Analysis of Italian Word Embeddings

TL;DR

This paper evaluates the performance of skip-gram and continuous bag of words algorithms for Italian word embeddings, analyzing hyper-parameter effects to identify optimal configurations for various tasks.

Contribution

It provides a detailed analysis and evaluation of hyper-parameter tuning for Italian word embeddings using skip-gram and CBOW algorithms.

Findings

Optimal hyper-parameter configurations identified for specific tasks.

Performance differences between the algorithms analyzed.

Guidelines for tuning embeddings in Italian language tasks.

Abstract

In this work we analyze the performances of two of the most used word embeddings algorithms, skip-gram and continuous bag of words on Italian language. These algorithms have many hyper-parameter that have to be carefully tuned in order to obtain accurate word representation in vectorial space. We provide an accurate analysis and an evaluation, showing what are the best configuration of parameters for specific tasks.