Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis

Morio, Gaku; Ozaki, Hiroaki; Yamaguchi, Atsuki; Sogawa, Yasuhiro

doi:10.18653/v1/2022.semeval-1.188

Cited by 4 publications

(3 citation statements)

References 21 publications

(31 reference statements)

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“…Another group of authors studies conveying the structure of target in context, emphasizing the target in texts and forming a prompt-based input constructions (Sun et al, 2019a;Shin et al, 2020). To the best of our knowledge, the structured representation of the input contexts (Morio et al, 2022) as a part of sequence-to-sequence and graph-based models represents the latest advances in target oriented sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts

Golubev,

Rusnachenko,

Loukachevitch

2023

Computational Linguistics and Intellectual Technologies”

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The paper describes the RuSentNE-2023 evaluation devoted to targeted sentiment analysis in Russian news texts. The task is to predict sentiment towards a named entity in a single sentence. The dataset for RuSentNE-2023 evaluation is based on the Russian news corpus RuSentNE having rich sentiment-related annotation. The corpus is annotated with named entities and sentiments towards these entities, along with related effects and emotional states. The evaluation was organized using the CodaLab competition framework. The main evaluation measure was macro-averaged measure of positive and negative classes. The best results achieved were of 66% Macro Fmeasure (Positive+Negative classes). We also tested ChatGPT on the test set from our evaluation and found that the zero-shot answers provided by ChatGPT reached 60% of the F-measure, which corresponds to 4th place in the evaluation. ChatGPT also provided detailed explanations of its conclusion. This can be considered as quite high for zero-shot application.

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Section: Related Workmentioning

confidence: 99%

RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts

Golubev,

Rusnachenko,

Loukachevitch

2023

Computational Linguistics and Intellectual Technologies”

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“…Candidate source language selection through forward and backward strategies will increase compute requirements. To discover the effectiveness of semantic and syntactic parsing and the effects of subjective aspects on sentiment analysis, the authors at (Morio et al, 2022) performed a graphbased and seq2seq-based analysis with the help of a pre-trained language model and discovered that both research approaches perform well in extracting structured sentiment.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

2023

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Although Spanish is one of the most spoken languages in the world, it was only until very recently that the development of linguistic technologies for it had a strong boost. However, this is not entirely true for some of its Latin American variants, such as the Mexican Spanish, which show phonetic, and also some lexical and semantic differences with respect to peninsular Spanish. This talk will focus on presenting the development of NLP for Mexican Spanish, emphasizing the path taken through the organization of different evaluation campaigns. It will present some data about Mexican Spanish as well as about the impact of the organization of shared tasks in the context of IberLEF for the development of the NLP area in our country, first as a mechanism to motivate more students to get involved, and then as a vehicle to build resources and design and implement specific methods. The talk will conclude by exposing some of the obstacles faced, our main achievements, and some plans for the coming years.Bio: Manuel Montes is Full Professor at the National Institute of Astrophysics, Optics and Electronics (INAOE) of Mexico. His research is on automatic text processing. He is author of more than 250 journal and conference papers in the fields of information retrieval, text mining and authorship analysis. He has been visiting professor at the Polytechnic University of Valencia (Spain), and the University of Alabama (USA). He is also member of the Mexican Academy of Sciences (AMC), and founding member of the Mexican Academy of Computer Science (AMEXCOMP), and the Mexican Association of Natural Language Processing (AMNLP). In the context of the latter, he has been the organizer of

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Section: Related Workmentioning

confidence: 99%

Ushoshi2023 at BLP-2023 Task 2: A Comparison of Traditional to Advanced Linguistic Models to Analyze Sentiment in Bangla Texts

Khushbu,

Nur,

Ahmed

et al. 2023

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

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This article describes our analytical approach designed for BLP Workshop-2023 Task-2: in Sentiment Analysis. During actual task submission, we used DistilBERT. However, we later applied rigorous hyperparameter tuning and preprocessing, improving the result to 68% accuracy and a 68% F1 micro score with vanilla LSTM. Traditional machine learning models were applied to compare the result where 75% accuracy was achieved with traditional SVM. Our contributions are a) data augmentation using the oversampling method to remove data imbalance and b) attention masking for data encoding with masked language modeling to capture representations of language semantics effectively, by further demonstrating it with explainable AI. Originally, our system scored 0.26 micro-F1 in the competition and ranked 30th among the participants for a basic DistilBERT model, which we later improved to 0.68 and 0.65 with LSTM and XLM-RoBERTa-base models, respectively.

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Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis

Cited by 4 publications

References 21 publications

RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts

RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

Ushoshi2023 at BLP-2023 Task 2: A Comparison of Traditional to Advanced Linguistic Models to Analyze Sentiment in Bangla Texts

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