue": "",
"pages": "3754--3761",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "L. Derczynski, H. Llorens, and E. Saquete. 2012. Mas- sively Increasing TIMEX3 Resources: A Transduc- tion Approach. In Proceedings of the international Conference on Language Resources and Evaluation, pages 3754-3761.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "TIDES 2005 standard for the annotation of temporal expressions",
"authors": [
{
"first": "L",
"middle": [],
"last": "Ferro",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Gerber",
"suffix": ""
},
{
"first": "I",
"middle": [],
"last": "Mani",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Sundheim",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Wilson",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "L. Ferro, L. Gerber, I. Mani, B. Sundheim, and G. Wil- son. 2005. TIDES 2005 standard for the annotation of temporal expressions.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Incorporating non-local information into information extraction systems by Gibbs sampling",
"authors": [
{
"first": "J",
"middle": [
"R"
],
"last": "Finkel",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Grenager",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "363--370",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. R. Finkel, T. Grenager, and C. Manning. 2005. In- corporating non-local information into information extraction systems by Gibbs sampling. In Proceed- ings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 363-370. Associ- ation for Computational Linguistics.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "From language to time: A temporal expression anchorer",
"authors": [
{
"first": "B",
"middle": [],
"last": "Han",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Gates",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Levin",
"suffix": ""
}
],
"year": 2006,
"venue": "Temporal Representation and Reasoning",
"volume": "",
"issue": "",
"pages": "196--203",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "B. Han, D. Gates, and L. Levin. 2006. From language to time: A temporal expression anchorer. In Tem- poral Representation and Reasoning, 2006. TIME 2006. Thirteenth International Symposium on, pages 196-203. IEEE.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Temporal profiles of queries",
"authors": [
{
"first": "R",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Diaz",
"suffix": ""
}
],
"year": 2007,
"venue": "ACM Transactions on Information Systems (TOIS)",
"volume": "25",
"issue": "3",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. Jones and F. Diaz. 2007. Temporal profiles of queries. ACM Transactions on Information Systems (TOIS), 25(3):14.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Unsupervised multilingual sentence boundary detection",
"authors": [
{
"first": "T",
"middle": [],
"last": "Kiss",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Strunk",
"suffix": ""
}
],
"year": 2006,
"venue": "Computational Linguistics",
"volume": "32",
"issue": "4",
"pages": "485--525",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "T. Kiss and J. Strunk. 2006. Unsupervised multilin- gual sentence boundary detection. Computational Linguistics, 32(4):485-525.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "TIMEN: An Open Temporal Expression Normalisation Resource",
"authors": [
{
"first": "H",
"middle": [],
"last": "Llorens",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Derczynski",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Gaizauskas",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Saquete",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 7th International Conference on Language Resources and Evaluation",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Llorens, L. Derczynski, R. Gaizauskas, and E. Sa- quete. 2012a. TIMEN: An Open Temporal Ex- pression Normalisation Resource. In Proceedings of the 7th International Conference on Language Re- sources and Evaluation.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language",
"authors": [
{
"first": "H",
"middle": [],
"last": "Llorens",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Saquete",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Navarro-Colorado",
"suffix": ""
}
],
"year": 2012,
"venue": "Information Processing & Management",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Llorens, E. Saquete, and B. Navarro-Colorado. 2012b. Applying semantic knowledge to the auto- matic processing of temporal expressions and events in natural language. Information Processing & Management.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Improving search relevance for implicitly temporal queries",
"authors": [
{
"first": "D",
"middle": [],
"last": "Metzler",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Peng",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Zhang",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval",
"volume": "",
"issue": "",
"pages": "700--701",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "D. Metzler, R. Jones, F. Peng, and R. Zhang. 2009. Improving search relevance for implicitly temporal queries. In Proceedings of the 32nd international ACM SIGIR conference on Research and develop- ment in information retrieval, pages 700-701. ACM.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Beyond the Zipf-Mandelbrot law in quantitative linguistics",
"authors": [
{
"first": "M",
"middle": [
"A"
],
"last": "Montemurro",
"suffix": ""
}
],
"year": 2001,
"venue": "Physica A: Statistical Mechanics and its Applications",
"volume": "300",
"issue": "3",
"pages": "567--578",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. A. Montemurro. 2001. Beyond the Zipf- Mandelbrot law in quantitative linguistics. Phys- ica A: Statistical Mechanics and its Applications, 300(3):567-578.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Use of temporal expressions in web search",
"authors": [
{
"first": "S",
"middle": [],
"last": "Nunes",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Ribeiro",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "David",
"suffix": ""
}
],
"year": 2008,
"venue": "Advances in Information Retrieval",
"volume": "",
"issue": "",
"pages": "580--584",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "S. Nunes, C. Ribeiro, and G. David. 2008. Use of temporal expressions in web search. In Advances in Information Retrieval, pages 580-584. Springer.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "A picture of search",
"authors": [
{
"first": "G",
"middle": [],
"last": "Pass",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Chowdhury",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Torgeson",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the 1st International Conference on Scalable Information Systems",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G. Pass, A. Chowdhury, and C. Torgeson. 2006. A picture of search. In Proceedings of the 1st Interna- tional Conference on Scalable Information Systems.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "TimeML: Robust specification of event and temporal expressions in text",
"authors": [
{
"first": "J",
"middle": [],
"last": "Pustejovsky",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Castano",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Ingria",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Sauri",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Gaizauskas",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Setzer",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Katz",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Radev",
"suffix": ""
}
],
"year": 2003,
"venue": "New directions in question answering",
"volume": "3",
"issue": "",
"pages": "28--34",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Pustejovsky, J. Castano, R. Ingria, R. Sauri, R. Gaizauskas, A. Setzer, G. Katz, and D. Radev. 2003. TimeML: Robust specification of event and temporal expressions in text. New directions in question answering, 3:28-34.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "The tenses of verbs",
"authors": [
{
"first": "H",
"middle": [],
"last": "Reichenbach",
"suffix": ""
}
],
"year": 1947,
"venue": "Elements of Symbolic Logic",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Reichenbach. 1947. The tenses of verbs. In Ele- ments of Symbolic Logic. Dover Publications.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Crowdsourcing research opportunities: lessons from natural language processing",
"authors": [
{
"first": "M",
"middle": [],
"last": "Sabou",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Bontcheva",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Scharl",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Sabou, K. Bontcheva, and A. Scharl. 2012. Crowd- sourcing research opportunities: lessons from natu- ral language processing. In Proceedings of the 12th International Conference on Knowledge Manage- ment and Knowledge Technologies, page 17. ACM.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "LODE: Linking Open Descriptions of Events",
"authors": [
{
"first": "R",
"middle": [],
"last": "Shaw",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Troncy",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Hardman",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the Asian Semantic Web Conference",
"volume": "5926",
"issue": "",
"pages": "153--167",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. Shaw, R. Troncy, and L. Hardman. 2009. LODE: Linking Open Descriptions of Events. In Proceed- ings of the Asian Semantic Web Conference, volume LNCS 5926, pages 153-167. Springer.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Romantics and revolutionaries. Linguistic Issues in Language Technology",
"authors": [
{
"first": "M",
"middle": [],
"last": "Steedman",
"suffix": ""
}
],
"year": 2011,
"venue": "",
"volume": "6",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Steedman. 2011. Romantics and revolutionaries. Linguistic Issues in Language Technology, 6(11).",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "HeidelTime: High quality rule-based extraction and normalization of temporal expressions",
"authors": [
{
"first": "J",
"middle": [],
"last": "Str\u00f6tgen",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Gertz",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 5th International Workshop on Semantic Evaluation",
"volume": "",
"issue": "",
"pages": "321--324",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Str\u00f6tgen and M. Gertz. 2010. HeidelTime: High quality rule-based extraction and normalization of temporal expressions. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 321-324. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Identification of top relevant temporal expressions in documents",
"authors": [
{
"first": "J",
"middle": [],
"last": "Str\u00f6tgen",
"suffix": ""
},
{
"first": "O",
"middle": [],
"last": "Alonso",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Gertz",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2nd Temporal Web Analytics Workshop",
"volume": "",
"issue": "",
"pages": "33--40",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Str\u00f6tgen, O. Alonso, and M. Gertz. 2012. Identifi- cation of top relevant temporal expressions in doc- uments. In Proceedings of the 2nd Temporal Web Analytics Workshop, pages 33-40. ACM.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "tegrating multiple calendars using \u03c4 ZAMAN. Software: Practice and Experience",
"authors": [
{
"first": "B",
"middle": [],
"last": "Urgun",
"suffix": ""
},
{
"first": "C",
"middle": [
"E"
],
"last": "Dyreson",
"suffix": ""
},
{
"first": "R",
"middle": [
"T"
],
"last": "Snodgrass",
"suffix": ""
},
{
"first": "J",
"middle": [
"K"
],
"last": "Miller",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Kline",
"suffix": ""
},
{
"first": "M",
"middle": [
"D"
],
"last": "Soo",
"suffix": ""
},
{
"first": "C",
"middle": [
"S"
],
"last": "Jensen",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "37",
"issue": "",
"pages": "267--308",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "B. Urgun, C. E. Dyreson, R. T. Snodgrass, J. K. Miller, N. Kline, M. D. Soo, and C. S. Jensen. 2007. In- tegrating multiple calendars using \u03c4 ZAMAN. Soft- ware: Practice and Experience, 37(3):267-308.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations",
"authors": [
{
"first": "N",
"middle": [],
"last": "Uzzaman",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Llorens",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Derczynski",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Verhagen",
"suffix": ""
},
{
"first": "J",
"middle": [
"F"
],
"last": "Allen",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Pustejovsky",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 7th International Workshop on Semantic Evaluations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "N. UzZaman, H. Llorens, L. Derczynski, M. Verhagen, J. F. Allen, and J. Pustejovsky. 2013. SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations. In Proceedings of the 7th International Workshop on Semantic Evalu- ations.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "SemEval-2010 task 13: TempEval-2",
"authors": [
{
"first": "M",
"middle": [],
"last": "Verhagen",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Saur\u00ed",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Caselli",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Pustejovsky",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 5th International Workshop on Semantic Evaluation",
"volume": "",
"issue": "",
"pages": "57--62",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Verhagen, R. Saur\u00ed, T. Caselli, and J. Pustejovsky. 2010. SemEval-2010 task 13: TempEval-2. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 57-62. Association for Computational Linguistics.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "Figure 1: Frequency distribution of general terms",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF2": {
"text": "Frequency distribution of terms used as timexes in TimeBank and AQUAINT",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF3": {
"text": "Holidays from a country (Bangladesh), as shown on a Wikipedia page",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF4": {
"text": "Example named timex rules in TIMEN",
"uris": null,
"type_str": "figure",
"num": null
},
"TABREF0": {
"text": "en.wikipedia.org/wiki/...",
"content": "<table><tr><td colspan=\"2\">Federal_holidays_in_the_United_States</td></tr><tr><td colspan=\"2\">Public_and_Bank_holidays_in_Scotland</td></tr><tr><td colspan=\"2\">Public_holidays_in_Australia</td></tr><tr><td colspan=\"2\">Public_holidays_in_Canada</td></tr><tr><td colspan=\"2\">Public_holidays_in_Denmark</td></tr><tr><td colspan=\"2\">Public_holidays_in_France</td></tr><tr><td colspan=\"2\">Public_holidays_in_Germany</td></tr><tr><td colspan=\"2\">Public_holidays_in_Hong_Kong</td></tr><tr><td colspan=\"2\">Public_holidays_in_India</td></tr><tr><td colspan=\"2\">Public_holidays_in_Italy</td></tr><tr><td colspan=\"2\">Public_holidays_in_Malaysia</td></tr><tr><td colspan=\"2\">Public_holidays_in_South_Africa</td></tr><tr><td colspan=\"2\">Public_holidays_in_the_European_Union</td></tr><tr><td colspan=\"2\">Public_holidays_in_the_United_Kingdom</td></tr><tr><td colspan=\"2\">Public_holidays_in_the_United_States</td></tr><tr><td colspan=\"2\">Figure 4: URLs from which source NTE descriptions were</td></tr><tr><td>extracted</td><td/></tr><tr><td>Official name</td><td>Date</td></tr><tr><td>Columbus Day</td><td>Second Monday in October</td></tr><tr><td>Veterans Day</td><td>November 11</td></tr><tr><td colspan=\"2\">Thanksgiving Day Fourth Thursday in November</td></tr><tr><td>Christmas</td><td>December 25</td></tr></table>",
"html": null,
"num": null,
"type_str": "table"
},
"TABREF1": {
"text": "",
"content": "<table/>",
"html": null,
"num": null,
"type_str": "table"
},
"TABREF3": {
"text": "Sample Wikipedia events and interpretations. Lenient matches includes annotations that at least overlap with the reference.",
"content": "<table/>",
"html": null,
"num": null,
"type_str": "table"
},
"TABREF5": {
"text": "Timex interpretation accuracy with and without rules mined from Wikipedia. ER is Error Reduction",
"content": "<table/>",
"html": null,
"num": null,
"type_str": "table"
}
}
}
} |