# A Local Information Perception Enhancement-Based Method for Chinese NER
## 3 Approach
### Sequence Encoding Layer  
#### 3.2.1 Short-Sequence CNN with Sliding Window  
[PERSON] and [PERSON] considered that by relying solely on word embeddings, it is unable to utilize explicit character-level features, such as prefixes and suffixes [20]. They, therefore, proposed a hybrid model of bidirectional LSTMs and CNNs that learns both character- and word-level features. This approach is of particular interest because it both inputs word-level embeddings and handles the characters of each word. This approach inspired us to use LSTMs to process entire sequences and extract features simultaneously for shorter subsequences. To capture character-based nuances in short sequences, we designed a sliding window module that sequentially divides the sentence into multiple shorter subsequences. At the same time, CNN is good at capturing local features (such as n-grams or short sequences). Combined with LSTM's ability to solve the long-term dependency problem, our sequence encoding layer can effectively balance the need for global and local sequence information.  
Convolutional neural networks have a natural advantage for local feature extraction due to their sliding convolutional computation. The detection of entity boundaries can be considered as detecting local semantic mutations in text, similar to image contour detection. Thus, we propose the short-sequence CNN module (SSCNN) to enhance local information extraction. As illustrated in Figure 8, we construct a sliding window of front zone, zone, and back zone to generate a subsequence \(\{z_{i-1},z_{i},z_{i+1}\}\) of length 3 for each character and the left and right adjacent characters, and then apply it to a convolutional neural network. The antecedent and consequent relationships between each character in this subsequence and its neighboring characters will contain \(z_{i}\)-rich local contextual information. This operation is characterized by the fact that the local features of each character can be highlighted more clearly without being corrupted by long-distance information.  
Figure 8: Short-sequence CNN with sliding window.  
When the sentence length is \(n\), assume that \(z_{i}\in\mathbb{R}^{d}\) is the corresponding d-dimensional vector of the \(i\)-th character. Furthermore, some sentences need to add padded operations. The convolution operation involves the filter \(w\in\mathbb{R}^{hd}\). Then filters are applied to a single character (\(h=1\)) in each subsequence \(\{z_{i-1},z_{i},z_{i+1}\}\) generated by the sliding window (front zone, zone, back zone) to generate new features. For instance, feature \(x_{i}\) is generated from the window of character \(z_{i}\), by \(x_{i}=f(w\cdot z_{i}+b)\), where \(f\) is the hyperbolic function and \(b\in\mathbb{R}\) is the bias. Use this filter operation for all the windows within the sentence. Each sub-sequence \(\{z_{i-1},z_{i},z_{i+1}\}\) generates a feature map of \(x=\{x_{i-1},x_{i},x_{i+1}\}\) and \(x\in\mathbb{R}^{i-1,i+1}\). Then, we used a maximum pooling operation on each feature map to capture the essential features [33]. They finally obtained a brand-new sequence \(Z^{{}^{\prime}}=\left\{Z^{{}^{\prime}}_{1},Z^{{}^{\prime}}_{2},\ldots,Z^{{}^{ \prime}}_{n}\right\}\) containing local feature information.