【摘要】：源領(lǐng)域和目標(biāo)領(lǐng)域特征空間的不一致導(dǎo)致遷移學(xué)習(xí)準(zhǔn)確率下降。為此,提出一種基于Word2Vec的不同領(lǐng)域特征對(duì)齊算法。只選取形容詞、副詞、名詞、動(dòng)詞作為特征,針對(duì)每種詞性,選擇源領(lǐng)域和目標(biāo)領(lǐng)域的樞紐特征,分別在源領(lǐng)域和目標(biāo)領(lǐng)域?yàn)樵摌屑~特征計(jì)算出與之語(yǔ)義相似度最大的非樞紐特征,將其作為相似樞紐特征,從而為每個(gè)樞紐特征構(gòu)成一個(gè)相似樞紐特征對(duì)。將出現(xiàn)在這些領(lǐng)域中的每一個(gè)相似樞紐特征按照樞紐特征對(duì)進(jìn)行特征替換,從而將不同領(lǐng)域語(yǔ)義相似的特征進(jìn)行對(duì)齊,并在特征替換后的源領(lǐng)域和目標(biāo)領(lǐng)域數(shù)據(jù)上進(jìn)行機(jī)器學(xué)習(xí)。實(shí)驗(yàn)結(jié)果表明,該算法的平均分類(lèi)精度達(dá)到88.2%,高于Baseline算法。
[Abstract]:The inconsistency of feature space between source domain and target domain leads to the reduction of migration learning accuracy. For this reason, a new feature alignment algorithm for different domains based on Word2Vec is proposed. Only adjectives, adverbs, nouns and verbs are selected as the characteristics, and for each part of speech, the pivotal features of the source and target fields are selected. The non-hub features with the greatest semantic similarity are calculated in the source domain and the target domain, respectively, which are regarded as similar hub features, so as to form a similar hub feature pair for each hub feature. Each similar hub feature that appears in these domains is replaced by a feature pair of hub features to align the semantic similarity features of different domains, Machine learning is carried out on the source and target domain data after feature replacement. The experimental results show that the average classification accuracy of this algorithm is 88. 2%, which is higher than that of Baseline algorithm.
【作者單位】： 大連理工大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;大連外國(guó)語(yǔ)大學(xué)軟件學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61572102,61562080) 大連外國(guó)語(yǔ)大學(xué)科研基金(2014XJQN14)
【分類(lèi)號(hào)】：TP391.1

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