Automatic Chinese patent approval prediction is an emerging and valuable task in patent analysis. However, it involves a rigorous and transparent decision-making process that includes patent comparison and examination to assess its innovation and correctness. This resultant necessity of decision evidentiality, coupled with intricate patent comprehension presents significant challenges and obstacles for the patent analysis community. Consequently, few existing studies are addressing this task. This paper presents the pioneering effort on this task using a retrieval-based classification approach. We propose a novel framework called DiSPat, which focuses on structural representation learning and disentanglement to predict the approval of Chinese patents and offer decision-making evidence. DiSPat comprises three main components: base reference retrieval to retrieve the Top-k most similar patents as a reference base; structural patent representation to exploit the inherent claim hierarchy in patents for learning a structural patent representation; disentangled representation learning to learn disentangled patent representations that enable the establishment of an evidential decision-making process. To ensure a thorough evaluation, we have meticulously constructed three datasets of Chinese patents. Extensive experiments on these datasets unequivocally demonstrate our DiSPat surpasses state-of-the-art baselines on patent approval prediction, while also exhibiting enhanced evidentiality.

The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news by analyzing news content and/or its social context. However, these methods often overlook essential textual news content (articles) and heavily rely on sequential modeling and global attention to extract semantic information. These existing methods fail to handle the complex, subtle twists1 in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing. To bridge these significant gaps, we propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news. Specifically, we introduce a syntactical dependency graph and design a multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It extends the effect of word perception, leading to effective noise filtering and adjacent relation enhancement. Subsequently, a sequential relative position-aware Transformer is designed to capture the sequential information, together with an elaborate keyword debiasing module to mitigate the prior bias. Extensive experimental results on two public benchmark datasets verify the effectiveness and superior performance of our proposed MSynFD over state-of-the-art detection models.

The propagation of rumors is a complex and varied phenomenon. In the process of rumor dissemination, in addition to rumor claims, there will be abundant social context information surrounding the rumor. Therefore, it is vital to learn the characteristics of rumors in terms of both the linear temporal sequence and the non-linear diffusion structure simultaneously. However, in some existing research, time-dependent and diffusion-related information has not been fully utilized. Accordingly, in this paper, we propose a novel model Rumor Detection with Field of Linear and Non-Linear Propagation (RDLNP) to automatically detect rumors from the above two fields by taking advantage of claim content, social context and temporal information. First, the Rumor Hybrid Feature Learning (RHFL) we designed can extract the correlations between the claims and temporal information, differentiate the hybrid features of specific posts, and generate unified representations for rumors. Second, we proposed Non-Linear Structure Learning (NLSL) and Linear Sequence Learning (LSL) to integrate contextual features along the path of the diffusion structure and temporal engagement variation of responses respectively. Finally, Shared Feature Learning (SFL) models the representation reinforcement and learns the mutual influence between NLSL and LSL, and then highlights their valuable features. Experiments conduct on two public and widely used datasets, i.e. PHEME and RumorEval, demonstrate both the effectiveness and the outstanding performance of the proposed approach.

Aspect term extraction (ATE) is a subtask of aspect-based sentiment analysis (ABSA), which aims to extract all aspect-specific words in a sentence. Recent neural network methods ignore the problem that word may play different semantic roles in different sentences and have limitation in handling dependencies between labels. In this work, we first exploit BERT as embedding layer to obtain word-level representations and utilize BERT architecture to capture global sequence features. Then, a position-aware attention is proposed to extract local context information. Global-local context representations of words are built by merging the global sequence features and local context information, which can select related information from both sides: global sequence and local context. Finally, to model the label dependency, we construct a label dependency module based on RNN and CRF, where the previous label features are introduced as additional information for label relationship modeling. Experimental results on four benchmark datasets show that our proposed model obtains the state-of-the-art performance.

Question-answering sentiment analysis (QASA) is a novel but meaningful sentiment analysis task based on question-answering online reviews. Existing neural network-based models that conduct sentiment analysis of online reviews have already achieved great success. However, the syntax and implicitly semantic connection in the dependency tree have not been made full use of, especially for Chinese which has specific syntax. In this work, we propose a Residual-Duet Network leveraging textual and tree dependency information for Chinese question-answering sentiment analysis. In particular, we explore the synergies of graph embedding with structural dependency links to learn syntactic information. The transverse and longitudinal compression encoders are developed to capture sentiment evidence with disparate types of compression and different residual connections. We evaluate our model on three Chinese QASA datasets in different domains. Experimental results demonstrate the superiority of our proposed model in Chinese question-answering sentiment analysis.

Recently, sequential recommendation has attracted substantial attention from researchers due to its status as an essential service for e-commerce. Accurately understanding user intention is an important factor to improve the performance of recommendation system. However, user intention is highly time-dependent and flexible, so it is very challenging to learn the latent dynamic intention of users for sequential recommendation. To this end, in this paper, we propose a novel intention modeling from ordered and unordered facets (IMfOU) for sequential recommendation. Specifically, the global and local item embedding (GLIE) we proposed can comprehensively capture the sequential context information in the sequences and highlight the important features that users care about. We further design ordered preference drift learning (OPDL) and unordered purchase motivation learning (UPML) to obtain user’s the process of preference drift and purchase motivation respectively. With combining the users’ dynamic preference and current motivation, it considers not only sequential dependencies between items but also flexible dependencies and models the user purchase intention more accurately from ordered and unordered facets respectively. Evaluation results on three real-world datasets demonstrate that our proposed approach achieves better performance than the state-of-the-art sequential recommendation methods achieving improvement of AUC by an average of 2.26%.

The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.

In this paper, a novel similarity measuring method based on Rough Formal Concept Analysis (RFCA) and information content similarity(ICS) is proposed which evaluates the similarity degree between the concepts. We use the information content approach to automatically obtain part of similarity scores of two concepts which makes up the normal featural and structural evaluating model. Thus the similarity of two concepts can be directly calculated from the lower object approximations and lower attribute approximations based on the RFCA and ICS. Consequently the proposed method combines semantic, featural and structural information into decision which can be viewed as the development of Tverskyąrs similarity model.