977:, where words are mapped directly to indices with a hashing function. Thus, no memory is required to store a dictionary. Hash collisions are typically dealt via freed-up memory to increase the number of hash buckets. In practice, hashing simplifies the implementation of bag-of-words models and improves scalability.
639:" and "dog bites man" are the same, so any algorithm that operates with a BoW representation of text must treat them in the same way. Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple tasks that do not require word order. For instance, for
1083:
And this stock of combinations of elements becomes a factor in the way later choices are made ... for language is not merely a bag of words but a tool with particular properties which have been fashioned in the course of its
675:
alternatives have been developed to account for the class label of a document. Lastly, binary (presence/absence or 1/0) weighting is used in place of frequencies for some problems (e.g., this option is implemented in the
667:
Implementations of the bag-of-words model might involve using frequencies of words in a document to represent its contents. The frequencies can be "normalized" by the inverse of document frequency, or
626:
643:, if the words "stocks" "trade" "investors" appears multiple times, then the text is likely a financial report, even though it would be insufficient to distinguish between
1208:
1368:
1098:
1346:
1757:
1201:
1117:
Weinberger, K. Q.; Dasgupta A.; Langford J.; Smola A.; Attenberg, J. (2009). "Feature hashing for large scale multitask learning".
1926:
572:
66:
20:
1957:
1667:
1358:
1194:
1921:
1103:
1528:
1682:
1513:
1152:
1453:
1870:
1523:
1518:
1263:
1787:
1508:
1480:
1096:
Youngjoong Ko (2012). "A study of term weighting schemes using class information for text classification".
399:
Each key is the word, and each value is the number of occurrences of that word in the given text document.
30:(BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "
1825:
1810:
1782:
1647:
1642:
1217:
35:
1562:
1533:
1311:
659:
and so the BoW representation would be insufficient to determine the detailed meaning of the document.
62:
58:
47:
1962:
1405:
1258:
635:
The BoW representation of a text removes all word ordering. For example, the BoW representation of "
1931:
1855:
1587:
1543:
1428:
1326:
1835:
1805:
1472:
677:
640:
54:
1306:
421:(3) John likes to watch movies. Mary likes movies too. Mary also likes to watch football games.
1692:
1385:
1363:
1353:
1321:
1296:
1552:
88:
The following models a text document using bag-of-words. Here are two simple text documents:
39:
1905:
1581:
1557:
1410:
1132:
8:
1885:
1815:
1772:
1728:
1500:
1490:
1485:
1373:
1041:
672:
1136:
1895:
1767:
1632:
1395:
1378:
1158:
1122:
1006:
991:
986:
101:
Based on these two text documents, a list is constructed as follows for each document:
1900:
1612:
1420:
1331:
1148:
565:, the "union" of two documents in the bags-of-words representation is, formally, the
1777:
1662:
1637:
1438:
1341:
1140:
1078:
1074:
996:
1162:
1889:
1850:
1845:
1713:
1443:
1316:
1291:
1273:
1597:
1577:
1301:
566:
1186:
1049:
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31, NO. 4
1951:
1860:
1672:
1652:
1433:
1062:
974:
636:
73:
72:
An early reference to "bag of words" in a linguistic context can be found in
1144:
57:
where, for example, the (frequency of) occurrence of each word is used as a
1840:
1458:
1119:
Proceedings of the 26th Annual
International Conference on Machine Learning
1797:
1677:
1390:
1283:
1231:
1011:
1116:
1400:
668:
203:
43:
1268:
1743:
1723:
1708:
1687:
1657:
1602:
1567:
1448:
655:
Yesterday, investors were retreating, but today, they are rallying.
647:
Yesterday, investors were rallying, but today, they are retreating.
562:
31:
1127:
1880:
1738:
1718:
1592:
1336:
1251:
1001:
404:{"too":1,"Mary":1,"movies":2,"John":1,"watch":1,"likes":2,"to":1}
1246:
1241:
1936:
1572:
671:. Additionally, for the specific purpose of classification,
199:
1042:"Efficient visual search of videos cast as text retrieval"
1733:
417:
Note: if another document is like a union of these two,
92:(1) John likes to watch movies. Mary likes movies too.
