## 5 Experiments
### Experiment Result  
We used the following methods as comparative experiments:  
SKIPGRAM: [PERSON] (2007) first proposed this model of training word embedding. The SKIPGRAM model is intended to predict the surrounding words in the sentence given the current word. CWE: [PERSON] (2015) proposed Character-Enhanced Word Embedding. CWE considers character embedding in an effort to improve word embedding. CWEP: [PERSON] (2015) proposed the Position-based Character Embedding model. In the position-based CWE, various embeddings of each character are differentiated by the character position in the word, and the embedding assignment for a specific character in a word can be automatically determined by the character position. Char-BLSTM-CRF: [PERSON] (2015) proposed a neural model for predicting a tag for each character using word and character information. This model is like [PERSON]'s CWE but with a different method to concatenate the word embedding and the character embedding. Segment-Level Neural CRF: [PERSON] (2016) presented Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. According to this article, the effect is slightly better than [PERSON]'s LSTM-CNN-CRF model. The experimental parameters of the model refer to this article. In addition, this experiment also provides three methods, including the use of two additional dictionary features. However, due to the lack of a dictionary in Chinese, dictionary features cannot be added. Table 3 compares several advanced methods with our methods. It is divided into two parts: Word-based and Word-Character-based. **Word-Based Models**: Segment-level Neural CRF improved the CRF layer on the basis of LSTM-CNN-CRF, and then achieved a better effect. However, due to the relative complexity of the CRF model and the CNN layer, the time required was much longer than other models. **Word-Character-Based Models**: The combination of word and character embedding is one of the main research directions at present. The four different methods are mainly due to the different combinations of word and character embedding. According to the last chapter, not all words are similar in meaning to their characters, so our method can achieve better performance. In addition, we considered the semantic feature of words in sentences, so the effect could be better than other methods. Of course, we used attention mechanisms when combining word embedding and character embedding and added a BLSTM layer when combining character embedding, so our method took about 10% more time than the Segment-level Neural CRF method. Table 4 mainly analyzes the F1 value of each event argument in several methods. As we can see, the Segment-level Neural CRF method is the best method for identifying the location, while our method has the best effect on several dimensions: event time, trigger, and event participants. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(SKIPGRAM)** & **Segment-Level** & **(CWE)** & **(CWEP)** & **Char-** & **Our** \\  & **BLSTM-CRF** & **Neural CRF** & **BLSTM-CRF** & **BLSTM-CRF** & **BLSTM-CRF** & **Method** \\ \hline
**Input** & \multicolumn{2}{c}{**Word-Based**} & \multicolumn{4}{c}{**Word–Character-Based**} \\ \hline R & 74.963 & 78.153 & 75.855 & 77.231 & 75.279 & 80.574 \\ P & 70.455 & 76.864 & 73.668 & 74.495 & 73.597 & 79.856 \\ F & 72.709 & 77.509 & 74.762 & 75.863 & 76.438 & 80.215 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance complexities.We used recall (R), precision (P) and the F-measure (F) to evaluate the results. Table 5 shows the averaged word length after splitting an event into event arguments. As we can see, trigger is the shortest and location is the longest. Therefore, CNN can extract more information from location. This is the reason why Segment-level Neural CRF (BLSTM-CNNs-CRF) performs better than BLSTM-CRF in location. We also conducted a statistical analysis to mark error information.