Recognition and classification of Figurative Language (FL) is an open problem of Sentiment Analysis in the broader field of Natural Language Processing (NLP) due to the contradictory meaning contained in phrases with metaphorical content.
Neural Architecture Search (NAS) has recently gained increased attention, as a class of approaches that automatically searches in an input space of network architectures.
This paper introduces the Class-wise Principal Component Analysis, a supervised feature extraction method for hyperspectral data.
This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles.
In this paper we report on a research effort focusing on recognition of static features of sign formation in single sign videos.
Figurative Language (FL) seems ubiquitous in all social-media discussion forums and chats, posing extra challenges to sentiment analysis endeavors.
Sentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge amounts of related data.
As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online.
Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data, with the actual textual description of items serving only as side information.