53:
The bag-of-words model is commonly used in methods of
575:
16:
Text represented as an unordered collection of words
690:# Make sure to install the necessary packages first
19:For the bag-of-words model in computer vision, see
973:A common alternative to using dictionaries is the
620:
46:(and thus most of syntax or grammar) but captures
1949:
1419:
1216:
402:The order of elements is free, so, for example
1202:
1095:
569:, summing the multiplicities of each element.
97:(2) Mary also likes to watch football games.
1110:
1209:
1195:
410:. It is also what we expect from a strict
1126:
683:
621:{\displaystyle BoW3=BoW1\biguplus BoW2}
425:its JavaScript representation will be:
1950:
1061:
1190:
1039:
21:Bag-of-words model in computer vision
1668:Simple Knowledge Organization System
1183:. Springer International Publishing.
1065:(1954). "Distributional Structure".
1033:
202:, and attributing to the respective
198:Representing each bag-of-words as a
680:machine learning software system).
13:
662:
14:
1974:
1683:Thesaurus (information retrieval)
1179:McTear, Michael (et al) (2016).
968:
1051:. opposition. pp. 591–605.
1264:Natural language understanding
1089:
1079:10.1080/00437956.1954.11659520
1055:
1024:
1:
1788:Optical character recognition
1173:
897:"Bag of word sentence 1:
630:
83:
1481:Multi-document summarization
1181:The Conversational Interface
7:
1958:Natural language processing
1811:Latent Dirichlet allocation
1783:Natural language generation
1648:Machine-readable dictionary
1643:Linguistic Linked Open Data
1218:Natural language processing
1040:Sivic, Josef (April 2009).
980:
693:# pip install --upgrade pip
36:natural language processing
34:") of words. It is used in
10:
1979:
1563:Explicit semantic analysis
1312:Deep linguistic processing
1030:McTear et al 2016, p. 167.
18:
1914:
1869:
1824:
1796:
1756:
1701:
1623:
1611:
1542:
1499:
1471:
1406:Word-sense disambiguation
1282:
1259:Computational linguistics
1224:
1932:Natural Language Toolkit
1856:Pronunciation assessment
1758:Automatic identification
1588:Latent semantic analysis
1544:Distributional semantics
1429:Compound-term processing
1327:Named-entity recognition
1017:
726:keras.preprocessing.text
696:# pip install tensorflow
687:
427:
419:
208:
103:
95:
90:
78:Distributional Structure
67:used for computer vision
1836:Automated essay scoring
1806:Document classification
1473:Automatic summarization
1145:10.1145/1553374.1553516
641:document classification
55:document classification
1693:Universal Dependencies
1386:Terminology extraction
1369:Semantic decomposition
1364:Semantic role labeling
1354:Part-of-speech tagging
1322:Information extraction
1307:Coreference resolution
1297:Collocation extraction
1121:. pp. 1113–1120.
657:
649:
622:
406:is also equivalent to
1454:Sentence segmentation
684:Python implementation
653:
645:
623:
561:So, as we see in the
40:information retrieval
1906:Voice user interface
1617:datasets and corpora
1558:Document-term matrix
1411:Word-sense induction
948:unique tokens."
