Search Results for author:
Found 16 papers, 7 papers with code
Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models
Topic models have been widely used to discover hidden topics in a collection of documents.
An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures
Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents.
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI), which explores the detection of misogynous memes on the web by taking advantage of available texts and images.
A gentle push funziona benissimo: making instructed models in Italian via contrastive activation steering
Adapting models to a language that was only partially present in the pre-training data requires fine-tuning, which is expensive in terms of both data and computational resources.
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence
Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences.
AI-UPV at EXIST 2023 -- Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime
In particular, our system is articulated in three different pipelines.
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Topic models are statistical methods that extract underlying topics from document collections.
Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content
Two further binary labels have been collected from both the experts and the crowdsourcing platform, for memes evaluated as misogynistic, concerning aggressiveness and irony.
OCTIS: Comparing and Optimizing Topic models is Simple!
In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach.
Profiling Italian Misogynist: An Empirical Study
Hate speech may take different forms in online social environments.
Cross-lingual Contextualized Topic Models with Zero-shot Learning
They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.
Constrained Relational Topic Models
Relational topic models (RTM) have been widely used to discover hidden topics in a collection of networked documents.
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter.
A Multi-View Sentiment Corpus
Sentiment Analysis is a broad task that involves the analysis of various aspect of the natural language text.
TWINE: A real-time system for TWeet analysis via INformation Extraction
In this paper we present TWINE, a real-time system for the big data analysis and exploration of information extracted from Twitter streams.
Named entity recognition using conditional random fields with non-local relational constraints
We begin by introducing the Computer Science branch of Natural Language Processing, then narrowing the attention on its subbranch of Information Extraction and particularly on Named Entity Recognition, discussing briefly its main methodological approaches.
