Shanshan Feng — Page 3

arXiv2020

User-based Network Embedding for Collective Opinion Spammer Detection

Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation. To further enhance the influence of spam reviews, spammers often collaboratively post spam reviewers within a short period of time, the activities of whom are called collective opinion spam campaign. As the goals and members of the spam campaign activities change frequently, and some spammers also imitate normal purchases to conceal identity, which makes the spammer detection challenging. In this paper, we propose an unsupervised network embedding-based approach to jointly exploiting different types of relations, e.g., direct common behaviour relation and indirect co-reviewed relation to effectively represent the relevances of users for detecting the collective opinion spammers. The average improvements of our method over the state-of-the-art solutions on dataset AmazonCn and YelpHotel are [14.09%,12.04%] and [16.25%,12.78%] in terms of AP and AUC, respectively.

cs.IR↗

arXiv2020

A Survey on Recent Advances in Sequence Labeling from Deep Learning Models

Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc. Though prevalent and effective in many downstream applications (e.g., information retrieval, question answering, and knowledge graph embedding), conventional sequence labeling approaches heavily rely on hand-crafted or language-specific features. Recently, deep learning has been employed for sequence labeling tasks due to its powerful capability in automatically learning complex features of instances and effectively yielding the stat-of-the-art performances. In this paper, we aim to present a comprehensive review of existing deep learning-based sequence labeling models, which consists of three related tasks, e.g., part-of-speech tagging, named entity recognition, and text chunking. Then, we systematically present the existing approaches base on a scientific taxonomy, as well as the widely-used experimental datasets and popularly-adopted evaluation metrics in the SL domain. Furthermore, we also present an in-depth analysis of different SL models on the factors that may affect the performance and future directions in the SL domain.

cs.CL↗