{"id":478206,"date":"2023-08-09T09:28:58","date_gmt":"2023-08-09T09:28:58","guid":{"rendered":""},"modified":"2023-09-05T11:16:18","modified_gmt":"2023-09-05T11:16:18","slug":"n-grams","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/n-grams\/","title":{"rendered":"N-gram"},"content":{"rendered":"<p>Th\u00f4ng tin t\u00f3m t\u1eaft v\u1ec1 N-gram<\/p>\n<p>N-gram l\u00e0 c\u00e1c chu\u1ed7i li\u1ec1n k\u1ec1 c\u1ee7a &#039;n&#039; m\u1ee5c t\u1eeb m\u1ed9t m\u1eabu v\u0103n b\u1ea3n ho\u1eb7c l\u1eddi n\u00f3i nh\u1ea5t \u0111\u1ecbnh. Ch\u00fang \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i trong x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean (NLP), m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef th\u1ed1ng k\u00ea v\u00e0 nh\u1eadn d\u1ea1ng m\u1eabu. N-gram c\u00f3 k\u00edch th\u01b0\u1edbc 1 \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 \u201cunigram\u201d, k\u00edch th\u01b0\u1edbc 2 l\u00e0 \u201cbigram\u201d, k\u00edch th\u01b0\u1edbc 3 l\u00e0 \u201ctrigram\u201d, v.v.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a N-gram v\u00e0 s\u1ef1 \u0111\u1ec1 c\u1eadp \u0111\u1ea7u ti\u00ean v\u1ec1 n\u00f3<\/h2>\n<p>N-gram \u0111\u01b0\u1ee3c nh\u00e0 to\u00e1n h\u1ecdc v\u00e0 nh\u00e0 ph\u00e2n t\u00edch m\u1eadt m\u00e3 Warren Weaver c\u1ee7a Harvard gi\u1edbi thi\u1ec7u v\u00e0o n\u0103m 1949 nh\u01b0 m\u1ed9t ph\u1ea7n c\u00f4ng vi\u1ec7c c\u1ee7a \u00f4ng v\u1ec1 d\u1ecbch m\u00e1y th\u1ed1ng k\u00ea. Kh\u00e1i ni\u1ec7m n\u00e0y sau \u0111\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c ch\u00ednh th\u1ee9c h\u00f3a v\u00e0 tr\u1edf th\u00e0nh trung t\u00e2m c\u1ee7a nhi\u1ec1u l\u0129nh v\u1ef1c ng\u00f4n ng\u1eef h\u1ecdc t\u00ednh to\u00e1n v\u00e0 nh\u1eadn d\u1ea1ng m\u1eabu.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 N-gram: M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1<\/h2>\n<p>N-gram \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong nhi\u1ec1u l\u0129nh v\u1ef1c t\u00ednh to\u00e1n kh\u00e1c nhau, ch\u1ee7 y\u1ebfu \u0111\u1ec3 m\u00f4 h\u00ecnh h\u00f3a ng\u00f4n ng\u1eef v\u00e0 x\u1eed l\u00fd v\u0103n b\u1ea3n. Ch\u00fang \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 d\u1ef1 \u0111o\u00e1n s\u1ef1 xu\u1ea5t hi\u1ec7n c\u1ee7a m\u1ed9t t\u1eeb d\u1ef1a tr\u00ean c\u00e1c t\u1eeb tr\u01b0\u1edbc \u0111\u00f3 trong m\u1ed9t chu\u1ed7i, t\u1ea1o \u0111i\u1ec1u ki\u1ec7n thu\u1eadn l\u1ee3i cho c\u00e1c \u1ee9ng d\u1ee5ng nh\u01b0 ho\u00e0n th\u00e0nh v\u0103n b\u1ea3n, nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i v\u00e0 d\u1ecbch thu\u1eadt.<\/p>\n<h3>M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef<\/h3>\n<p>N-gram \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u00ednh x\u00e1c su\u1ea5t c\u1ee7a m\u1ed9t chu\u1ed7i t\u1eeb, gi\u00fap x\u00e2y d\u1ef1ng c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef th\u1ed1ng k\u00ea. B\u1eb1ng c\u00e1ch ki\u1ec3m tra t\u1ea7n su\u1ea5t v\u00e0 kh\u1ea3 n\u0103ng x\u1ea3y ra c\u1ee7a chu\u1ed7i t\u1eeb, c\u00e1c m\u00f4 h\u00ecnh n\u00e0y h\u1ed7 tr\u1ee3 c\u00e1c \u1ee9ng d\u1ee5ng nh\u01b0 nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i v\u00e0 d\u1ecbch m\u00e1y.