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[求助]
一段話,求翻譯為英語
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目前,大多數(shù)監(jiān)督標(biāo)注方法在大規(guī)模語料環(huán)境中可以獲得較好的效果,但 標(biāo)注語料資源在實(shí)際應(yīng)用中既難以獲取,也難具有通用性。本文提出基于A方法的原型模式擴(kuò)展算法: 首先,使用初始小規(guī)模訓(xùn)練數(shù)據(jù)構(gòu)造一定準(zhǔn)確率的一體化標(biāo)注器。 其次,利用A 算法自動擴(kuò)展訓(xùn)練數(shù)據(jù)。從未標(biāo)記數(shù)據(jù)中預(yù)測出候選實(shí)例,把 數(shù)值大于某個域值的數(shù)據(jù)加入訓(xùn)練集。 最后,通過訓(xùn)練數(shù)據(jù)中存在的約束來對噪聲進(jìn)行剪輯。并使用擴(kuò)展后的訓(xùn)練 數(shù)據(jù)重新迭代訓(xùn)練分類器,直到最終趨于穩(wěn)定迭代終止。 |
木蟲 (小有名氣)
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At present, most of the supervision and labeling methods can achieve good effect in the large-scale corpus environment , but in a real world application, tagging corpus resources is not only difficult to obtain, hard also to be versatile. In this article, we present a prototype model extension algorithma based on A-method: First of all, using the original small-scale training data conducts integration annotators with a certain accuracy rate. Secondly, useing the A-algorithm expands the training data automatically. To predict the candidate example among untagged data, then the numerical data which is greater than a certain thresholdto should join in a training set . Finally, in line with the constraints existed in training data cutting the noise for clips. And using the training data after extension to afresh the training classifier iteratively, until approaching the final stable iteration. |

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