Search Results for author: Sanja Stajner

Found 10 papers, 1 papers with code

Exploring Reliability of Gold Labels for Emotion Detection in Twitter

no code implementations RANLP 2021 Sanja Stajner

Emotion detection from social media posts has attracted noticeable attention from natural language processing (NLP) community in recent years.


How to Obtain Reliable Labels for MBTI Classification from Texts?

no code implementations RANLP 2021 Sanja Stajner, Seren Yenikent

Automatic detection of the Myers-Briggs Type Indicator (MBTI) from short posts attracted noticeable attention in the last few years.


Lexical Simplification Benchmarks for English, Portuguese, and Spanish

2 code implementations12 Sep 2022 Sanja Stajner, Daniel Ferres, Matthew Shardlow, Kai North, Marcos Zampieri, Horacio Saggion

To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs.\ non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset.

Lexical Simplification

What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts

no code implementations ACL 2021 Sanja Stajner, Seren Yenikent, Bilal Ghanem, Marc Franco-Salvador

According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people{'}s everyday life and career.

Benchmarking Binary Classification +1

Why Is MBTI Personality Detection from Texts a Difficult Task?

no code implementations EACL 2021 Sanja Stajner, Seren Yenikent

Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities.

A Survey of Automatic Personality Detection from Texts

no code implementations COLING 2020 Sanja Stajner, Seren Yenikent

Personality profiling has long been used in psychology to predict life outcomes.

When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features

no code implementations LREC 2020 Sanja Stajner, Ioana Hulpu{\textcommabelow{s}}

We find that the shallow features achieve state-of-the-art results on both tasks, significantly outperforming performances of the deep semantic features on the five-level classification task.

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