<\/p>\n<h3>X\u1eed l\u00fd v\u0103n b\u1ea3n<\/h3>\n<p>Trong x\u1eed l\u00fd v\u0103n b\u1ea3n, N-gram cung c\u1ea5p c\u00e1c m\u1eabu ng\u1eef c\u1ea3nh v\u00e0 s\u1ef1 xu\u1ea5t hi\u1ec7n, h\u1ed7 tr\u1ee3 ph\u00e2n t\u00edch c\u1ea3m x\u00fac, l\u1ecdc th\u01b0 r\u00e1c v\u00e0 t\u1ed1i \u01b0u h\u00f3a t\u00ecm ki\u1ebfm.<\/p>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a N-gram: N-gram ho\u1ea1t \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o<\/h2>\n<p>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a N-gram bao g\u1ed3m m\u1ed9t chu\u1ed7i c\u00e1c t\u1eeb ho\u1eb7c k\u00fd hi\u1ec7u &#039;n&#039;. V\u00ed d\u1ee5, b\u00e1t qu\u00e1i (3 gam) \u201cT\u00f4i y\u00eau c\u00e0 ph\u00ea\u201d g\u1ed3m ba t\u1eeb li\u00ean ti\u1ebfp. X\u00e1c su\u1ea5t c\u1ee7a m\u1ed7i N-gram c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c t\u00ednh b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng s\u1ed1 t\u1ea7n s\u1ed1 v\u00e0 \u01b0\u1edbc t\u00ednh kh\u1ea3 n\u0103ng t\u1ed1i \u0111a.<\/p>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a N-gram<\/h2>\n<ul>\n<li><strong>S\u1ef1 \u0111\u01a1n gi\u1ea3n:<\/strong> D\u1ec5 d\u00e0ng t\u00ednh to\u00e1n v\u00e0 hi\u1ec3u.<\/li>\n<li><strong>Kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng:<\/strong> C\u00f3 th\u1ec3 m\u1edf r\u1ed9ng \u0111\u1ebfn b\u1ea5t k\u1ef3 gi\u00e1 tr\u1ecb &#039;n&#039; n\u00e0o.<\/li>\n<li><strong>\u0110\u1ed9 nh\u1ea1y ng\u1eef c\u1ea3nh:<\/strong> Gi\u00e1 tr\u1ecb &#039;n&#039; cao h\u01a1n cung c\u1ea5p nhi\u1ec1u ng\u1eef c\u1ea3nh h\u01a1n nh\u01b0ng c\u00f3 th\u1ec3 d\u1eabn \u0111\u1ebfn c\u00e1c v\u1ea5n \u0111\u1ec1 v\u1ec1 \u0111\u1ed9 th\u01b0a th\u1edbt.<\/li>\n<li><strong>T\u00ednh linh ho\u1ea1t:<\/strong> \u0110\u01b0\u1ee3c s\u1eed d\u1ee5ng tr\u00ean nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau nh\u01b0 x\u1eed l\u00fd ng\u00f4n ng\u1eef, tin sinh h\u1ecdc, v.v.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i N-gram: Danh m\u1ee5c v\u00e0 v\u00ed d\u1ee5<\/h2>\n<table>\n<thead>\n<tr>\n<th>Ki\u1ec3u<\/th>\n<th>V\u00ed d\u1ee5<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Unigram<\/td>\n<td>(T\u00f4i th\u00edch c\u00e0 ph\u00ea)<\/td>\n<\/tr>\n<tr>\n<td>Bigram<\/td>\n<td>(T\u00f4i, t\u00ecnh y\u00eau), (t\u00ecnh y\u00eau, c\u00e0 ph\u00ea)<\/td>\n<\/tr>\n<tr>\n<td>B\u00e1t qu\u00e1i<\/td>\n<td>(T\u00f4i th\u00edch c\u00e0 ph\u00ea)<\/td>\n<\/tr>\n<tr>\n<td>4 gam<\/td>\n<td>(T\u00f4i, t\u00ecnh y\u00eau, m\u00e0u \u0111en, c\u00e0 ph\u00ea)<\/td>\n<\/tr>\n<tr>\n<td>\u2026<\/td>\n<td>\u2026<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng N-gram, v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p<\/h2>\n<h3>C\u00e1ch s\u1eed d\u1ee5ng:<\/h3>\n<ul>\n<li>Ph\u00e2n lo\u1ea1i v\u0103n b\u1ea3n<\/li>\n<li>Ph\u00e2n t\u00edch t\u00ecnh c\u1ea3m<\/li>\n<li>Nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i<\/li>\n<li>D\u1ecbch m\u00e1y<\/li>\n<\/ul>\n<h3>C\u00e1c v\u1ea5n \u0111\u1ec1:<\/h3>\n<ul>\n<li><strong>\u0110\u1ed9 th\u01b0a th\u1edbt d\u1eef li\u1ec7u:<\/strong> N-gram hi\u1ebfm c\u00f3 th\u1ec3 d\u1eabn \u0111\u1ebfn c\u00e1c v\u1ea5n \u0111\u1ec1 t\u00ednh to\u00e1n.<\/li>\n<li><strong>Chi ph\u00ed t\u00ednh to\u00e1n:<\/strong> Gi\u00e1 tr\u1ecb &#039;n&#039; cao h\u01a1n c\u00f3 th\u1ec3 l\u00e0m t\u0103ng \u0111\u1ed9 ph\u1ee9c t\u1ea1p.