## 2 Related Work  
**Metonymy Resolution** Analysis of metonymy as a linguistic phenomenon dates back to feature-based methods [17; 18]. Ref. [19] use an SVM with a robust knowledge base built from Wikipedia to remain the best result in all feature-based methods. These methods inevitably suffer from error propagation because of their dependence on the manual feature extraction. Furthermore, the work takes much effort, while helpful information is hard to be caught. As a result, the performance is not satisfied. Recently, the majority of works for MR have focused on the deep neural network (DNN). Ref. [8] propose PreWin based on Long Short Term Memory (LSTM) architecture. This work is the first to integrate structural knowledge into MR. In contrast, the effect is limited because of the setting of the predicate window, which only retains the words around the predicate while losing much important information in that process. Other approaches [20] leverage NER and POS features on LSTM to enrich the representation of tokens. However, these methods only improve slightly since they only pay attention to the independent tokens and ignore the relations between tokens in a sentence that is beneficial to MR.  
Pre-trained language models have shown great success in many NLP tasks. In particular, BERT, proposed by [16], shows a significant impact. Intuitively, it is natural to introduce pre-trained models to MR. These models vastly outperform the conventional DNN models and reach the top of the leaderboard in MR. Nevertheless, pre-trained models encode the whole sentence to catch contextual features, leading to the ignorance of syntactic features in sentences. Ref. [21] fine-tunes BERT with target word masking and data augmentation to detect metonymy more accurately. In addition to the context information provided by the sequence-based model, [22] pays attention to the entity word. They disambiguate every word in a sentence by reformulating metonymy detection as a sequence labeling task and investigate the impact of entity and context on metonymy detection. **Dependency Constraints Integration** The research on MR so far has made limited application of dependency trees. However, research on other NLP classification tasks widely employ dependency information. Differing from traditional sequence-based models, dependency-based models integrate dependency information [23], taking advantage of seizing dependency relations that are obscure from the surface form alone. As the effect of dependency information is widely recognized, more attention is paid to pruning strategies (i.e., how to distill syntactic information from dependency trees efficiently). Ref. [3] use the shortest dependency path between the entities in the full tree. Ref. [24] apply graph convolutional networks [25] model on a pruned tree and a novel pruning strategy to the input trees by retaining words immediately around the shortest path between entities among which a relation might hold. Although these hard-pruningmethods remove irrelevant relations efficiently based on predefined rules, they suffer from eliminating useful information wrongly at the same time. More recently, ref. [26] proposed AGGCN and employed a soft-pruning strategy. The method enables the dependency relations to have weights to balance relevant and irrelevant information with multi-head attention mechanism. Ref. [27] proposed a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN). In this approach, an attention mechanism in graph convolutional networks is applied to different contextual words in the dependency tree obtained from an off-the-shelf dependency parser, to distinguish the importance of different word dependencies. **Pre-trained Model** This is the idea of pre-training, originated in the field of computer vision, and then developed into NLP. A pre-trained word vector is the most common application of pre-training in NLP. The annotated corpus is very limited in many NLP tasks, which is not enough to train excellent word vectors.