从认知与历史双视角,解析词法生成力的机制与差异。
Historical and psycholinguistic perspectives on morphological productivity: A sketch of an integrative approach
- 用判别词典模型分析芬兰语、马来语等词形变化的生成规律。
- 发现词素单位常与词嵌入中心点关联,体现形式与意义系统性。
- 揭示文学巨匠托马斯·曼输入远多于输出,新词生成率极低。
本研究从认知-计算与历时两个视角探讨词法生成力。在认知-计算层面,采用判别词典模型(Discriminative Lexicon Model, DLM)分析芬兰语名词变格、马来语派生及英语复合词中的模式生成能力。结果显示,当形式空间与语义空间存在系统性对应时,模型才能对未见词汇进行理解与生成;否则仅靠记忆无法泛化。DLM 显示词素类子词单位倾向于关联具有特定词素的词嵌入中心点。在历时层面,以作家托马斯·曼为例,分析其输入与输出中新颖派生词的数量。发现其输入中新词远多于输出,且使用某个后缀生成新词的可能性随已有派生词嵌入向量与中心点平均距离增大而降低。该研究揭示了个体语言使用中词法创新的局限性,也挑战了基于特定说话人嵌入表示稀有与新词的可行性。
原文摘要 · Abstract (English)
In this study, we approach morphological productivity from two perspectives: a cognitive-computational perspective, and a diachronic perspective zooming in on an actual speaker, Thomas Mann. For developing the first perspective, we make use of a cognitive computational model of the mental lexicon, the discriminative lexicon model. For computational mappings between form and meaning to be productive, in the sense that novel, previously unencountered words, can be understood and produced, there must be systematicities between the form space and the semantic space. If the relation between form and meaning would be truly arbitrary, a model could memorize form and meaning pairings, but there is no way in which the model would be able to generalize to novel test data. For Finnish nominal inflection, Malay derivation, and English compounding, we explore, using the Discriminative Lexicon Model as a computational tool, to trace differences in the degree to which inflectional and word formation patterns are productive. We show that the DLM tends to associate affix-like sublexical units with the centroids of the embeddings of the words with a given affix. For developing the second perspective, we study how the intake and output of one prolific writer, Thomas Mann, changes over time. We show by means of an examination of what Thomas Mann is likely to have read, and what he wrote, that the rate at which Mann produces novel derived words is extremely low. There are far more novel words in his input than in his output. We show that Thomas Mann is less likely to produce a novel derived word with a given suffix the greater the average distance is of the embeddings of all derived words to the corresponding centroid, and discuss the challenges of using speaker-specific embeddings for low-frequency and novel words.
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