| Challenge: | Existing methods for opinion expression detection are based on token-level sequence labeling . |
| Approach: | They propose to use BERT and conditional random field embedders to detect opinion expressions. |
| Outcome: | The proposed model outperforms ELMo embedders in opinion expression detection. |
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| Challenge: | Existing methods for argument mining focus on analyzing local argumentation structures, but information-seeking approaches need to be able to deal with heterogeneous sources and topics. |
| Approach: | They propose to use contextualized word embeddings to classify and cluster topic-dependent arguments using a UKP Sentential Argument Mining Corpus and IBM Debater - Evidence Sentences datasets. |
| Outcome: | The proposed method improves state-of-the-art on argument classification and clustering tasks and across multiple datasets. |
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction (2021.emnlp-main)
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| Challenge: | Current methods for extracting opinion words for an aspect in text leverage position embeddings to capture relative position of word to the target. |
| Approach: | They propose to use pretrained word embeddings to extract opinion words for a given aspect in text. |
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Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (D19-55)
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| Challenge: | Existing studies on ABSA use a sequence tagging problem to extract aspect-specific opinion words from the sentence given the aspect. |
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Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States (2024.lrec-main)
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Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, SeyedAbolghasem Mirroshandel, Owen Rambow
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Mining Tweets that refer to TV programs with Deep Neural Networks (D19-55)
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| Challenge: | opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy . |
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Contextual Embeddings: When Are They Worth It? (2020.acl-main)
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| Challenge: | In recent years, rich contextual embeddings have enabled rapid progress on benchmarks like GLUE, but require significant computational resources during pretraining and during downstream task training and inference. |
| Approach: | They empirically compare contextual embeddings with classic pretrained embedders and a random word embeddable with a simple baseline. |
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An Empirical Examination of Online Restaurant Reviews (2020.lrec-1)
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| Challenge: | Existing methods for opinion mining and sentiment analysis focus on extracting either positive or negative opinions from texts and determining the targets of these opinions. |
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Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (P18-2)
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| Challenge: | Recent supervised deep learning models have achieved state-of-the-art performance, but there are two other considerations that are important. |
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Contextual String Embeddings for Sequence Labeling (C18-1)
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| Challenge: | Recent advances in language modeling have made it viable to model language as distributions over characters. |
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Can Large Language Models be Effective Online Opinion Miners? (2025.emnlp-main)
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| Challenge: | OOMB is a novel benchmark designed to assess the ability of large language models (LLMs) to extract and analyze opinions from diverse and complex online environments. |
| Approach: | They propose an online opinion mining benchmark to assess the ability of large language models to extract and analyze opinions from diverse online environments. |
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