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Found 19 papers, 4 papers with code
Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations
By analyzing embeddings such as bag-of-words, character n-grams, Word2Vec, GloVe, fastText, and GPT2 embeddings, we examine how well our one-shot classification systems capture semantic information within medical consultations.
MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms
This study investigates the computational processing of euphemisms, a universal linguistic phenomenon, across multiple languages.
MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations
Zero-shot classification enables text to be classified into classes not seen during training.
Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model
In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023.
FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms
Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context.
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification
Leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language.
A Report on the Euphemisms Detection Shared Task
This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022.
Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms
This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs.
CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms
Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not.
yosm: A new yoruba sentiment corpus for movie reviews
Several works on sentiment analysis have been done on high resource languages while low resources languages like Yoruba have been sidelined.
Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection
Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used.
Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo
The crowdsourcing results suggest that while humans tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterparts, they in general cannot make a better guess than our model when it comes to `reading the mind' of the censors in deciding whether a blogpost should be censored.
Neural Network Prediction of Censorable Language
According to Freedom House{'}s annual Freedom on the Net report, more than half the world{'}s Internet users now live in a place where the Internet is censored or restricted.
Linguistic Characteristics of Censorable Language on SinaWeibo
This paper investigates censorship from a linguistic perspective.
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Our starting point is that words in a given text segment, such as a paragraph, that are highranking representatives of a common topic of discussion are less likely to be a part of an idiomatic expression.
Experiments in Idiom Recognition
Some expressions can be ambiguous between idiomatic and literal interpretations depending on the context they occur in, e. g., {`}sales hit the roof{'} vs. {`}hit the roof of the car{'}.
