5 papers
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction
Gabriele Picco, Marcos Martínez Galindo, Alberto Purpura +3
The Zero-Shot Learning (ZSL) task pertains to the identification of entities or relations in texts that were not seen during training. ZSL has emerged as a critical research area d…
Learning to Rank from Relevance Judgments Distributions
Alberto Purpura, Gianmaria Silvello, Gian Antonio Susto
Learning to Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair. Within the Cranfield…
Neural Feature Selection for Learning to Rank
Alberto Purpura, Karolina Buchner, Gianmaria Silvello +1
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neu…
Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms
Alessandro Fabris, Alberto Purpura, Gianmaria Silvello +1
Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found en…
A Semi-Automated Approach for Information Extraction, Classification and Analysis of Unstructured Data
Alberto Purpura, Marco Calaresu
In this paper, we show how Quantitative Narrative Analysis and simple Natural Language Processing techniques apply to the extraction and categorization of data in a sample case stu…