“欺骗是一种常见的社会现象, 但对欺骗类动词的研究十分有限。本文筛选“欺骗”类动词的单句并对其进行大规模的句法依存和语义依存分析。研究显示,“欺骗”类动词在句中作为从属词时, 可作为不同的句法成分和语义角色, 同时此类动词在句法功能上表现出高度的相似性。作为支配词的“欺骗”类动词, 承担不同句法功能时, 表现出不同的句法共现模式。语义上, 本文详细描述、解释了该类动词在语义密度、主客体角色、情境角色和事件关系等维度的语义依存特点。“欺骗”类动词的句法语义虽具有多样性, 但主要的句型为主谓宾句式, 而该句式中最常用的语义搭配模式是施事对涉事进行欺骗行为, 并对涉事产生影响。本研究结合依存语法和框架语义学, 融合定量统计和定性分析探究欺骗类动词的句法语义, 深化了对欺骗行为言语线索以及言说动词的研究。”
We employ a metric model and a layout encoder to map the RGB images and the ground-truth layouts to the embedding space, respectively, and a layout decoder to map the embeddings to the corresponding layouts, where the whole framework is trained in an end-to-end manner.
In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models.
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents.
In recent years, large-scale pre-trained language models (PLMs) containing billions of parameters have achieved promising results on various NLP tasks.
Prompt-based paradigm has shown its competitive performance in many NLP tasks.