Search Results for author:
Found 10 papers, 4 papers with code
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories.
GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps
We approach this task as a sequence tagging problem, where the goal is to produce <POI name, accessibility label> pairs from unstructured text.
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification
Multi-label text classification is one of the fundamental tasks in natural language processing.
SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media
The rapid development of social media changes the lifestyle of people and simultaneously provides an ideal place for publishing and disseminating rumors, which severely exacerbates social panic and triggers a crisis of social trust.
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning
In this way, we can explicitly exploit the credibility of publishers and users for early fake news detection.
DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users' Dynamic Preferences for Information Diffusion Prediction
For one thing, previous works cannot jointly utilize both the social network and diffusion graph for prediction, which is insufficient to model the complexity of the diffusion process and results in unsatisfactory prediction performance.
Beyond Statistical Relations: Integrating Knowledge Relations into Style Correlations for Multi-Label Music Style Classification
Based on the two types of relations, we use a graph convolutional network to learn the deep correlations between styles automatically.
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots
Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information.
Ranked #5 on
Conversational Response Selection
on RRS
Learning review representations from user and product level information for spam detection
Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information.
Jointly embedding the local and global relations of heterogeneous graph for rumor detection
The development of social media has revolutionized the way people communicate, share information and make decisions, but it also provides an ideal platform for publishing and spreading rumors.
