Improving subjectivity detection for Spanish texts using subjectivity word sense disambiguation based on knowledge

Cabezudo, Marco Antonio Sobrevilla; Palomino, Nora Bertha La Serna; Perez, Rolando Maguina

doi:10.1109/clei.2015.7360018

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Figure 1 shows an example of a MC positive sentiment sentence. Other types of text classification datasets that we include are the FilmAffinity corpus (FAC) (Sobrevilla Cabezudo et al, 2015) for subjective/objective classification and the Spanish QC dataset (SQC) ( Á. García Cumbreras et al, 2006) for question-type. For all of these tasks, the input to the classifier is the representation of the sentence.…”

Section: Sentence Classification (Sc)mentioning

confidence: 99%

Evaluation Benchmarks for Spanish Sentence Representations

Araujo¹,

Carvallo²,

Kundu³

et al. 2022

Preprint

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Due to the success of pre-trained language models, versions of languages other than English have been released in recent years. This fact implies the need for resources to evaluate these models. In the case of Spanish, there are few ways to systematically assess the models' quality. In this paper, we narrow the gap by building two evaluation benchmarks. Inspired by previous work (Conneau and Kiela, 2018;Chen et al., 2019), we introduce Spanish SentEval and Spanish DiscoEval, aiming to assess the capabilities of stand-alone and discourse-aware sentence representations, respectively. Our benchmarks include considerable pre-existing and newly constructed datasets that address different tasks from various domains. In addition, we evaluate and analyze the most recent pre-trained Spanish language models to exhibit their capabilities and limitations.As an example, we discover that for the case of discourse evaluation tasks, mBERT, a language model trained on multiple languages, usually provides a richer latent representation than models trained only with documents in Spanish. We hope our contribution will motivate a fairer, more comparable, and less cumbersome way to evaluate future Spanish language models.

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Section: Sentence Classification (Sc)mentioning

confidence: 99%

Evaluation Benchmarks for Spanish Sentence Representations

Araujo¹,

Carvallo²,

Kundu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These linguistic resources provided polarity values to evaluate the degree to which a word expresses subjectivity and objectivity. A fuzzy clustering algorithm was utilized [18].…”

Section: Introductionmentioning

confidence: 99%

Word Sense Disambiguation based on Maximum Entropy Classifier

Zhang¹

2019

IJPE

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Word sense disambiguation (WSD) is one of the most important research issues in the field of natural language processing. In this paper, a new method of word sense disambiguation is proposed, in which words and parts of speech (POS) are extracted as discriminative features. At the same time, a maximum entropy classifier is adopted to determine ambiguous words' semantic categories. Training data of SemEval-2007: Task#5 is used to optimize the maximum entropy model. A test corpus is applied to test the performance of the WSD classifier. Experimental results show that the performance of word sense disambiguation is improved after the proposed approach is used.

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Effectiveness of Recent Research Approaches in Natural Language Processing on Data Science-An Insight

Shruthi¹,

Swamy²

2018

Advances in Intelligent Systems and Computing

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Improving subjectivity detection for Spanish texts using subjectivity word sense disambiguation based on knowledge

Cited by 7 publications

References 14 publications

Evaluation Benchmarks for Spanish Sentence Representations

Evaluation Benchmarks for Spanish Sentence Representations

Word Sense Disambiguation based on Maximum Entropy Classifier

Effectiveness of Recent Research Approaches in Natural Language Processing on Data Science-An Insight

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