# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 2 Related Works
### Aspect Term Extraction  
Earlier, the aspect term extraction task was mainly based on a rule and lexicon approach, proposing a series of unsupervised models. Later supervised statistical models were used to extract aspect terms, such as conditional random fields(CRF), hidden Markov model(HMM), etc.  
With the development of deep learning techniques, most researchers worked on developing various types of neural network models. The aspect term extraction task is similar to the named entity recognition task. Since the bi-directional long short-term memory (Bi-LSTM) model is a common model in named entity recognition tasks, it can be applied to the aspect term extraction task as well. Since words in a sentence have dependency information between them, recurrent neural networks are sequential models that cannot effectively capture the tree-based dependency information of a sentence. In order to effectively utilize the dependency information of sentences, [PERSON] et al. [6] proposed the DTBCSNN model to capture syntactic features by introducing a multilayer convolutional neural network based on the dependency tree. [PERSON] et al. [7] proposed a DE-CNN model to obtain word embeddings using generic embeddings and domain-specific embeddings and then used convolutional neural networks for aspectual term extraction. The DE-CNN model makes full use of domain knowledge to make the word vector more accurate, but cannot dynamically acquire contextual semantic information. Prior to 2018, word embeddings used Word2 Vec [8] or GloVe [9] models, which provide only context-independent word-level features, but this is insufficient to capture complex semantic dependencies in sentences. The advent of pre-trained models, such as BERT [10] allows for improved model performance by adding even simple linear classification layers to the BERT model structure. [PERSON] et al. [11] study the ability of the BERT model to model contextual embedding, which is validated in aspect-based sentiment analysis. Since words play different roles in different sentences, there are dependencies between different tags. [PERSON] et al. [12] proposed the BERT-GLCLD model to construct a global-local context representation using the BERT model and location-aware attention, and a label-dependent module based on recurrent neural networks and conditional random fields to constrain the boundaries of aspect terms. The BERT-GLCLD model focuses on both fine-grained and coarse-grained information, combining label information for prediction, but does not fully utilize contextual information, and the current word prediction relies not only on the previous word but also uses the latter word to assist in prediction. To enrich the word vector information, [PERSON] et al. [5] proposed the CASE model, which combines dependency-based embedding, contextual embedding, and lexical embedding to enhance the performance of the model. The CASE model introduces external knowledge and makes full use of various information, which increases the complexity of the model.  
Inspired by [PERSON] et al. [12], it is believed that global-local contextual information can understand different semantic meanings of words in different sentences. In this study, local context information is integrated using convolutional neural networks to extract features further.  
This study is the first to integrate local contextual information with the help of an equal-width convolutional neural network and to explore the potential of the new network structure for the aspect term extraction task.