{"id":476417,"date":"2023-08-09T07:29:55","date_gmt":"2023-08-09T07:29:55","guid":{"rendered":""},"modified":"2023-09-05T11:12:43","modified_gmt":"2023-09-05T11:12:43","slug":"context-vectors","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/context-vectors\/","title":{"rendered":"Vect\u01a1 b\u1ed1i c\u1ea3nh"},"content":{"rendered":"<h2>Ngu\u1ed3n g\u1ed1c c\u1ee7a vect\u01a1 b\u1ed1i c\u1ea3nh<\/h2>\n<p>Kh\u00e1i ni\u1ec7m Vect\u01a1 b\u1ed1i c\u1ea3nh, th\u01b0\u1eddng \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 nh\u00fang t\u1eeb, b\u1eaft ngu\u1ed3n t\u1eeb l\u0129nh v\u1ef1c X\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean (NLP), m\u1ed9t nh\u00e1nh c\u1ee7a tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o x\u1eed l\u00fd s\u1ef1 t\u01b0\u01a1ng t\u00e1c gi\u1eefa m\u00e1y t\u00ednh v\u00e0 ng\u00f4n ng\u1eef c\u1ee7a con ng\u01b0\u1eddi.<\/p>\n<p>N\u1ec1n t\u1ea3ng c\u1ee7a Vect\u01a1 b\u1ed1i c\u1ea3nh \u0111\u01b0\u1ee3c \u0111\u1eb7t v\u00e0o cu\u1ed1i nh\u1eefng n\u0103m 1980 v\u00e0 \u0111\u1ea7u nh\u1eefng n\u0103m 1990 v\u1edbi s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef m\u1ea1ng th\u1ea7n kinh. Tuy nhi\u00ean, ph\u1ea3i \u0111\u1ebfn n\u0103m 2013, v\u1edbi s\u1ef1 gi\u1edbi thi\u1ec7u thu\u1eadt to\u00e1n Word2Vec c\u1ee7a c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u t\u1ea1i Google, kh\u00e1i ni\u1ec7m n\u00e0y m\u1edbi th\u1ef1c s\u1ef1 th\u00e0nh c\u00f4ng. Word2Vec \u0111\u00e3 tr\u00ecnh b\u00e0y m\u1ed9t ph\u01b0\u01a1ng ph\u00e1p hi\u1ec7u qu\u1ea3 v\u00e0 hi\u1ec7u qu\u1ea3 \u0111\u1ec3 t\u1ea1o ra c\u00e1c vect\u01a1 ng\u1eef c\u1ea3nh ch\u1ea5t l\u01b0\u1ee3ng cao nh\u1eb1m n\u1eafm b\u1eaft nhi\u1ec1u m\u1eabu ng\u00f4n ng\u1eef. K\u1ec3 t\u1eeb \u0111\u00f3, c\u00e1c m\u00f4 h\u00ecnh vect\u01a1 ng\u1eef c\u1ea3nh n\u00e2ng cao h\u01a1n, ch\u1eb3ng h\u1ea1n nh\u01b0 GloVe v\u00e0 FastText, \u0111\u00e3 \u0111\u01b0\u1ee3c ph\u00e1t tri\u1ec3n v\u00e0 vi\u1ec7c s\u1eed d\u1ee5ng vect\u01a1 ng\u1eef c\u1ea3nh \u0111\u00e3 tr\u1edf th\u00e0nh ti\u00eau chu\u1ea9n trong c\u00e1c h\u1ec7 th\u1ed1ng NLP hi\u1ec7n \u0111\u1ea1i.<\/p>\n<h2>Gi\u1ea3i m\u00e3 vect\u01a1 b\u1ed1i c\u1ea3nh<\/h2>\n<p>Vect\u01a1 ng\u1eef c\u1ea3nh l\u00e0 m\u1ed9t ki\u1ec3u bi\u1ec3u di\u1ec5n t\u1eeb cho ph\u00e9p c\u00e1c t\u1eeb c\u00f3 ngh\u0129a t\u01b0\u01a1ng t\u1ef1 c\u00f3 c\u00e1ch bi\u1ec3u di\u1ec5n t\u01b0\u01a1ng t\u1ef1. Ch\u00fang l\u00e0 m\u1ed9t c\u00e1ch bi\u1ec3u di\u1ec5n ph\u00e2n t\u00e1n cho v\u0103n b\u1ea3n, c\u00f3 l\u1ebd l\u00e0 m\u1ed9t trong nh\u1eefng b\u01b0\u1edbc \u0111\u1ed9t ph\u00e1 quan tr\u1ecdng mang l\u1ea1i hi\u1ec7u su\u1ea5t \u1ea5n t\u01b0\u1ee3ng c\u1ee7a c\u00e1c ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc s\u00e2u \u0111\u1ed1i v\u1edbi c\u00e1c v\u1ea5n \u0111\u1ec1 NLP \u0111\u1ea7y th\u00e1ch th\u1ee9c.