Our work addresses the problem of unsupervised Aspect Category Detection using a small set of seed words.Recent works have focused on learning embedding spaces for seed words and sentences to establish similarities between sentences and aspects.However, aspect representations are limited by the quality of initial seed words, and model performances are compromised by noise.To mitigate this limitation, we propose a simple framework that automatically enhances the quality of initial seed words and selects high-quality sentences for training instead of using the entire dataset.Our main concepts are to add a number of seed words to the initial set and to treat the task of noise resolution as a task of augmenting data for a low-resource task.In addition, we jointly train Aspect Category Detection with Aspect Term Extraction and Aspect Term Polarity to further enhance performance.This approach facilitates shared representation learning, allowing Aspect Category Detection to benefit from the additional guidance offered by other tasks.Extensive experiments demonstrate that our framework surpasses strong baselines on standard datasets.

Software-defined networking (SDN) is a novel net-working paradigm for network management and autonomous systems. However, SDN has some challenges with scalability and quality of services (QoS) in distributed multi-domain scenarios due to the unprecedented growth of heterogeneous characteristics services. There is a current gap in a standard routing mechanism for satisfying various service requirements in distributed SDN. Most existing works design a homogeneous routing strategy for heterogeneous services, which might need to be more scalable and efficient for the future of rising heterogeneous online services. This study proposes a multi service-oriented routing mechanism for multi-domain SDN, which aims to help Internet service providers (ISPs) achieve high QoS and service-level agreements (SLAs). The mechanism utilizes a service classification (through a deep learning model) and optimizes network routing (using a new cost function containing both QoS and the server load). The mechanism has been integrated into the Knowledge-defined heterogeneous network architecture and tested on four prevalent considered services: E-commerce, Interactive Data, Video On-demand, and Bulk Data Transfer. The experimental results indicate that the proposed service-oriented routing mechanism outperforms the benchmark in terms of faster server response time while reducing up to 25% of the network congestion.