573:
535:"football"
373:"football"
187:"football"
42:(IR). It disregards
1886:Interactive fiction
1816:Pachinko allocation
1773:Speech segmentation
1729:Google Ngram Viewer
1501:Machine translation
1491:Text simplification
1486:Sentence extraction
1374:Semantic similarity
1137:2009arXiv0902.2206W
76:'s 1954 article on
65:. It has also been
1896:Question answering
1768:Speech recognition
1633:Corpus linguistics
1613:Language resources
1396:Textual entailment
1379:Sentiment analysis
1007:Vector space model
992:Feature extraction
987:Additive smoothing
813:texts_to_sequences
618:
487:"movies"
268:"movies"
148:"movies"
130:"movies"
28:bag-of-words model
1945:
1944:
1901:Virtual assistant
1826:Computer-assisted
1752:
1751:
1509:Computer-assisted
1467:
1466:
1459:Word segmentation
1421:Text segmentation
1359:Semantic analysis
1347:Syntactic parsing
1332:Ontology learning
547:"games"
475:"watch"
451:"likes"
385:"games"
361:"watch"
337:"likes"
256:"watch"
232:"likes"
193:"games"
181:"watch"
169:"likes"
142:"likes"
124:"watch"
112:"likes"
1970:
1963:Machine learning
1922:Formal semantics
1871:Natural language
1778:Speech synthesis
1760:and data capture
1663:Semantic network
1638:Lexical resource
1621:
1620:
1439:Lexical analysis
1417:
1416:
1342:Semantic parsing
1211:
1204:
1197:
1188:
1187:
1167:
1166:
1130:
1114:
1108:
1107:
1093:
1087:
1086:
1059:
1053:
1052:
1046:
1037:
1031:
1028:
997:Machine learning
964:
961:
958:
955:
952:
949:
946:
943:
940:
937:
934:
931:
928:
925:
922:
919:
916:
913:
910:
907:
904:
901:
898:
895:
892:
889:
886:
883:
880:
877:
874:
871:
868:
865:
862:
859:
856:
853:
850:
847:
844:
841:
838:
835:
832:
829:
826:
823:
820:
817:
814:
811:
808:
805:
802:
799:
796:
793:
790:
787:
784:
781:
778:
775:
772:
769:
766:
763:
760:
757:
754:
751:
748:
745:
742:
739:
736:
733:
730:
727:
724:
721:
718:
715:
712:
709:
706:
703:
700:
697:
694:
691:
627:
625:
624:
619:
557:
554:
551:
548:
545:
542:
539:
536:
533:
530:
527:
524:
523:"also"
521:
518:
515:
512:
509:
506:
503:
500:
499:"Mary"
497:
494:
491:
488:
485:
482:
479:
476:
473:
470:
467:
464:
461:
458:
455:
452:
449:
446:
443:
440:
439:"John"
437:
434:
431:
414:representation.
405:
395:
392:
389:
386:
383:
380:
377:
374:
371:
368:
365:
362:
359:
356:
353:
350:
347:
344:
341:
338:
335:
332:
329:
326:
325:"also"
323:
320:
317:
314:
313:"Mary"
311:
308:
305:
302:
299:
296:
293:
290:
287:
284:
281:
280:"Mary"
278:
275:
272:
269:
266:
263:
260:
257:
254:
251:
248:
245:
242:
239:
236:
233:
230:
227:
224:
221:
220:"John"
218:
215:
212:
194:
191:
188:
185:
182:
179:
176:
173:
170:
167:
164:
163:"also"
161:
158:
157:"Mary"
155:
152:
149:
146:
143:
140:
137:
136:"Mary"
134:
131:
128:
125:
122:
119:
116:
113:
110:
107:
106:"John"
1978:
1977:
1973:
1972:
1971:
1969:
1968:
1967:
1948:
1947:
1946:
1941:
1910:
1890:Syntax guessing
1872:
1865:
1851:Predictive text
1846:Grammar checker
1827:
1820:
1792:
1759:
1748:
1714:Bank of English
1697:
1625:
1616:
1607:
1538:
1495:
1463:
1415:
1317:Distant reading
1292:Argument mining
1278:
1274:Text processing
1220:
1215:
1176:
1171:
1170:
1155:
1115:
1111:
1094:
1090:
1073:(2/3): 146–62.
