| Challenge: | Existing studies on aspect extraction focus on sequence tagging models trained on human-annotated data. |
| Approach: | They propose a novel neural model capable of coupling global and local representations to discover aspect words by combining global and locale contexts. |
| Outcome: | The proposed model outperforms state-of-the-art models on laptop and restaurant reviews on two benchmarks. |
Similar Papers
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020.aacl-srw)
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| Challenge: | Existing approaches to extract aspects from text are supervised and unsupervised . experimental results show that unsupervised approaches are more accurate than supervised ones . |
| Approach: | They propose to combine a lexical rule-based approach with coreference resolution to improve accuracy. |
| Outcome: | The proposed approach outperforms baseline methods on two benchmark datasets. |
GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction (2020.coling-main)
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| Challenge: | Supervised-learning approaches fail to scale across domains where labeled data is lacking. |
| Approach: | They propose a method for incorporating external linguistic knowledge into a self-attention mechanism coupled with a transformer-based model. |
| Outcome: | The proposed method enables leveraging syntactic knowledge from transformer-based models to bridge the gap between domains. |
Embarrassingly Simple Unsupervised Aspect Extraction (2020.acl-main)
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| Challenge: | Existing systems for aspect extraction are supervised, but are unlikely to transfer well between domains. |
| Approach: | They propose a novel approach that uses an RBF kernel to generate a single-head attention mechanism for aspect extraction from text. |
| Outcome: | The proposed method is based on an RBF kernel and can be applied to new domains and languages. |
Enhancing Aspect Term Extraction with Soft Prototypes (2020.emnlp-main)
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| Challenge: | Existing studies focus on designing neural sequence taggers to extract linguistic features from token level. |
| Approach: | They propose to correlating aspects with each other through soft prototypes . they propose to combine ATE with almost all sequence taggers to extract aspect terms . |
| Outcome: | The proposed model boosts the performance of three typical ATE methods on four SemEval datasets. |
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)
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| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
| Approach: | They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor. |
| Outcome: | The proposed solution outperforms the state-of-the-art models on SemEval-2014 dataset in both subtasks. |
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction (P18-1)
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| Challenge: | supervised learning methods have been used for fine-grained opinion analysis but lack of labeled data hinders learning . authors develop a recursive neural network that could reduce domain shift in word level . a recent paper shows that unsupervised methods fail to adapt well across domains . |
| Approach: | They propose a supervised neural network that reduces domain shift effectively in word level . they treat these relations as invariant "pivot information" across domains to build structural correspondences . |
| Outcome: | The proposed model reduces domain shift effectively in word level through syntactic relations . it can be used to predict the relation between two adjacent words in the dependency tree . |
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
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| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that unifies word discovery, learning how words fit together to form sentences, and by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction (2021.findings-emnlp)
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| Challenge: | Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. |
| Approach: | They propose a tightly coupled two-stage approach to extract latent user sentiments and item properties from reviews and an Attention-Property-aware Rating Estimator (APRE). |
| Outcome: | Extensive experiments on seven real-world Amazon review datasets show that the proposed approach extracts the latent user sentiments, item properties, and the complicated interactions between the two components. |
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. |
| Approach: | They propose a supervised aspect extraction model using general-purpose embeddings and domain-specific embeddables. |
| Outcome: | The proposed model outperforms state-of-the-art methods without supervision and achieves very good results. |