45 research outputs found
TC-GAT: Graph Attention Network for Temporal Causality Discovery
The present study explores the intricacies of causal relationship extraction,
a vital component in the pursuit of causality knowledge. Causality is
frequently intertwined with temporal elements, as the progression from cause to
effect is not instantaneous but rather ensconced in a temporal dimension. Thus,
the extraction of temporal causality holds paramount significance in the field.
In light of this, we propose a method for extracting causality from the text
that integrates both temporal and causal relations, with a particular focus on
the time aspect. To this end, we first compile a dataset that encompasses
temporal relationships. Subsequently, we present a novel model, TC-GAT, which
employs a graph attention mechanism to assign weights to the temporal
relationships and leverages a causal knowledge graph to determine the adjacency
matrix. Additionally, we implement an equilibrium mechanism to regulate the
interplay between temporal and causal relations. Our experiments demonstrate
that our proposed method significantly surpasses baseline models in the task of
causality extraction.Comment: Accepted by IJCNN 202
A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency
Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user’s retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user’s network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user’s retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks
Frozen CLIP Model is An Efficient Point Cloud Backbone
The pretraining-finetuning paradigm has demonstrated great success in NLP and
2D image fields because of the high-quality representation ability and
transferability of their pretrained models. However, pretraining such a strong
model is difficult in the 3D point cloud field since the training data is
limited and point cloud collection is expensive. This paper introduces
Efficient Point Cloud Learning (EPCL), an effective and efficient point cloud
learner for directly training high-quality point cloud models with a frozen
CLIP model. Our EPCL connects the 2D and 3D modalities by semantically aligning
the 2D features and point cloud features without paired 2D-3D data.
Specifically, the input point cloud is divided into a sequence of tokens and
directly fed into the frozen CLIP model to learn point cloud representation.
Furthermore, we design a task token to narrow the gap between 2D images and 3D
point clouds. Comprehensive experiments on 3D detection, semantic segmentation,
classification and few-shot learning demonstrate that the 2D CLIP model can be
an efficient point cloud backbone and our method achieves state-of-the-art
accuracy on both real-world and synthetic downstream tasks. Code will be
available.Comment: Technical repor
Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures
The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness