# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 5 Conclusions  
In summary, our research focuses on firm reports in the financial field. FLSs are used as objects to recognize valuable financial information, such as entities, attributes, and attribute values. Considering the three different types of elements, a synchronous recognition strategy with the advantages of dependency syntax is incorporated to capture the structure of elements and define POS syntactic rules based on the contexts of attributes and attribute values. Then, the integrity algorithm is used to correct the boundaries of the LSTM-CRF model labeling results. Finally, without losing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Set**} & \multirow{2}{*}{**Elements**} & \multicolumn{3}{c}{**Accurate Evaluation**} & \multicolumn{3}{c}{**Coverage Evaluation**} \\ \cline{3-7}  & & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline \multirow{3}{*}{1} & En & 57.35 & 41.20 & 47.95 & 65.21 & 50.82 & 57.12 \\  & Attr & 80.62 & 58.23 & 67.62 & 85.14 & 67.44 & 75.26 \\  & Val & 83.10 & 72.91 & 77.67 & 88.62 & 75.23 & 81.38 \\ \hline \multirow{3}{*}{2} & En & 53.37 & 39.50 & 45.40 & 61.67 & 45.20 & 52.17 \\  & Attr & 69.93 & 43.46 & 53.61 & 79.95 & 61.74 & 69.67 \\  & Val & 78.03 & 79.52 & 78.77 & 84.29 & 80.59 & 82.40 \\ \hline \multirow{3}{*}{Avg} & En & 55.36 & 40.35 & 46.68 & 63.44 & 48.01 & 54.65 \\  & Attr & 75.28 & 50.85 & 60.62 & 82.55 & 64.59 & 72.47 \\ \cline{1-1}  & Val & 80.57 & 76.25 & 78.22 & 86.46 & 77.91 & 81.89 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Field cross-recognition results for the Our method (%).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **F** \\ \hline CRF & 60.65 \\ LSTM-CRF & 64.19 \\ Char-LSTM-CRF & 72.56 \\ LSTM-CNNs-CRF & 73.09 \\ Our method & 72.87 \\ LSTM-CNNs-CRF + Integrity algorithm & 75.21 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative experiments of attribute (%). The F value is calculated under the precise evaluation.  
the recall rate, the accuracy of the model is improved by correcting the integrity of the element, thereby optimizing the model performance. In addition, experiments in different fields are repeated. The experiments showed that the proposed model displays good domain independence and can be easily applied in various fields. Integrity algorithms can also be easily combined with neural network models to avoid relying solely on being data driven.  
The next steps in this research are as follows. First, we will continue to conduct research on the boundaries of elements and further improve the effectiveness of recognition. Second, because information on the Internet is both genuine and fake, methods of distinguishing between genuine and fake information and selecting high-value information should be investigated. Third, determining how to use the identified elements to interpret the current status of a company and provide decision support and early warnings will be a focus of upcoming research.  
Methodology, experimental analysis, and paper writing, R.G.; The work was done under the supervision and guidance of [PERSON] and [PERSON].  
This work is sponsored by Natural Science Foundation of Shanghai, Project Number: 16 ZR1411200.  
The authors declare no conflict of interest.