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摘要: 针对维吾尔语名词短语指代现象，提出了一种利用栈式自编码深度学习算法进行基于语义特征的指代消解方法.通过对维吾尔语名词短语指称性的研究，提取出利于消解任务的13项特征.为提高特征对文本语义的表达，在特征集中引入富含词汇语义及上下文位置关系的Word embedding.利用深度学习机制无监督的提取隐含的深层语义特征，训练Softmax分类器进而完成指代消解任务.该方法在维吾尔语指代消解任务中的准确率为74.5%，召回率为70.6%，F值为72.4%.实验结果证明，深度学习模型较浅层的支持向量机更合适于本文的指代消解任务，对Word embedding特征项的引入，有效地提高了指代消解模型的性能.
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关键词:
- 深度学习 /
- 栈式自编码神经网络 /
- 指代消解 /
- Word embedding /
- 维吾尔语
Abstract: Aimed at the reference phenomena of Uyghur noun phrases, a method using stacked autoencoder model to achieve coreference resolution based on semantic characteristics is presented. Through the study of noun phrases referentiality, we pick up beneficial 13 features for coreference resolution tasks. In order to improve the expression of features for semantic text, Word embedding is added into feature sets, which makes feature sets contain lexical semantic information and context positional relationship. A deep learning algorithm is proposed for unsupervised detection of implicit semantic information, and also introduced is a softmax classifier to decide whether the two markables actually corefer. Experiments show that precision rate, recall rate and F value of coreference resolution reach 74.5%, 70.6% and 72.4%, respectively, which demonstrates that the proposed method on coreference resolution of Uyghur noun phrase and introduction of Word embedding to feature sets are able to improve the performance of coreference resolution system.-
Key words:
- Deep learning /
- stacked autoencoder /
- coreference resolution /
- word embedding /
- Uyghur
1) 本文责任编委 张民 -
表 1 指示词库
Table 1 The demonstrative thesaurus
指人指物 指性质 指数量 指地点 
, 
$\cdots$ $\cdots$ $\cdots$ $\cdots$ 
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表 2 维吾尔语名词短语指代消解训练和测试样例
Table 2 Training or testing sample format for Uyghur noun phrases
先行语 照应语 样例值(13个特征值+ 50维先行语、照应语Word embedding) 是否指代 
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0 0.133, $-$0.053, 0.114, $\cdots$, $-$0.108
0.177, $-$0.008, 0.127, $\cdots$, $-$0.055是 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0 0.076, 0.099, 0.019, $\cdots$, $-$0.069
0.177, $-$0.008, 0.127, $\cdots$, $-$0.055否 
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0 0.060, $-$0.135, 0.277, $\cdots$, $-$0.042
0.177, $-$0.008, 0.127, $\cdots$, $-$0.055否
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表 3 SAE模型最优参数
Table 3 Optimal parameters of SAE
参数 $\rho$ $\beta$ $\lambda$ maxIter 值 0.1 3 3E$-$3 800
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表 4 基于SAE模型的有效性验证
Table 4 The validation of SAE effectiveness
模型 $P$ (%) $R$ (%) $F$ (%) SAE$^1$ 61.775 73.319 67.054 SAE$^2$ 66.064 71.256 68.562 SAE$^3$ 66.134 71.995 68.940 SAE$^4$ 68.695 71.743 70.186 SVM 66.727 70.115 68.379
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表 5 特征集对结果的影响
Table 5 The influence of introducing features sets
特征项 $P$ (%) $R$ (%) $F$ (%) AnProperNoun 46.079 0.856 1.681 CaProperNoun 66.159 0.713 1.411 AnDefiniteNP 75.897 1.102 2.172 CaDefiniteNP 59.932 4.579 8.508 AnDemonstrativeNP 65.432 8.409 14.903 CaDemonstrativeNP 57.092 9.112 15.716 AnPossessionNP 60.411 26.912 37.222 CaPossessionNP 44.439 38.403 41.201 AnPossessionNP 48.231 51.082 49.616 CaPossessionNP 45.831 70.334 55.498 PropertyFit 64.470 51.108 57.017 SinglePluralFit 58.631 80.205 67.742 FullMatch 68.695 71.743 70.186
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表 6 Word embedding的引入对实验的影响
Table 6 The influence of introducing word embedding
模型 $P$ (%) $R$ (%) $F$ (%) SAE$^1$ 60.915 74.569 67.054 SAE$^1$ + WE 64.382 70.103 67.121 SAE$^2$ 66.064 71.256 68.562 SAE$^2+$ WE 66.571 71.419 68.910 SAE$^3$ 66.134 71.995 68.940 SAE$^3$ + WE 68.215 72.375 70.233 SAE$^4$ 68.695 71.743 70.186 SAE$^4$+ WE 72.352 69.743 71.024
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表 7 Word embedding维度对实验的影响
Table 7 The influence of adjusting word embedding dimension
SAE$^4$ + WE SVM + WE $P$ (%) $R$ (%) $F$ (%) $P$ (%) $R$ (%) $F$ (%) 10 72.4 69.7 71.0 67.0 70.3 68.6 50 73.9 69.8 71.8 70.5 69.8 70.1 100 74.5 70.6 72.4 69.9 69.9 69.9 150 75.8 68.4 71.9 69.0 70.4 69.7 200 77.0 67.0 71.9 68.2 70.9 69.4
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