20 research outputs found
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
Nativised, Playfully Aetiologised Literary Zoonyms, I: The Squirrel Cuckoo. Rich Onomasiology, Semantic Calques, and Phono-Morphological Matching, Encapsulated into a Stylemically Constrained Narration
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