92 research outputs found
Researchers eye-view of sarcasm detection in social media textual content
The enormous use of sarcastic text in all forms of communication in social
media will have a physiological effect on target users. Each user has a
different approach to misusing and recognising sarcasm. Sarcasm detection is
difficult even for users, and this will depend on many things such as
perspective, context, special symbols. So, that will be a challenging task for
machines to differentiate sarcastic sentences from non-sarcastic sentences.
There are no exact rules based on which model will accurately detect sarcasm
from many text corpus in the current situation. So, one needs to focus on
optimistic and forthcoming approaches in the sarcasm detection domain. This
paper discusses various sarcasm detection techniques and concludes with some
approaches, related datasets with optimal features, and the researcher's
challenges.Comment: 8 page
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
Figures of speech, such as metaphor and irony, are ubiquitous in literature
works and colloquial conversations. This poses great challenge for natural
language understanding since figures of speech usually deviate from their
ostensible meanings to express deeper semantic implications. Previous research
lays emphasis on the literary aspect of figures and seldom provide a
comprehensive exploration from a view of computational linguistics. In this
paper, we first propose the concept of figurative unit, which is the carrier of
a figure. Then we select 12 types of figures commonly used in Chinese, and
build a Chinese corpus for Contextualized Figure Recognition (ConFiguRe).
Different from previous token-level or sentence-level counterparts, ConFiguRe
aims at extracting a figurative unit from discourse-level context, and
classifying the figurative unit into the right figure type. On ConFiguRe, three
tasks, i.e., figure extraction, figure type classification and figure
recognition, are designed and the state-of-the-art techniques are utilized to
implement the benchmarks. We conduct thorough experiments and show that all
three tasks are challenging for existing models, thus requiring further
research. Our dataset and code are publicly available at
https://github.com/pku-tangent/ConFiguRe.Comment: Accepted to Coling 202
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