Revisiting the Importance of Encoding Logic Rules in Sentiment Classification (D18-1)
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| Challenge: | Neural models that explicitly encode word order, syntax and semantic features are unequipped to deal with complex syntactic structures that affect sentiment, such as contrastive conjunctions. |
| Approach: | They propose to use contextualized ELMo embeddings instead of logic rules to compare models on syntactically complex sentences. |
| Outcome: | The proposed models outperform baseline models even on sentences with ambiguous sentiment labels. |
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| Challenge: | Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture . previous studies focus on predicting the overall sentiment from original text using statistical or neural models, but these methods either heavily rely on human knowledge or suffer from the complex structure of the text. |
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| Challenge: | Existing methods for structured sentiment analysis (SSA) focus on subcomponents of sentiment graphs without explicitly expressing their relations or the polarity. |
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Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts (2021.acl-tutorials)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |