Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality
Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality
复制标题
优化用于预测德语助词动词组合性的分布式语义模型
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Sabine Schulte im Walde
中科院分区：
文献类型：
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作者：
Stefan Bott;Sabine Schulte im Walde
在这里提出的工作中，我们用一个仅依赖于词窗口信息而无法访问句法信息的分布式语义模型来评估德语粒子动词的组合程度。我们的方法仅将粒子动词与其基本动词之间的词法分布距离作为组合性的预测因子。我们发现，分布相似度的排序与人类对一系列粒子动词及其衍生基动词的语义组合性判断的排序有显著的相关性。我们还研究了进一步的语言因素的影响，如动词的歧义和总体频率，以及动词和助词在句法上的分离出现，这些都给助词的正确词形化带来了困难。我们分析了这些因素在多大程度上可能影响到粒子动词组合性预测的成功。
In the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no access to syntactic information as such. Our method only takes the lexical distributional distance between the Particle Verb to its Base Verb as a predictor for compositionality. We show that the ranking of distributional similarity correlates significantly with the ranking of human judgements on semantic compositionality for a series of Particle Verbs and the Base Verbs they are derived from. We also investigate the influence of further linguistic factors, such as the ambiguity and the overall frequency of the verbs and a syntactically separate occurrences of verbs and particles that causes difficulties for the correct lemmatization of Particle Verbs. We analyse in how far these factors may influence the success with which the compositionality of the Particle Verbs may be predicted.