1060:
1056:
1044:
1038:
1034:
1029:
1025:
1020:
983:
971:
966:
965:
962:
959:
956:
953:
950:
947:
944:
941:
938:
935:
932:
929:
927:"We found
926:
923:
920:
917:
914:
911:
908:
905:
902:
899:
896:
893:
890:
887:
884:
881:
878:
875:
872:
869:
866:
863:
860:
857:
854:
851:
848:
845:
842:
839:
836:
833:
830:
827:
824:
821:
818:
815:
812:
809:
806:
803:
800:
797:
794:
791:
788:
785:
782:
779:
776:
773:
770:
767:
764:
761:
758:
755:
752:
749:
746:
743:
740:
737:
734:
731:
728:
725:
722:
719:
716:
713:
710:
707:
704:
701:
698:
695:
692:
689:
686:
665:
663:Implementations
633:
574:
571:
570:
559:
558:
555:
552:
549:
546:
543:
540:
537:
534:
531:
528:
525:
522:
519:
516:
513:
511:"too"
510:
507:
504:
501:
498:
495:
492:
489:
486:
483:
480:
477:
474:
471:
468:
465:
462:
459:
456:
453:
450:
447:
444:
441:
438:
435:
432:
429:
423:
422:
403:
397:
396:
393:
390:
387:
384:
381:
378:
375:
372:
369:
366:
363:
360:
357:
354:
351:
348:
345:
342:
339:
336:
333:
330:
327:
324:
321:
318:
315:
312:
309:
306:
303:
300:
297:
294:
292:"too"
291:
288:
285:
282:
279:
276:
273:
270:
267:
264:
261:
258:
255:
252:
249:
246:
243:
240:
237:
234:
231:
228:
225:
222:
219:
216:
213:
210:
196:
195:
192:
189:
186:
183:
180:
177:
174:
171:
168:
165:
162:
159:
156:
154:"too"
153:
150:
147:
144:
141:
138:
135:
132:
129:
126:
123:
120:
117:
114:
111:
108:
105:
99:
98:
94:
93:
86:
61:for training a
24:
17:
12:
11:
5:
1976:
1966:
1965:
1960:
1943:
1942:
1940:
1939:
1934:
1929:
1924:
1918:
1916:
1912:
1911:
1909:
1908:
1903:
1898:
1893:
1883:
1877:
1875:
1873:user interface
1867:
1866:
1864:
1863:
1858:
1853:
1848:
1843:
1838:
1832:
1830:
1822:
1821:
1819:
1818:
1813:
1808:
1802:
1800:
1794:
1793:
1791:
1790:
1785:
1780:
1775:
1770:
1764:
1762:
1754:
1753:
1750:
1749:
1747:
1746:
1741:
1736:
1731:
1726:
1721:
1716:
1711:
1705:
1703:
1699:
1698:
1696:
1695:
1690:
1685:
1680:
1675:
1670:
1665:
1660:
1655:
1650:
1645:
1640:
1635:
1629:
1627:
1618:
1609:
1608:
1606:
1605:
1600:
1598:Word embedding
1595:
1590:
1585:
1578:Language model
1575:
1570:
1565:
1560:
1555:
1549:
1547:
1540:
1539:
1537:
1536:
1531:
1529:Transfer-based
1526:
1521:
1516:
1511:
1505:
1503:
1497:
1496:
1494:
1493:
1488:
1483:
1477:
1475:
1469:
1468:
1465:
1464:
1462:
1461:
1456:
1451:
1446:
1441:
1436:
1431:
1425:
1423:
1414:
1413:
1408:
1403:
1398:
1393:
1388:
1382:
1381:
1376:
1371:
1366:
1361:
1356:
1351:
1350:
1349:
1344:
1334:
1329:
1324:
1319:
1314:
1309:
1304:
1302:Concept mining
1299:
1294:
1288:
1286:
1280:
1279:
1277:
1276:
1271:
1266:
1261:
1256:
1255:
1254:
1249:
1239:
1234:
1228:
1226:
1222:
1221:
1214:
1213:
1206:
1199:
1191:
1185:
1184:
1175:
1172:
1169:
1168:
1153:
1109:
1088:
1063:Harris, Zellig
1054:
1032:
1022:
1021:
1019:
1016:
1015:
1014:
1009:
1004:
999:
994:
989:
982:
979:
970:
967:
688:
685:
682:
664:
661:
632:
629:
617:
614:
611:
608:
605:
602:
599:
596:
593:
590:
587:
584:
581:
578:
567:disjoint union
463:"to"
428:
420:
349:"to"
244:"to"
209:
175:"to"
118:"to"
104:
96:
91:
85:
82:
15:
9:
6:
4:
3:
2:
1975:
1964:
1961:
1959:
1956:
1955:
1953:
1938:
1935:
1933:
1930:
1928:
1927:Hallucination
1925:
1923:
1920:
1919:
1917:
1913:
1907:
1904:
1902:
1899:
1897:
1894:
1891:
1887:
1884:
1882:
1879:
1878:
1876:
1874:
1868:
1862:
1861:Spell checker
1859:
1857:
1854:
1852:
1849:
1847:
1844:
1842:
1839:
1837:
1834:
1833:
1831:
1829:
1823:
1817:
1814:
1812:
1809:
1807:
1804:
1803:
1801:
1799:
1795:
1789:
1786:
1784:
1781:
1779:
1776:
1774:
1771:
1769:
1766:
1765:
1763:
1761:
1755:
1745:
1742:
1740:
1737:
1735:
1732:
1730:
1727:
1725:
1722:
1720:
1717:
1715:
1712:
1710:
1707:
1706:
1704:
1700:
1694:
1691:
1689:
1686:
1684:
1681:
1679:
1676:
1674:
1673:Speech corpus
1671:
1669:
1666:
1664:
1661:
1659:
1656:
1654:
1653:Parallel text
1651:
1649:
1646:
1644:
1641:
1639:
1636:
1634:
1631:
1630:
1628:
1622:
1619:
1614:
1610:
1604:
1601:
1599:
1596:
1594:
1591:
1589:
1586:
1583:
1579:
1576:
1574:
1571:
1569:
1566:
1564:
1561:
1559:
1556:
1554:
1551:
1550:
1548:
1545:
1541:
1535:
1532:
1530:
1527:
1525:
1522:
1520:
1517:
1515:
1514:Example-based
1512:
1510:
1507:
1506:
1504:
1502:
1498:
1492:
1489:
1487:
1484:
1482:
1479:
1478:
1476:
1474:
1470:
1460:
1457:
1455:
1452:
1450:
1447:
1445:
1444:Text chunking
1442:
1440:
1437:
1435:
1434:Lemmatisation
1432:
1430:
1427:
1426:
1424:
1422:
1418:
1412:
1409:
1407:
1404:
1402:
1399:
1397:
1394:
1392:
1389:
1387:
1384:
1383:
1380:
1377:
1375:
1372:
1370:
1367:
1365:
1362:
1360:
1357:
1355:
1352:
1348:
1345:
1343:
1340:
1339:
1338:
1335:
1333:
1330:
1328:
1325:
1323:
1320:
1318:
1315:
1313:
1310:
1308:
1305:
1303:
1300:
1298:
1295:
1293:
1290:
1289:
1287:
1285:
1284:Text analysis
1281:
1275:
1272:
1270:
1267:
1265:
1262:
1260:
1257:
1253:
1250:
1248:
1245:
1244:
1243:
1240:
1238:
1235:
1233:
1230:
1229:
1227:
1225:General terms
1223:
1219:
1212:
1207:
1205:
1200:
1198:
1193:
1192:
1189:
1182:
1178:
1177:
1164:
1160:
1156:
1154:9781605585161
1150:
1146:
1142:
1138:
1134:
1129:
1124:
1120:
1113:
1105:
1101:
1100:
1092:
1085:
1080:
1076:
1072:
1068:
1064:
1058:
1050:
1043:
1036:
1027:
1023:
1013:
1010:
1008:
1005:
1003:
1000:
998:
995:
993:
990:
988:
985:
984:
978:
976:
975:hashing trick
969:Hashing trick
681:
679:
674:
670:
660:
656:
652:
648:
644:
642:
638:
637:man bites dog
628:
615:
612:
609:
606:
603:
600:
597:
594:
591:
588:
585:
582:
579:
576:
568:
564:
426:
418:
415:
413:
409:
400:
207:
205:
201:
102:
89:
81:
79:
75:
74:Zellig Harris
70:
68:
64:
60:
56:
51:
49:
45:
41:
37:
33:
29:
22:
1841:Concordancer
1237:Bag-of-words
1236:
1180:
1118:
1112:
1097:
1091:
1082:
1070:
