英语介词短语的论元性预测
Predicting the Argumenthood of English Prepositional Phrases
摘要：
动词的论元与非论元的区分是一个由来已久的问题。在自然语言处理中，论证信息在诸如语义角色标注（SRL）和介词短语（PP）附加消歧等任务中是重要的。在理论语言学中，存在许多用于论证的诊断测试，但它们经常产生相互矛盾的和潜在的梯度结果。这对于句法上倾斜的项目（如PP）来说尤其如此。我们提出了两个PP论证预测任务，从这两个动机分支：（1）VerbNet中PP的二元论元-附属分类，以及（2）使用人类判断作为金标准的梯度论证预测，并报告使用预训练的词嵌入和其他语言信息特征的预测模型的结果。我们在每个任务上的最佳结果是（1）$acc.=0.955$、$F_1=0.954$（ELMo+BiLSTM）和（2）Pearson的$r=0.624$（word 2 vec +MLP）。此外，我们证明了论证预测的效用，通过SRL的性能增益时，句子编码器与我们的任务进行预训练，以提高句子表示。
英文摘要：
Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such as semantic role labeling (SRL) and prepositional phrase (PP) attachment disambiguation. In theoretical linguistics, many diagnostic tests for argumenthood exist but they often yield conflicting and potentially gradient results. This is especially the case for syntactically oblique items such as PPs. We propose two PP argumenthood prediction tasks branching from these two motivations: (1) binary argument-adjunct classification of PPs in VerbNet, and (2) gradient argumenthood prediction using human judgments as gold standard, and report results from prediction models that use pretrained word embeddings and other linguistically informed features. Our best results on each task are (1) $acc.=0.955$, $F_1=0.954$ (ELMo+BiLSTM) and (2) Pearson's $r=0.624$ (word2vec+MLP). Furthermore, we demonstrate the utility of argumenthood prediction in improving sentence representations via performance gains on SRL when a sentence encoder is pretrained with our tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
