## 5 Experiments
### Experiment Results  
We compared our proposed approach TPORE with several competitive baselines for Chinese open relation extraction. UnCORE [18] exploits using word distance and entity distance constraints to generate candidate relation triples from the raw corpus and then adopts global ranking and domain ranking methods to discover relation words from the candidate relation triples. ZORE [37] is a syntactic-based system, which identifies relation candidates from automatically parsed dependency trees, and then extracts relations with their semantic patterns iteratively through a novel double propagation algorithm. PGCORE [29] casts relation extraction as a text summary task and proposes an end-to-end abstract Chinese Open RE model based on the Pointer-Generator Network. SpanOIE [30], instead of the previously adopted sequence labeling formulation for n-ary OpenIE, first finds predicate spans and then takes a sentence and predicate span as input and outputs argument spans for this predicate. MGD-CNN [35] constructs a multi-grained dependency (MGD) graph with dependency edges between words and soft-segment edges between words and characters and updates node representations using a deep graph neural network to fully exploit the topology structure of the MGD graph and capture multi-hop dependencies. Table 3 shows the experiment results of ZORE, UnCORE and PGCORE, and we used the results in their original papers. As shown in Tables 3 and 4, experiment results in COER are much higher than SpanSAOKE; even an exact match is used in the COER dataset. By analyzing the data, we found that cases in SpanSAOKE are much more sophisticated than COER. First, the average sentence length of the COER is 21, while the average sentence length of SpanSAOKE is 46. Previous work can capture context information, even using the LSTM model, and obtain a higher precision in the COER dataset. We also statistically analyzed the case that one sentence contains multiple triples; in COER, 25% percentage of sentences contain multiple triples, while in SpanSAOKE, the percentage is 53.9%. Since our model is good at dealing with the situation of multiple triples in one sentence, compared with previous methods, our method can greatly improve recall and F1 with comparable precision. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & \multicolumn{3}{c}{**SpanSAOKE(Partial Match)**} \\ \hline  & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline ZORE & 37.45 & 10.63 & 16.56 \\ \hline CharLSTM & 40.40 & 45.40 & 42.70 \\ \hline SpanOIE & 41.80 & 44.30 & 43.00 \\ \hline MGD-GNN & 45.00 & 47.10 & 46.00 \\ \hline Our model & 62.03 & **49.24** & **54.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of each method on SpanSAOKE. Some experimental results are directly quoted from previous comparative experiments. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & \multicolumn{3}{c}{**COER(Exact Match)**} \\ \hline  & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline ZORE & 83.76 & 14.47 & 24.88 \\ \hline UnCORE & 80.57 & 47.62 & 59.86 \\ \hline PGCORE & 85.37 & 54.28 & 66.36 \\ \hline Our model & 83.45 & **76.15** & **79.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of each method on COER. Some experimental results are directly quoted from previous comparative experiments. Table 4 shows the experiment results of ZORE, charLSTM, Span OIE, MGD-GNN, and for charLSTM, Span OIE and MGD-GNN, we used the results in their original papers. Table 4 shows the experiment results in the SpanSAOKE dataset; our model obtained the best performance in P, R and F1. The model learned the paradigm of each element in ORE, as a large number of relations that are not present in training data are extracted correctly in test data. Our model can solve various nesting problems, while other works cannot handle the situation where there are multiple entities and relations in a single sentence very well. This is also the main reason why the performance of our model is significantly higher than in previous works.