<\/li>\n<\/ul>\n<h3>C\u00e1c gi\u1ea3i ph\u00e1p:<\/h3>\n<ul>\n<li><strong>K\u1ef9 thu\u1eadt l\u00e0m m\u1ecbn:<\/strong> \u0110\u1ec3 x\u1eed l\u00fd s\u1ef1 th\u01b0a th\u1edbt d\u1eef li\u1ec7u.<\/li>\n<li><strong>Gi\u1edbi h\u1ea1n &#039;n&#039;:<\/strong> \u0110\u1ec3 qu\u1ea3n l\u00fd chi ph\u00ed t\u00ednh to\u00e1n.<\/li>\n<\/ul>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 so s\u00e1nh v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1<\/h2>\n<table>\n<thead>\n<tr>\n<th>T\u00ednh n\u0103ng<\/th>\n<th>N-gram<\/th>\n<th>X\u00edch Markov<\/th>\n<th>T\u00fai T\u1eeb<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>B\u1ed1i c\u1ea3nh<\/td>\n<td>\u0110\u00fang<\/td>\n<td>Gi\u1edbi h\u1ea1n<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1eb7t h\u00e0ng<\/td>\n<td>\u0110\u00fang<\/td>\n<td>\u0110\u00fang<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td>t\u00ednh to\u00e1n<\/td>\n<td>V\u1eeba ph\u1ea3i<\/td>\n<td>Th\u1ea5p<\/td>\n<td>Th\u1ea5p<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn N-gram<\/h2>\n<p>N-gram ti\u1ebfp t\u1ee5c ph\u00e1t tri\u1ec3n v\u1edbi c\u00e1c \u1ee9ng d\u1ee5ng trong c\u00e1c l\u0129nh v\u1ef1c m\u1edbi n\u1ed5i nh\u01b0 h\u1ecdc s\u00e2u v\u00e0 m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh. Nghi\u00ean c\u1ee9u v\u1ec1 N-gram nhi\u1ec1u chi\u1ec1u h\u01a1n v\u00e0 t\u00edch h\u1ee3p v\u1edbi c\u00e1c m\u00f4 h\u00ecnh kh\u00e1c h\u1ee9a h\u1eb9n s\u1ebd \u0111\u01b0a ra nh\u1eefng d\u1ef1 \u0111o\u00e1n ch\u00ednh x\u00e1c h\u01a1n v\u00e0 ph\u00f9 h\u1ee3p v\u1edbi ng\u1eef c\u1ea3nh h\u01a1n.<\/p>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi N-gram<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy, gi\u1ed1ng nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p, c\u00f3 th\u1ec3 t\u1ea1o \u0111i\u1ec1u ki\u1ec7n thu\u1eadn l\u1ee3i cho vi\u1ec7c thu th\u1eadp v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u quy m\u00f4 l\u1edbn \u0111\u1ec3 l\u1eadp m\u00f4 h\u00ecnh N-gram. B\u1eb1ng c\u00e1ch che gi\u1ea5u \u0111\u1ecba ch\u1ec9 IP v\u00e0 \u0111\u1ea3m b\u1ea3o t\u00ednh \u1ea9n danh, m\u00e1y ch\u1ee7 proxy cho ph\u00e9p thu th\u1eadp d\u1eef li\u1ec7u v\u0103n b\u1ea3n tr\u00ean web m\u1ed9t c\u00e1ch h\u1ee3p ph\u00e1p. D\u1eef li\u1ec7u n\u00e0y c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c x\u1eed l\u00fd b\u1eb1ng m\u00f4 h\u00ecnh N-gram \u0111\u1ec3 bi\u1ebft th\u00f4ng tin chi ti\u1ebft v\u00e0 xu h\u01b0\u1edbng.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/N-gram\" target=\"_new\" rel=\"noopener nofollow\">N-gram tr\u00ean Wikipedia<\/a><\/li>\n<li><a href=\"https:\/\/nlp.stanford.edu\" target=\"_new\" rel=\"noopener nofollow\">Nh\u00f3m NLP Stanford: N-gram<\/a><\/li>\n<li><a href=\"https:\/\/books.google.com\/ngrams\" target=\"_new\" rel=\"noopener nofollow\">Tr\u00ecnh xem N-gram c\u1ee7a Google<\/a><\/li>\n<\/ul>\n<hr>\n<p><strong>Tuy\u00ean b\u1ed1 t\u1eeb ch\u1ed1i tr\u00e1ch nhi\u1ec7m:<\/strong> B\u00e0i vi\u1ebft n\u00e0y l\u00e0 d\u00e0nh cho m\u1ee5c \u0111\u00edch gi\u00e1o d\u1ee5c. OneProxy kh\u00f4ng qu\u1ea3ng b\u00e1 ho\u1eb7c x\u00e1c nh\u1eadn b\u1ea5t k\u1ef3 ho\u1ea1t \u0111\u1ed9ng phi \u0111\u1ea1o \u0111\u1ee9c ho\u1eb7c b\u1ea5t h\u1ee3p ph\u00e1p n\u00e0o li\u00ean quan \u0111\u1ebfn N-gram ho\u1eb7c m\u00e1y ch\u1ee7 proxy. Lu\u00f4n tu\u00e2n th\u1ee7 lu\u1eadt ph\u00e1p hi\u1ec7n h\u00e0nh v\u00e0 \u0111i\u1ec1u kho\u1ea3n d\u1ecbch v\u1ee5 c\u1ee7a trang web.