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Found 17 papers, 1 papers with code
Mitigating Topic Bias when Detecting Decisions in Dialogue
This work revisits the task of detecting decision-related utterances in multi-party dialogue.
CoRAL: a Context-aware Croatian Abusive Language Dataset
In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task.
Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model
Moderation of reader comments is a significant problem for online news platforms.
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages
We present XHate-999, a multi-domain and multilingual evaluation data set for abusive language detection.
Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction
Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure.
PANDORA Talks: Personality and Demographics on Reddit
Personality and demographics are important variables in social sciences, while in NLP they can aid in interpretability and removal of societal biases.
Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context
We address the task of automatically detecting toxic content in user generated texts.
Data Set for Stance and Sentiment Analysis from User Comments on Croatian News
Nowadays it is becoming more important than ever to find new ways of extracting useful information from the evergrowing amount of user-generated data available online.
Cross-Domain Detection of Abusive Language Online
We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types.
Combining Shallow and Deep Learning for Aggressive Text Detection
The task, however, is far from being trivial, as what is considered as aggressive speech can be quite subjective, and the task is further complicated by the noisy nature of user-generated text on social networks.
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA
We use features based on different semantic similarity models (e. g., Latent Dirichlet Allocation), as well as features based on several types of pre-trained word embeddings.
Evaluation of Classification Algorithms and Features for Collocation Extraction in Croatian
Features which contributed the most to overall performance were PMI, semantic relatedness, and POS information.
