J Biomed Inform - Disambiguation of ambiguous biomedical terms using examples generated from the UMLS Metathesaurus.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ learn(2355) train(1041) set(1003) }
{ framework(1458) process(801) describ(734) }
{ model(3404) distribut(989) bayesian(671) }
{ problem(2511) optim(1539) algorithm(950) }
{ system(1050) medic(1026) inform(1018) }
{ system(1976) rule(880) can(841) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ case(1353) use(1143) diagnosi(1136) }
{ group(2977) signific(1463) compar(1072) }
{ first(2504) two(1366) second(1323) }
{ result(1111) use(1088) new(759) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ can(774) often(719) complex(702) }
{ concept(1167) ontolog(924) domain(897) }
{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
{ import(1318) role(1303) understand(862) }
{ data(3008) multipl(1320) sourc(1022) }
{ health(1844) social(1437) communiti(874) }
{ implement(1333) system(1263) develop(1122) }
{ imag(1947) propos(1133) code(1026) }
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{ care(1570) inform(1187) nurs(1089) }
{ featur(1941) imag(1645) propos(1176) }
{ data(2317) use(1299) case(1017) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
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{ take(945) account(800) differ(722) }
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{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
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{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
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{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
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{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
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{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }

Resumo

Researchers have access to a vast amount of information stored in textual documents and there is a pressing need for the development of automated methods to enable and improve access to this resource. Lexical ambiguity, the phenomena in which a word or phrase has more than one possible meaning, presents a significant obstacle to automated text processing. Word Sense Disambiguation (WSD) is a technology that resolves these ambiguities automatically and is an important stage in text understanding. The most accurate approaches to WSD rely on manually labeled examples but this is usually not available and is prohibitively expensive to create. This paper offers a solution to that problem by using information in the UMLS Metathesaurus to automatically generate labeled examples. Two approaches are presented. The first is an extension of existing work (Liu et al., 2002 [1]) and the second a novel approach that exploits information in the UMLS that has not been used for this purpose. The automatically generated examples are evaluated by comparing them against the manually labeled ones in the NLM-WSD data set and are found to outperform the baseline. The examples generated using the novel approach produce an improvement in WSD performance when combined with manually labeled examples.

Resumo Limpo

research access vast amount inform store textual document press need develop autom method enabl improv access resourc lexic ambigu phenomena word phrase one possibl mean present signific obstacl autom text process word sens disambigu wsd technolog resolv ambigu automat import stage text understand accur approach wsd reli manual label exampl usual avail prohibit expens creat paper offer solut problem use inform uml metathesaurus automat generat label exampl two approach present first extens exist work liu et al second novel approach exploit inform uml use purpos automat generat exampl evalu compar manual label one nlmwsd data set found outperform baselin exampl generat use novel approach produc improv wsd perform combin manual label exampl

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