<\/p>\n<p>C\u00e1c vect\u01a1 n\u00e0y n\u1eafm b\u1eaft b\u1ed1i c\u1ea3nh t\u1eeb c\u00e1c t\u00e0i li\u1ec7u v\u0103n b\u1ea3n trong \u0111\u00f3 c\u00e1c t\u1eeb xu\u1ea5t hi\u1ec7n. M\u1ed7i t\u1eeb \u0111\u01b0\u1ee3c bi\u1ec3u di\u1ec5n b\u1eb1ng m\u1ed9t vect\u01a1 trong kh\u00f4ng gian nhi\u1ec1u chi\u1ec1u (th\u01b0\u1eddng l\u00e0 v\u00e0i tr\u0103m chi\u1ec1u) sao cho vect\u01a1 n\u1eafm b\u1eaft \u0111\u01b0\u1ee3c m\u1ed1i quan h\u1ec7 ng\u1eef ngh\u0129a gi\u1eefa c\u00e1c t\u1eeb. Nh\u1eefng t\u1eeb gi\u1ed1ng nhau v\u1ec1 m\u1eb7t ng\u1eef ngh\u0129a s\u1ebd \u1edf g\u1ea7n nhau trong kh\u00f4ng gian n\u00e0y, trong khi nh\u1eefng t\u1eeb kh\u00e1c nhau th\u00ec c\u00e1ch xa nhau.<\/p>\n<h2>D\u01b0\u1edbi mui xe c\u1ee7a c\u00e1c vect\u01a1 b\u1ed1i c\u1ea3nh<\/h2>\n<p>Vect\u01a1 b\u1ed1i c\u1ea3nh ho\u1ea1t \u0111\u1ed9ng b\u1eb1ng c\u00e1ch hu\u1ea5n luy\u1ec7n m\u1ed9t m\u00f4 h\u00ecnh m\u1ea1ng th\u1ea7n kinh n\u00f4ng v\u1ec1 m\u1ed9t nhi\u1ec7m v\u1ee5 NLP \u201cgi\u1ea3\u201d, trong \u0111\u00f3 m\u1ee5c ti\u00eau th\u1ef1c s\u1ef1 l\u00e0 t\u00ecm hi\u1ec3u tr\u1ecdng s\u1ed1 c\u1ee7a l\u1edbp \u1ea9n. Nh\u1eefng tr\u1ecdng s\u1ed1 n\u00e0y l\u00e0 c\u00e1c vect\u01a1 t\u1eeb m\u00e0 ch\u00fang ta t\u00ecm ki\u1ebfm.<\/p>\n<p>V\u00ed d\u1ee5: trong Word2Vec, ng\u01b0\u1eddi ta c\u00f3 th\u1ec3 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh \u0111\u1ec3 d\u1ef1 \u0111o\u00e1n m\u1ed9t t\u1eeb d\u1ef1a tr\u00ean ng\u1eef c\u1ea3nh xung quanh n\u00f3 (T\u00fai t\u1eeb li\u00ean t\u1ee5c ho\u1eb7c CBOW) ho\u1eb7c d\u1ef1 \u0111o\u00e1n c\u00e1c t\u1eeb xung quanh cho m\u1ed9t t\u1eeb m\u1ee5c ti\u00eau (Skip-gram). Sau khi hu\u1ea5n luy\u1ec7n h\u00e0ng t\u1ef7 t\u1eeb, c\u00e1c tr\u1ecdng s\u1ed1 trong m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng l\u00e0m vect\u01a1 t\u1eeb.<\/p>\n<h2>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a vect\u01a1 b\u1ed1i c\u1ea3nh<\/h2>\n<ul>\n<li><strong>S\u1ef1 t\u01b0\u01a1ng \u0111\u1ed3ng v\u1ec1 ng\u1eef ngh\u0129a<\/strong>: Vect\u01a1 ng\u1eef c\u1ea3nh n\u1eafm b\u1eaft m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 s\u1ef1 gi\u1ed1ng nhau v\u1ec1 ng\u1eef ngh\u0129a gi\u1eefa c\u00e1c t\u1eeb v\u00e0 c\u1ee5m t\u1eeb. Nh\u1eefng t\u1eeb c\u00f3 ngh\u0129a g\u1ea7n nhau \u0111\u01b0\u1ee3c bi\u1ec3u di\u1ec5n b\u1eb1ng c\u00e1c vect\u01a1 g\u1ea7n nhau trong kh\u00f4ng gian vect\u01a1.