Stance Detection in Web and Social Media: A Comparative Study

Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media

Graphemic puns, and software making them up: The case of Hebrew vs Chinese and Japanese

Yaakov HaCohen-Kerner, Daniel Nisim Cohen, Ephraim Nissan and Ghil'ad Zuckerman

Text Summarization by Sentence Extraction Using Unsupervised Learning

The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. Although, some approaches claim being domain and language independent, they use high dependence knowledge like key-phrases or golden samples for machine-learning approaches. In this work, we propose a language- and domain-independent automatic text summarization approach by sentence extraction using an unsupervised learning algorithm. Our hypothesis is that an unsupervised algorithm can help for clustering similar ideas (sentences). Then, for composing the summary, the most representative sentence is selected from each cluster. Several experiments in the standard DUC-2002 collection show that the proposed method obtains more favorable results than other approaches