An Unsupervised Neural Attention Model for Aspect Extraction
By Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier. Annual Meeting of the Association for Computational Linguistics, 2017
393 citations recorded by the source corpus.
Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discovering coherent aspects. The model improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings. Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space. In addition, we use an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects. Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects, and substantially outperforms baseline methods on several evaluation tasks.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/c9faf8c7c8e1f13b7a14a86b57c6432c4fd953eb
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Citations in the corpus
- Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2019, methodology): “Note that we define “aspects” as implicit aspect categories, namely clusters of associated attributes of product, which is commonly used in unsupervised aspect extraction (Wang et al., 2015; He et al., 2017).”
- Deep learning for sentiment analysis: A survey (2018, background): “He et al. 86 proposed an attention-based model for unsupervised aspect extraction.”
- Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (2019, background): “The ‘Extract’ circle linked to aspect term denotes ATE, such as (Qiu et al. 2011; Liu, Xu, and Zhao 2013; 2014; Liu, Joty, and Meng 2015; Yin et al. 2016; Wang et al. 2016a; 2017; He et al. 2017; Li and Lam 2017; Li et al. 2018b; Xu et al. 2018).”
- Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training (2019, methodology): “Recently, neural topic models (Iyyer et al., 2016; Srivastava and Sutton, 2071; He et al., 2017) have been shown to produce more coherent topics than earlier models such as Latent Dirichlet Allocation (LDA) (Blei et al.”
- Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018, methodology): “, 2017) and unsupervised approaches (Hu and Liu, 2004; Zhuang et al., 2006; Mei et al., 2007; Qiu et al., 2011; Yin et al., 2016; He et al., 2017).”
- A General Survey on Attention Mechanisms in Deep Learning (2022, background): “In comparison to the total amount of research that has been done on attention models, research on attention models for semi-supervised learning [123], [124] or unsupervised learning [125], [126] has received limited attention and has only become active recently.”
- An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals (2020, methodology): “To achieve these, we adopt the contrastive max-margin loss which has been used in many studies [54], [55].”
- Aspect-Controllable Opinion Summarization (2021, background): “Opinion mining has assumed several guises in the literature such as sentiment analysis (Pang et al., 2002), aspect extraction (Hu and Liu, 2004; He et al., 2017), combinations thereof (Mukherjee and Liu, 2012; Pontiki et al., 2016), and notably opinion summarization (Hu and Liu, 2006; Wang and…”