<\/li>\n<li><strong>M\u1ed1i quan h\u1ec7 ng\u1eef ngh\u0129a tinh t\u1ebf<\/strong>: Vect\u01a1 ng\u1eef c\u1ea3nh c\u00f3 th\u1ec3 n\u1eafm b\u1eaft c\u00e1c m\u1ed1i quan h\u1ec7 ng\u1eef ngh\u0129a tinh t\u1ebf h\u01a1n, ch\u1eb3ng h\u1ea1n nh\u01b0 c\u00e1c m\u1ed1i quan h\u1ec7 t\u01b0\u01a1ng t\u1ef1 (v\u00ed d\u1ee5: \u201cvua\u201d l\u00e0 v\u1edbi \u201cn\u1eef ho\u00e0ng\u201d c\u0169ng nh\u01b0 \u201c\u0111\u00e0n \u00f4ng\u201d l\u00e0 v\u1edbi \u201cph\u1ee5 n\u1eef\u201d).<\/li>\n<li><strong>Gi\u1ea3m k\u00edch th\u01b0\u1edbc<\/strong>: Ch\u00fang cho ph\u00e9p gi\u1ea3m \u0111\u00e1ng k\u1ec3 k\u00edch th\u01b0\u1edbc (t\u1ee9c l\u00e0 bi\u1ec3u di\u1ec5n c\u00e1c t\u1eeb trong \u00edt k\u00edch th\u01b0\u1edbc h\u01a1n) trong khi v\u1eabn duy tr\u00ec nhi\u1ec1u th\u00f4ng tin ng\u00f4n ng\u1eef c\u00f3 li\u00ean quan.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i vect\u01a1 b\u1ed1i c\u1ea3nh<\/h2>\n<p>C\u00f3 m\u1ed9t s\u1ed1 lo\u1ea1i vect\u01a1 ng\u1eef c\u1ea3nh, trong \u0111\u00f3 ph\u1ed5 bi\u1ebfn nh\u1ea5t l\u00e0:<\/p>\n<ol>\n<li><strong>Word2Vec<\/strong>: \u0110\u01b0\u1ee3c ph\u00e1t tri\u1ec3n b\u1edfi Google, bao g\u1ed3m c\u00e1c m\u00f4 h\u00ecnh CBOW v\u00e0 Skip-gram. C\u00e1c vect\u01a1 Word2Vec c\u00f3 th\u1ec3 n\u1eafm b\u1eaft c\u1ea3 \u00fd ngh\u0129a ng\u1eef ngh\u0129a v\u00e0 c\u00fa ph\u00e1p.<\/li>\n<li><strong>GloVe (Vect\u01a1 to\u00e0n c\u1ea7u \u0111\u1ec3 bi\u1ec3u di\u1ec5n t\u1eeb)<\/strong>: \u0110\u01b0\u1ee3c ph\u00e1t tri\u1ec3n b\u1edfi Stanford, GloVe x\u00e2y d\u1ef1ng m\u1ed9t ma tr\u1eadn xu\u1ea5t hi\u1ec7n ng\u1eef c\u1ea3nh t\u1eeb r\u00f5 r\u00e0ng, sau \u0111\u00f3 ph\u00e2n t\u00edch n\u00f3 \u0111\u1ec3 t\u1ea1o ra c\u00e1c vect\u01a1 t\u1eeb.<\/li>\n<li><strong>v\u0103n b\u1ea3n nhanh<\/strong>: \u0110\u01b0\u1ee3c ph\u00e1t tri\u1ec3n b\u1edfi Facebook, t\u00ednh n\u0103ng n\u00e0y m\u1edf r\u1ed9ng Word2Vec b\u1eb1ng c\u00e1ch xem x\u00e9t th\u00f4ng tin t\u1eeb ph\u1ee5, c\u00f3 th\u1ec3 \u0111\u1eb7c bi\u1ec7t h\u1eefu \u00edch cho c\u00e1c ng\u00f4n ng\u1eef gi\u00e0u h\u00ecnh th\u00e1i ho\u1eb7c x\u1eed l\u00fd c\u00e1c t\u1eeb kh\u00f4ng c\u00f3 t\u1eeb v\u1ef1ng.<\/li>\n<\/ol>\n<table>\n<thead>\n<tr>\n<th style=\"text-align: center;\">Ng\u01b0\u1eddi m\u1eabu<\/th>\n<th style=\"text-align: center;\">CBOW<\/th>\n<th style=\"text-align: center;\">B\u1ecf qua gram<\/th>\n<th style=\"text-align: center;\">Th\u00f4ng tin t\u1eeb ph\u1ee5<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align: center;\">Word2Vec<\/td>\n<td style=\"text-align: center;\">\u0110\u00fang<\/td>\n<td style=\"text-align: center;\">\u0110\u00fang<\/td>\n<td style=\"text-align: center;\">KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">G\u0103ng tay<\/td>\n<td style=\"text-align: center;\">\u0110\u00fang<\/td>\n<td style=\"text-align: center;\">KH\u00d4NG<\/td>\n<td style=\"text-align: center;\">KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">v\u0103n b\u1ea3n nhanh<\/td>\n<td style=\"text-align: center;\">\u0110\u00fang<\/td>\n<td style=\"text-align: center;\">\u0110\u00fang<\/td>\n<td style=\"text-align: center;\">\u0110\u00fang<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u1ee8ng d\u1ee5ng, th\u00e1ch th\u1ee9c v\u00e0 gi\u1ea3i ph\u00e1p c\u1ee7a vect\u01a1 b\u1ed1i c\u1ea3nh<\/h2>\n<p>Vect\u01a1 b\u1ed1i c\u1ea3nh t\u00ecm th\u1ea5y c\u00e1c \u1ee9ng d\u1ee5ng trong nhi\u1ec1u t\u00e1c v\u1ee5 NLP, bao g\u1ed3m nh\u01b0ng kh\u00f4ng gi\u1edbi h\u1ea1n \u1edf ph\u00e2n t\u00edch t\u00ecnh c\u1ea3m, ph\u00e2n lo\u1ea1i v\u0103n b\u1ea3n, nh\u1eadn d\u1ea1ng th\u1ef1c th\u1ec3 \u0111\u01b0\u1ee3c \u0111\u1eb7t t\u00ean v\u00e0 d\u1ecbch m\u00e1y. Ch\u00fang gi\u00fap n\u1eafm b\u1eaft nh\u1eefng \u0111i\u1ec3m t\u01b0\u01a1ng \u0111\u1ed3ng v\u1ec1 b\u1ed1i c\u1ea3nh v\u00e0 ng\u1eef ngh\u0129a, \u0111i\u1ec1u n\u00e0y r\u1ea5t quan tr\u1ecdng \u0111\u1ec3 hi\u1ec3u ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean.<\/p>\n<p>Tuy nhi\u00ean, vect\u01a1 ng\u1eef c\u1ea3nh kh\u00f4ng ph\u1ea3i l\u00e0 kh\u00f4ng c\u00f3 th\u00e1ch th\u1ee9c. M\u1ed9t v\u1ea5n \u0111\u1ec1 l\u00e0 vi\u1ec7c x\u1eed l\u00fd nh\u1eefng t\u1eeb kh\u00f4ng c\u00f3 t\u1eeb v\u1ef1ng. M\u1ed9t s\u1ed1 m\u00f4 h\u00ecnh vect\u01a1 ng\u1eef c\u1ea3nh, nh\u01b0 Word2Vec v\u00e0 GloVe, kh\u00f4ng cung c\u1ea5p vect\u01a1 cho c\u00e1c t\u1eeb kh\u00f4ng c\u00f3 t\u1eeb v\u1ef1ng. FastText gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 n\u00e0y b\u1eb1ng c\u00e1ch xem x\u00e9t th\u00f4ng tin t\u1eeb ph\u1ee5.<\/p>\n<p>Ngo\u00e0i ra, vect\u01a1 ng\u1eef c\u1ea3nh y\u00eau c\u1ea7u ngu\u1ed3n l\u1ef1c t\u00ednh to\u00e1n \u0111\u00e1ng k\u1ec3 \u0111\u1ec3 hu\u1ea5n luy\u1ec7n tr\u00ean kh\u1ed1i v\u0103n b\u1ea3n l\u1edbn. C\u00e1c vect\u01a1 ng\u1eef c\u1ea3nh \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n tr\u01b0\u1edbc th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 ph\u00e1 v\u1ee1 \u0111i\u1ec1u n\u00e0y, c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c tinh ch\u1ec9nh cho nhi\u1ec7m v\u1ee5 c\u1ee5 th\u1ec3 n\u1ebfu c\u1ea7n thi\u1ebft.<\/p>\n<h2>So s\u00e1nh v\u1edbi c\u00e1c \u0111i\u1ec1u kho\u1ea3n t\u01b0\u01a1ng t\u1ef1<\/h2>\n<table>\n<thead>\n<tr>\n<th style=\"text-align: center;\">Thu\u1eadt ng\u1eef<\/th>\n<th style=\"text-align: center;\">S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<th style=\"text-align: center;\">So s\u00e1nh vect\u01a1 b\u1ed1i c\u1ea3nh<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align: center;\">M\u00e3 h\u00f3a m\u1ed9t l\u1ea7n n\u00f3ng<\/td>\n<td style=\"text-align: center;\">Bi\u1ec3u th\u1ecb m\u1ed7i t\u1eeb d\u01b0\u1edbi d\u1ea1ng m\u1ed9t vect\u01a1 nh\u1ecb ph\u00e2n trong t\u1eeb v\u1ef1ng.