<\/p>","protected":false},"featured_media":469007,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478206","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>N-grams: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What are N-grams?","answer":"<p>N-grams are contiguous sequences of 'n' items from a sample of text or speech. They are used in various applications like natural language processing, statistical language modeling, and pattern recognition. Depending on the size, they can be referred to as unigrams, bigrams, trigrams, etc.<\/p>"},{"question":"Who introduced the concept of N-grams?","answer":"<p>The concept of N-grams was introduced by the Harvard mathematician and cryptanalyst Warren Weaver in 1949. It was part of his work in statistical machine translation.<\/p>"},{"question":"How do N-grams work in language modeling?","answer":"<p>N-grams work by calculating the probability of a word sequence in a given text. They are used to predict the occurrence of a word based on preceding words in a sequence, facilitating applications like text completion, speech recognition, and machine translation.<\/p>"},{"question":"What are the key features of N-grams?","answer":"<p>The key features of N-grams include simplicity, scalability, context sensitivity, and versatility. They are easy to compute, can be expanded to any 'n' value, provide context through higher 'n' values, and are used across various domains.<\/p>"},{"question":"What are some common types of N-grams?","answer":"<p>Common types of N-grams include unigrams, bigrams, trigrams, and higher-order N-grams. Unigrams consist of one word, bigrams consist of two consecutive words, trigrams consist of three, and so on.<\/p>"},{"question":"What problems might be encountered with N-grams and how can they be solved?","answer":"<p>Problems with N-grams might include data sparsity and computational cost. Solutions include using smoothing techniques to handle sparsity and limiting the 'n' value to manage computational costs.<\/p>"},{"question":"How are proxy servers like OneProxy related to N-grams?","answer":"<p>Proxy servers like OneProxy can facilitate the collection and analysis of large-scale data for N-gram modeling. They enable lawful web scraping of text data, which can be processed using N-gram models for various insights.<\/p>"},{"question":"What are the future perspectives and technologies related to N-grams?","answer":"<p>The future of N-grams includes applications in emerging fields like deep learning and neural networks. Research into higher-dimensional N-grams and integration with other models promises more precise and context-aware predictions.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/478206","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\/478206\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/469007"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=478206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}