1066:
1057:
1048:
1035:
1026:
972:
789:fit_on_texts
666:
658:
654:
650:
646:
634:
560:
424:
416:
411:
407:
401:
398:
197:
100:
87:
77:
71:
52:
48:multiplicity
27:
25:
1798:Topic model
1678:Text corpus
1524:Statistical
1391:Text mining
1232:AI-complete
1012:w-shingling
563:bag algebra
412:JSON object
200:JSON object
1952:Categories
1519:Rule-based
1401:Truecasing
1269:Stop words
1174:References
939:word_index
882:word_index
858:word_index
837:word_index
825:word_index
702:tensorflow
673:supervised
631:Word order
206:variable:
204:JavaScript
84:Definition
63:classifier
44:word order
1828:reviewing
1626:standards
1624:Types and
1128:0902.2206
954:print_bow
870:sequences
831:tokenizer
807:tokenizer
801:sequences
783:tokenizer
777:Tokenizer
771:tokenizer
744:print_bow
732:Tokenizer
604:⨄
1744:Wikidata
1724:FrameNet
1709:BabelNet
1688:Treebank
1658:PropBank
1603:Word2vec
1568:fastText
1449:Stemming
1099:SIGIR'12
981:See also
960:sentence
819:sentence
795:sentence
750:sentence
735:sentence
1915:Related
1881:Chatbot
1739:WordNet
1719:DBpedia
1593:Seq2seq
1337:Parsing
1252:Trigram
1133:Bibcode
1002:MinHash
59:feature
1888:(c.f.
1546:models
1534:Neural
1247:Bigram
1242:n-gram
1163:291713
1161:
1151:
912:"
729:import
717:import
714:typing
705:import
669:tf–idf
1937:spaCy
1582:large
1573:GloVe
1159:S2CID
1123:arXiv
1045:(PDF)
1018:Notes
918:print
888:print
876:count
762:->
708:keras
1702:Data
1553:BERT
1149:ISBN
1067:Word
765:None
756:List
723:from
720:List
711:from
699:from
678:WEKA
430:BoW3
408:BoW1
304:BoW2
211:BoW1
38:and
26:The
1734:UBY
1141:doi
1104:ACM
1084:use
1075:doi
933:len
906:bow
864:bow
852:key
849:for
840:bow
741:def
651:and
32:bag
1954::
1157:.
1147:.
1139:.
1131:.
1102:.
1081:.
1071:10
1069:.
1047:.
900:\n
855:in
846:{}
780:()
556:};
394:};
301:};
80:.
69:.
50:.
1892:)
1615:,
1584:)
1580:(
1210:e
1203:t
1196:v
1165:.
1143::
1135::
1125::
1106:.
1077::
963:)
957:(
951:)
945:}
942:)
936:(
930:{
924:f
921:(
915:)
909:}
903:{
894:f
891:(
885:)
879:(
873:.
867:=
861::
843:=
834:.
828:=
822:)
816:(
810:.
804:=
798:)
792:(
786:.
774:=
768::
759:)
753::
747:(
738:=
616:2
613:W
610:o
607:B
601:1
598:W
595:o
592:B
589:=
586:3
583:W
580:o
577:B
553:1
550::
544:,
541:1
538::
532:,
529:1
526::
520:,
517:1
514::
508:,
505:2
502::
496:,
493:2
490::
484:,
481:2
478::
472:,
469:2
466::
460:,
457:3
454::
448:,
445:1
442::
436:{
433:=
391:1
388::
382:,
379:1
376::
370:,
367:1
364::
358:,
355:1
352::
346:,
343:1
340::
334:,
331:1
328::
322:,
319:1
316::
310:{
307:=
298:1
295::
289:,
286:1
283::
277:,
274:2
271::
265:,
262:1
259::
253:,
250:1
247::
241:,
238:2
235::
229:,
226:1
223::
217:{
214:=
190:,
184:,
178:,
172:,
166:,
160:,
151:,
145:,
139:,
133:,
127:,
121:,
115:,
109:,
23:.
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.