<\/td>\n<td style=\"text-align: center;\">C\u00e1c vect\u01a1 b\u1ed1i c\u1ea3nh d\u00e0y \u0111\u1eb7c v\u00e0 n\u1eafm b\u1eaft c\u00e1c m\u1ed1i quan h\u1ec7 ng\u1eef ngh\u0129a.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Vect\u01a1 TF-IDF<\/td>\n<td style=\"text-align: center;\">Bi\u1ec3u th\u1ecb c\u00e1c t\u1eeb d\u1ef1a tr\u00ean t\u1ea7n su\u1ea5t t\u00e0i li\u1ec7u v\u00e0 t\u1ea7n s\u1ed1 t\u00e0i li\u1ec7u ngh\u1ecbch \u0111\u1ea3o c\u1ee7a ch\u00fang.<\/td>\n<td style=\"text-align: center;\">C\u00e1c vect\u01a1 b\u1ed1i c\u1ea3nh n\u1eafm b\u1eaft c\u00e1c m\u1ed1i quan h\u1ec7 ng\u1eef ngh\u0129a, kh\u00f4ng ch\u1ec9 t\u1ea7n s\u1ed1.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/td>\n<td style=\"text-align: center;\">C\u00e1c m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u00ean kho v\u0103n b\u1ea3n l\u1edbn v\u00e0 \u0111\u01b0\u1ee3c tinh ch\u1ec9nh cho c\u00e1c nhi\u1ec7m v\u1ee5 c\u1ee5 th\u1ec3. V\u00ed d\u1ee5: BERT, GPT.<\/td>\n<td style=\"text-align: center;\">Nh\u1eefng m\u00f4 h\u00ecnh n\u00e0y s\u1eed d\u1ee5ng vect\u01a1 ng\u1eef c\u1ea3nh nh\u01b0 m\u1ed9t ph\u1ea7n ki\u1ebfn tr\u00fac c\u1ee7a ch\u00fang.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m t\u01b0\u01a1ng lai v\u1ec1 vect\u01a1 b\u1ed1i c\u1ea3nh<\/h2>\n<p>T\u01b0\u01a1ng lai c\u1ee7a vect\u01a1 ng\u1eef c\u1ea3nh c\u00f3 th\u1ec3 s\u1ebd g\u1eafn b\u00f3 ch\u1eb7t ch\u1ebd v\u1edbi s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a NLP v\u00e0 h\u1ecdc m\u00e1y. V\u1edbi nh\u1eefng ti\u1ebfn b\u1ed9 g\u1ea7n \u0111\u00e2y trong c\u00e1c m\u00f4 h\u00ecnh d\u1ef1a tr\u00ean bi\u1ebfn \u00e1p nh\u01b0 BERT v\u00e0 GPT, vect\u01a1 ng\u1eef c\u1ea3nh hi\u1ec7n \u0111\u01b0\u1ee3c t\u1ea1o \u0111\u1ed9ng d\u1ef1a tr\u00ean to\u00e0n b\u1ed9 ng\u1eef c\u1ea3nh c\u1ee7a c\u00e2u ch\u1ee9 kh\u00f4ng ch\u1ec9 ng\u1eef c\u1ea3nh c\u1ee5c b\u1ed9. Ch\u00fang t\u00f4i c\u00f3 th\u1ec3 d\u1ef1 \u0111o\u00e1n s\u1ef1 c\u1ea3i ti\u1ebfn h\u01a1n n\u1eefa c\u1ee7a c\u00e1c ph\u01b0\u01a1ng ph\u00e1p n\u00e0y, c\u00f3 kh\u1ea3 n\u0103ng k\u1ebft h\u1ee3p c\u00e1c vect\u01a1 ng\u1eef c\u1ea3nh t\u0129nh v\u00e0 \u0111\u1ed9ng \u0111\u1ec3 hi\u1ec3u ng\u00f4n ng\u1eef m\u1ea1nh m\u1ebd v\u00e0 nhi\u1ec1u s\u1eafc th\u00e1i h\u01a1n n\u1eefa.