Combining Ontologies and Neural Networks for Analyzing Historical Language Varieties. A Case Study in Middle Low German
Combining Ontologies and Neural Networks for Analyzing Historical Language Varieties. A Case Study in Middle Low German
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结合本体论和神经网络来分析历史语言品种。
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发表时间：
2016
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通讯作者：
C. Chiarcos
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作者：
Maria Sukhareva;C. Chiarcos
本文以中古低地德语（MLG）为例，对历史语言变体的形态句法注释进行了实验。中古低地德语是中世纪德国汉萨人的官方语言，也是当时波罗的海地区的主要语言。据我们所知，这是自动为中古低地德语生成形态句法注释的第一个实验，因此，目前还没有就词性（POS）标记集达成一致。在我们的实验中，我们说明了如何使用基于本体的投影注释规范来规避这个问题：我们不是针对给定的标记集进行训练和评估，而是将其分解为由神经网络独立预测的独立特征。然后，利用本体的一致性约束（公理），将预测的特征概率解码为可靠的本体表示。使用这些表示，我们最终可以引导一个POS标记集，只捕获可以可靠预测的形态语法特征。通过这种方式，我们的方法能够通过引导标记集同时优化形态句法注释的精度和召回率，而不是执行迭代循环。
In this paper, we describe experiments on the morphosyntactic annotation of historical language varieties for the example of Middle Low German (MLG), the official language of the German Hanse during the Middle Ages and a dominant language around the Baltic Sea by the time. To our best knowledge, this is the first experiment in automatically producing morphosyntactic annotations for Middle Low German, and accordingly, no part-of-speech (POS) tagset is currently agreed upon. In our experiment, we illustrate how ontology-based specifications of projected annotations can be employed to circumvent this issue: Instead of training and evaluating against a given tagset, we decomponse it into independent features which are predicted independently by a neural network. Using consistency constraints (axioms) from an ontology, then, the predicted feature probabilities are decoded into a sound ontological representation. Using these representations, we can finally bootstrap a POS tagset capturing only morphosyntactic features which could be reliably predicted. In this way, our approach is capable to optimize precision and recall of morphosyntactic annotations simultaneously with bootstrapping a tagset rather than performing iterative cycles.