statistical language造句
例句與造句
- The maximum entropy principal proved to be a very useful method to create statistical language model
近幾年的自然語(yǔ)言處理研究表明，最大熵原理是建立自然語(yǔ)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(yǔ)言來(lái)說(shuō)就是隨著拋擲增加（假定拋幣條件不變） ，相對(duì)頻率越來(lái)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(yǔ)言知識(shí)進(jìn)行檢索,近年來(lái)基于統(tǒng)計(jì)語(yǔ)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(yǔ)言模型在中文自然語(yǔ)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(yǔ)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。 - It's difficult to find statistical language in a sentence. 用statistical language造句挺難的
- 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(wú)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(yǔ)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(yǔ)言模型對(duì)傳統(tǒng)的統(tǒng)計(jì)語(yǔ)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(yǔ)言中可以對(duì)應(yīng)多個(gè)詞匯,而查詢語(yǔ)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(yǔ)言模型時(shí)不只是根據(jù)查詢語(yǔ)句的詞匯,而是通過(guò)查詢語(yǔ)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(yǔ)口語(yǔ)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(yǔ)言模型，及由語(yǔ)言模型引導(dǎo)的詞樹(shù)viterbi - beam搜索，并且采用基于音節(jié)的填充模型、話語(yǔ)確認(rèn)和拒識(shí)等方法對(duì)集外詞、非語(yǔ)聲和噪音進(jìn)行處理。