Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
arXiv:1805.04601
Abstract
One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.
arXiv commentACL 2018
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Convolutional Sequence to Sequence Learning
- Convolutional Neural Networks for Sentence Classification
- Enriching Word Vectors with Subword Information
- Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction
- Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
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Cited by in corpus (5)
- Natural Language Processing Advancements By Deep Learning: A Survey
- A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
- EmotionX-KU: BERT-Max based Contextual Emotion Classifier
- Lifelong Domain Word Embedding via Meta-Learning
- Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks