language n. 1.語言;(某民族,某國(guó)的)國(guó)語;語調(diào),措詞。 2.(談話者或作者所使用的)言語,語風(fēng),文風(fēng),文體。 3.專門用語,術(shù)語。 4.(動(dòng)物的)叫聲;(動(dòng)作,手勢(shì)等所表示的)表意語。 5.【自動(dòng)化】機(jī)器代碼 ( = machine language )。 6.〔俚語〕粗話,罵人的話;壞話。 7.態(tài)度,立場(chǎng)。 8.〔古語〕民族;某國(guó)國(guó)民。 a common language 共同的語言。 a dead language 死語言。 a foreign language 外國(guó)語。 a living language 活語言。 long language (與符號(hào)語言相對(duì)的)通用語言。 oral [spoken] language 口語。 the Chinese language 漢語。 written language 書面語。 high language 夸張的言詞。 in his own language 按他自己的說法。 with a great command [an easy flow] of language 口若懸河。 legal language 法律用語。 medical language 醫(yī)學(xué)用語。 parliamentary language 議會(huì)辭令;有禮貌的話。 the language of diplomacy 外交辭令。 the language of the science 科學(xué)用語。 finger [gesture, sign] language 手勢(shì)語。 the language of flowers 花語〔如以 lily 象征純潔等〕。 the language of the eyes 目語,眉目傳情。 billing gate language = language of the fish-market 下流的粗話。 in strong language 用激烈的下流話。 use (bad [foul, warm]) language to sb. 謾罵某人。 in fourteen languages 〔美俚〕非常。 speak the same language 說共同的語言,信仰和觀點(diǎn)相同。 language arts (中小學(xué)的)語言藝術(shù)學(xué)科。
The maximum entropy principal proved to be a very useful method to create statistical language model 近幾年的自然語言處理研究表明,最大熵原理是建立自然語言統(tǒng)計(jì)模型的一個(gè)很有效的方法。
In statistical language , we would say that the relative frequency become stable as the number of tosses becomes large ( if we are tossing the coin under uniform conditions ) 用統(tǒng)計(jì)學(xué)的語言來說就是隨著拋擲增加(假定拋幣條件不變) ,相對(duì)頻率越來越穩(wěn)定。
To retrieve information with more knowledge of language itself , statistical languages model for information retrieval was proposed a few years ago and develops fast 為了利用語言知識(shí)進(jìn)行檢索,近年來基于統(tǒng)計(jì)語言模型( slm - based )的信息檢索得到了快速發(fā)展。
An object - oriented chinese statistical language modeling toolkit is presented . the original trigram model is improved to have more capabilities of long dependency 論文對(duì)原有trigram的hmm統(tǒng)計(jì)模型進(jìn)行改進(jìn),使其具有更多的長(zhǎng)距依存能力,促進(jìn)統(tǒng)計(jì)語言模型在中文自然語言處理領(lǐng)域的應(yīng)用。
Caption recognition feature extraction using wavelet transformation and the combination of statistical language model and hidden markov model methods finally achieved the identification of caption 基于統(tǒng)計(jì)機(jī)器學(xué)習(xí)的字幕識(shí)別提取小波變換的特征并使用隱馬爾可夫模型和統(tǒng)計(jì)語言模型的識(shí)別技術(shù)相結(jié)合的機(jī)器學(xué)習(xí)方法,實(shí)現(xiàn)字幕文字的識(shí)別。
Neural networks are used more frequently in lossy data coding than in general lossless data coding , because standard neural networks must be trained off - line and they are too slow to be practical . in this thesis , statistical language model based on maximum entropy and neural networks are discussed particularly . then , an arithmetic coding algorithm based on maximum entropy and neural networks are proposed in this thesis 傳統(tǒng)的人工神經(jīng)網(wǎng)絡(luò)數(shù)據(jù)編碼算法需要離線訓(xùn)練且編碼速度慢,因此通常多用于專用有損編碼領(lǐng)域如聲音、圖像編碼等,在無損數(shù)據(jù)編碼領(lǐng)域應(yīng)用較少,針對(duì)這種現(xiàn)狀,本文詳細(xì)地研究了最大熵統(tǒng)計(jì)語言模型和神經(jīng)網(wǎng)絡(luò)算法各自的特點(diǎn),在此基礎(chǔ)上提出了一種基于神經(jīng)網(wǎng)絡(luò)和最大熵原理的算術(shù)編碼方法,這是一種自適應(yīng)的可在線學(xué)習(xí)的算法,并具有精簡(jiǎn)的網(wǎng)絡(luò)結(jié)構(gòu)。
The section language model makes the improvement on the traditional statistical language model in two aspects : firstly , aimed at the situation that the conceptual base can possibly correspond to many words in language , but the words in query are merely its particular cases , this paper has introduced the correlation vocabulary table . it contains all the possible words that may correspond to each conceptual base . when constructing the language model , not only the query ’ s words are considered , but also all the words corresponding 段語言模型對(duì)傳統(tǒng)的統(tǒng)計(jì)語言模型在兩個(gè)方面進(jìn)行改進(jìn):首先針對(duì)概念基在語言中可以對(duì)應(yīng)多個(gè)詞匯,而查詢語句中的詞匯僅僅是其特例的情況,本文引入了相關(guān)詞表的概念,在相關(guān)詞表中維護(hù)了每個(gè)概念基對(duì)應(yīng)的所有可能的詞匯表示,在構(gòu)建語言模型時(shí)不只是根據(jù)查詢語句的詞匯,而是通過查詢語句中概念基的所有相關(guān)詞匯,這就有效的提高了檢索結(jié)果的召回率。
Owing to the peculiarity of mandarin spoken language , the recognizer adopts the sonic model based on hmm , the mixed statistical language model based on both phrases and phrase class . moreover , it makes use of word tree viterbi - beam searching guided by language model and the sylable - filling model . a method of speech identifying and denying is used in the processing of vocabulary out of collecting , non - speech and noises 針對(duì)漢語口語的特點(diǎn),識(shí)別器采用了基于hmm的聲學(xué)模型,基于詞和基于詞類的混合統(tǒng)計(jì)語言模型,及由語言模型引導(dǎo)的詞樹viterbi - beam搜索,并且采用基于音節(jié)的填充模型、話語確認(rèn)和拒識(shí)等方法對(duì)集外詞、非語聲和噪音進(jìn)行處理。