学习苗语、拉祜语和汉语中并列复合词的顺序和详细表达
Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese
摘要：
并列复合词（CC）和复杂表达（EE）是东亚和东南亚语言中常见的并列结构。Mortensen（2006）声称：（1）苗族、拉祜族和汉语中EE和CC的线性排序可以通过语音层次结构来预测;（2）这些语音层次结构缺乏明确的语音依据。这些主张是重要的，因为形态句法经常被视为与音系学的前馈关系，而音系学的概括通常被认为是语音上的“自然”。我们研究了CC和EE的排序是否可以凭经验学习，以及计算模型（分类器和序列标记模型）是否学习类似于Mortensen（2006）所提出的不自然的层次结构。我们发现，决策树和支持向量机学习预测的基础上的语音CC/EE的顺序，与DTs学习层次结构惊人地相似，莫滕森提出的。然而，我们也发现，一个神经序列标记模型是能够学习的顺序，详细的表达苗族非常有效，而不使用任何语音信息。我们认为，EE排序可以通过两个独立的路线：语音和词汇分布，提出了一个更细致入微的图片比以前的工作。[ISO 639-3：hmn，lhu，cmn]
英文摘要：
Coordinate compounds (CCs) and elaborate expressions (EEs) are coordinate constructions common in languages of East and Southeast Asia. Mortensen (2006) claims that (1) the linear ordering of EEs and CCs in Hmong, Lahu, and Chinese can be predicted via phonological hierarchies and (2) these phonological hierarchies lack a clear phonetic rationale. These claims are significant because morphosyntax has often been seen as in a feed-forward relationship with phonology, and phonological generalizations have often been assumed to be phonetically "natural". We investigate whether the ordering of CCs and EEs can be learned empirically and whether computational models (classifiers and sequence labeling models) learn unnatural hierarchies similar to those posited by Mortensen (2006). We find that decision trees and SVMs learn to predict the order of CCs/EEs on the basis of phonology, with DTs learning hierarchies strikingly similar to those proposed by Mortensen. However, we also find that a neural sequence labeling model is able to learn the ordering of elaborate expressions in Hmong very effectively without using any phonological information. We argue that EE ordering can be learned through two independent routes: phonology and lexical distribution, presenting a more nuanced picture than previous work. [ISO 639-3:hmn, lhu, cmn]
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