Sense representations for Portuguese: experiments with sense embeddings and deep neural language models

TL;DR

This paper investigates unsupervised sense representations for Portuguese, introducing sense embeddings and deep neural language models, demonstrating their effectiveness in NLP tasks like semantic similarity and analogies.

Contribution

It presents the first Portuguese sense embeddings and evaluates deep neural models like BERT and ELMo for sense disambiguation and transfer learning.

Findings

Sense2vec outperformed traditional embeddings in analogy tasks.

Fine-tuned BERT models achieved higher accuracy in semantic similarity.

Portuguese sense representations improve NLP task performance.

Abstract

Sense representations have gone beyond word representations like Word2Vec, GloVe and FastText and achieved innovative performance on a wide range of natural language processing tasks. Although very useful in many applications, the traditional approaches for generating word embeddings have a strict drawback: they produce a single vector representation for a given word ignoring the fact that ambiguous words can assume different meanings. In this paper, we explore unsupervised sense representations which, different from traditional word embeddings, are able to induce different senses of a word by analyzing its contextual semantics in a text. The unsupervised sense representations investigated in this paper are: sense embeddings and deep neural language models. We present the first experiments carried out for generating sense embeddings for Portuguese. Our experiments show that the sense embedding model (Sense2vec) outperformed traditional word embeddings in syntactic and semantic analogies task, proving that the language resource generated here can improve the performance of NLP tasks in Portuguese. We also evaluated the performance of pre-trained deep neural language models (ELMo and BERT) in two transfer learning approaches: feature based and fine-tuning, in the semantic textual similarity task. Our experiments indicate that the fine tuned Multilingual and Portuguese BERT language models were able to achieve better accuracy than the ELMo model and baselines.