# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
## 4 Experiments
### Baseline Model  
The Flat-Lattice model makes full use of the parallel capability of the GPU to construct the head and tail for each character and vocabulary, reconstructing the original lattice structure, which can directly model the interaction between the character and all the vocabulary information that it matches and introduce the vocabulary information. Secondly, the relative encoding of character positions is obtained by successive transformations of the head and tail information, using dense vectors to model the relationship between them. Using relative position embedding, position information is assigned to each node; the distance and direction information of the characters is obtained to enhance the Transformer's direction perception ability, and the direction perception helps the characters to identify whether their neighbors constitute a continuous entity or not. Moreover, use Conditional Random Field (CRF) to decode the named entity to identify the label sequence.  
In order to assess that the proposed SISF model optimization vocabulary enhancement method in this paper is effective for Chinese-named entity recognition, this paper not only chooses Flat-Lattice as a comparison model but also selects some experiments in solving the above problems by using different methods and carries out a comparative analysis.  
1. FGN [31]: A novel CNN structure called CGSCNN is proposed to capture the interaction of glyph information between neighboring graphs, providing a method with a sliding window and an attentional mechanism to fuse the BERT representation and glyph representation of each character.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Hyper-Parameters** & **Ontonotes4.0** & **MSRA** & **Resume** & **Weibo** \\ \hline epoch & 100 & 100 & 100 & 100 \\ head & 8 & 8 & 12 & 12 \\ head\_dim & 20 & 20 & 16 & 20 \\ batch & 10 & 6 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of the dataset.  
2. Token-Relation [32]: proposes a masked self-attention mechanism to integrate the local contextual information of matched words and designs gated information controllers to deal with the conflict problem existing in matched words.
3. LR-CNN+BERT [9]: is a convolutional neural network-based approach that integrates the vocabulary through a rethinking mechanism to mitigate conflicts between matching repertoires of words in the lexicon.
4. PLTE+BERT [11]: introduces a novel porous mechanism to enhance the local dependency between neighboring characters.
5. SLK-NER [10]: This model mitigates conflicts in the matching vocabulary by developing a method to compute a weighted sum of lexical information and using it as an additional feature.
6. FLAT [7]: constructs head and tail for each character and vocabulary, reconstructs the original lattice structure, and can directly model the interaction between characters and all the vocabulary information they match. Reduces the loss of information where a character can only be fused with the vocabulary ending with that character and enhances named entity recognition.