# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 3 LSTM-CRF Model with the Integrity Algorithm
### Integrity Algorithm  
#### 3.3.1 Dependency Syntax  
To improve the integrity of element recognition, the dependency syntax relationship is introduced, which can capture the long-range collocation and modification relationships. This relationship is combined with the part-of-speech (POS) rules to filter out useless rules that occasionally occur. Then, the POS syntactic rules are organized to identify the complete structure of the elements. The LTP dependency syntax analysis based on the cloud platform ([[https://www.ltp-cloud.com/](https://www.ltp-cloud.com/)]([https://www.ltp-cloud.com/](https://www.ltp-cloud.com/))), each dependency is composed of core words and modifiers. The dependency relationship between two words is connected by a dependency arc, and the specific relationship between collocations is indicated by the marks on the dependency arc. Tags are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tags** & **Dependency Relations** & **Tags** & **Dependency Relations** \\ \hline SBV & Subject-verb & CMP & Complement \\ VOB & Verb object & COO & Coordinate \\ FOB & Fronting object & POB & Preposition object \\ DBL & Double & LAD & Left adjunct \\ IOB & Indirect object & RAD & Right adjunct \\ ATT & Attribute & IS & Independent structure \\ ADV & Adverbial & HED & Head \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tags of LTP-cloud Platform Dependency Syntax Analysis.  
By reviewing 1050 financial reports, we found that attributes are often composed of compound words, such as \"\(\Uparrow\)\" (profit growth rate). Even attribute values are clause structures, such as \"\(\Uparrow\)\" (the growth is close to 100%). Most of the attribute values in the reports appear in the form of objects, and there are also ATT and COO dependencies among the constituent units. Similarly, some fixed dependencies exist between the constituent units of attributes, such as in the following forward-looking sentence: \"\(\Uparrow\)\" (It is expected that the company's revenue and gross profit margin will continue to show a trend of rapid growth). The results of the dependency syntactic analysis are shown in Figure 3.  
The attributes of this forward-looking sentence are \"\(\Uparrow\)\" (revenue) and \"\(\Uparrow\)\" (gross profit margin), and the syntactic role they play in a sentence is providing the subject for the verb \"\(\Uparrow\)\" (show), namely, there is an SBV relationship. There is also a COO dependency between \"\(\Uparrow\)\" (revenue) and \"\(\Uparrow\)\" (gross profit margin). The attribute value element of the sentence is \"\(\Uparrow\)\" (show a trend of rapid growth), which is part of the subordinate clause, where \"\(\Uparrow\)\" (show) is the object of \"\(\Uparrow\)\" (expected), namely, a VOB relationship. Within the attribute value, there is an ADV relationship between \"\(\Uparrow\)\" (rapid) and \"\(\Uparrow\)\" (growth), a VOB relationship between \"\(\Uparrow\)\" (show) and \"\(\Uparrow\)\" (trend), and an ATT dependency relationship between \"\(\Uparrow\)\" (growth) and \"\(\Uparrow\)\" (trend).