<\/p>\n<h2>Vect\u01a1 b\u1ed1i c\u1ea3nh v\u00e0 m\u00e1y ch\u1ee7 proxy<\/h2>\n<p>M\u1eb7c d\u00f9 c\u00f3 v\u1ebb kh\u00e1c nhau nh\u01b0ng vect\u01a1 ng\u1eef c\u1ea3nh v\u00e0 m\u00e1y ch\u1ee7 proxy th\u1ef1c s\u1ef1 c\u00f3 th\u1ec3 giao nhau. V\u00ed d\u1ee5: trong l\u0129nh v\u1ef1c qu\u00e9t web, m\u00e1y ch\u1ee7 proxy cho ph\u00e9p thu th\u1eadp d\u1eef li\u1ec7u \u1ea9n danh v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n. D\u1eef li\u1ec7u v\u0103n b\u1ea3n \u0111\u01b0\u1ee3c thu th\u1eadp sau \u0111\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 hu\u1ea5n luy\u1ec7n c\u00e1c m\u00f4 h\u00ecnh vect\u01a1 ng\u1eef c\u1ea3nh. Do \u0111\u00f3, c\u00e1c m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 gi\u00e1n ti\u1ebfp h\u1ed7 tr\u1ee3 vi\u1ec7c t\u1ea1o v\u00e0 s\u1eed d\u1ee5ng vect\u01a1 ng\u1eef c\u1ea3nh b\u1eb1ng c\u00e1ch t\u1ea1o \u0111i\u1ec1u ki\u1ec7n thu\u1eadn l\u1ee3i cho vi\u1ec7c thu th\u1eadp kh\u1ed1i l\u01b0\u1ee3ng l\u1edbn v\u0103n b\u1ea3n.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ol>\n<li><a href=\"https:\/\/arxiv.org\/pdf\/1301.3781.pdf\" target=\"_new\" rel=\"noopener nofollow\">Gi\u1ea5y Word2Vec<\/a><\/li>\n<li><a href=\"https:\/\/nlp.stanford.edu\/pubs\/glove.pdf\" target=\"_new\" rel=\"noopener nofollow\">Gi\u1ea5y GloVe<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/pdf\/1607.04606.pdf\" target=\"_new\" rel=\"noopener nofollow\">Gi\u1ea5y v\u0103n b\u1ea3n nhanh<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/pdf\/1810.04805.pdf\" target=\"_new\" rel=\"noopener nofollow\">Gi\u1ea5y BERT<\/a><\/li>\n<li><a href=\"https:\/\/cdn.openai.com\/better-language-models\/language_models_are_unsupervised_multitask_learners.pdf\" target=\"_new\" rel=\"noopener nofollow\">Gi\u1ea5y GPT<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468002,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476417","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Context Vectors: Bridging the Gap Between Words and Meanings<\/mark>","faq_items":[{"question":"What are Context Vectors?","answer":"<p>Context Vectors, also known as word embeddings, are a type of word representation that allows words with similar meaning to have a similar representation. They capture context from the text documents in which the words appear, placing words that are semantically similar close together in a high-dimensional vector space.<\/p>"},{"question":"Where did the concept of Context Vectors originate?","answer":"<p>The concept of Context Vectors originated from the field of Natural Language Processing (NLP), a branch of artificial intelligence. The foundations were laid in the late 1980s and early 1990s with the development of neural network language models. However, it was the introduction of the Word2Vec algorithm by Google in 2013 that propelled the use of context vectors in modern NLP systems.