Aspect sentiment classification (ASC) constitutes a crucial research area within sentiment analysis tasks, aiming to predict sentiment polarity toward different aspects in given contexts. Identifying the relations between aspects and sentiments can be a challenging task, as aspects and sentiments are not always predefined. Most existing studies have demonstrated the effectiveness of using dependency parsing tree and graph convolutional network (GCN), achieving good experimental results. However, existing methods have mainly focused on either semantic or syntactic information individually and may introduce errors when the input sentence lacks clear syntactic information. To address these issues, we propose a novel approach based on Dual-Channel Graph Convolutional Network (DC-GCN), which integrates feature fusion within a dual-channel architecture. Our model can effectively capture the semantic information and enhance the feature representation of syntactic structures by introducing the multi-head self-attention graph convolution, guided by the TopK strategy, and the directional densely connected graph convolutional network. We further employ a bi-affine strategy and multi-layer perceptron to integrate semantic and syntactic information. Experimental results on publicly available datasets demonstrate the superior performance of our model over state-of-the-art methods. Specifically, our model improves upon baseline models on the Twitter, Lap14, Rest14, Rest15, and Rest16 datasets, with increases in Accuracy/Macro-F1 scores of 0.06/0.58, 0.58/0.47, 0.25/1.19, 0.23/1.05, and 0.36/1.32, respectively.

Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Previous studies usually obtain this property by greedily learning the local connections between relations. However, they are essentially limited because of failing to capture the global topology structure of relation ties and may easily fall into a locally optimal solution. To address this issue, we propose a novel force-directed graph to comprehensively learn relation ties. Specifically, we first construct a graph according to the global co-occurrence of all relations. Then, we borrow the idea of Coulomb’s law from physics and introduce the concept of attractive force and repulsive force into this graph to learn correlation and mutual exclusion between relations. Finally, the obtained relation representations are applied as an inter-dependent relation classifier. Extensive experimental results demonstrate that our method is capable of modeling global correlation and mutual exclusion between relations, and outperforms the state-of-the-art baselines. In addition, the proposed force-directed graph can be used as a module to augment existing relation extraction systems and improve their performance.

Extracting relational triples from unstructured text is an essential task in natural language processing and knowledge graph construction. Existing approaches usually contain two fundamental steps: (1) finding the boundary positions of head and tail entities; (2) concatenating specific tokens to form triples. However, nearly all previous methods suffer from the problem of error accumulation, i.e., the boundary recognition error of each entity in step (1) will be accumulated into the final combined triples. To solve the problem, in this paper, we introduce a fresh perspective to revisit the triple extraction task and propose a simple but effective model, named DirectRel. Specifically, the proposed model first generates candidate entities through enumerating token sequences in a sentence, and then transforms the triple extraction task into a linking problem on a ``head -> tail" bipartite graph. By doing so, all triples can be directly extracted in only one step. Extensive experimental results on two widely used datasets demonstrate that the proposed model performs better than the state-of-the-art baselines.

The noisy labeling problem has been one of the major obstacles for distant supervised relation extraction. Existing approaches usually consider that the noisy sentences are useless and will harm the model's performance. Therefore, they mainly alleviate this problem by reducing the influence of noisy sentences, such as applying bag-level selective attention or removing noisy sentences from sentence-bags. However, the underlying cause of the noisy labeling problem is not the lack of useful information, but the missing relation labels. Intuitively, if we can allocate credible labels for noisy sentences, they will be transformed into useful training data and benefit the model's performance. Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. Specifically, our model contains three modules: a sentence encoder, a noise detector and a label generator. The sentence encoder is used to obtain feature representations. The noise detector detects noisy sentences from sentence-bags, and the label generator produces high-confidence relation labels for noisy sentences. Extensive experimental results demonstrate that our model outperforms the state-of-the-art baselines on a popular benchmark dataset, and can indeed alleviate the noisy labeling problem.