<\/p>"},{"question":"How do Context Vectors work?","answer":"<p>Context Vectors work by training a shallow neural network model on a \"fake\" NLP task, where the real goal is to learn the weights of the hidden layer, which then become the word vectors. For instance, the model may be trained to predict a word given its surrounding context or predict surrounding words given a target word.<\/p>"},{"question":"What are some key features of Context Vectors?","answer":"<p>Context vectors capture the semantic similarity between words and phrases, such that words with similar meanings have similar representations. They also capture more subtle semantic relationships like analogies. Additionally, context vectors allow for significant dimensionality reduction while maintaining relevant linguistic information.<\/p>"},{"question":"What types of Context Vectors exist?","answer":"<p>The most popular types of context vectors are Word2Vec developed by Google, GloVe (Global Vectors for Word Representation) developed by Stanford, and FastText developed by Facebook. Each of these models has its unique capabilities and features.<\/p>"},{"question":"What are some applications of Context Vectors?","answer":"<p>Context vectors are used in numerous Natural Language Processing tasks, including sentiment analysis, text classification, named entity recognition, and machine translation. They help capture context and semantic similarities which are crucial for understanding natural language.<\/p>"},{"question":"How are Context Vectors related to proxy servers?","answer":"<p>In the realm of web scraping, proxy servers allow for more efficient and anonymous data collection. The collected textual data can be used to train context vector models. Thus, proxy servers can indirectly support the creation and usage of context vectors by facilitating the gathering of large text corpora.<\/p>"},{"question":"What is the future perspective of Context Vectors?","answer":"<p>The future of context vectors is likely to be closely intertwined with the evolution of NLP and machine learning. With advancements in transformer-based models like BERT and GPT, context vectors are now generated dynamically based on the entire context of a sentence, not just local context. This could further enhance the effectiveness and robustness of context vectors.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/476417","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/476417\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468002"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=476417"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}