# Named Entity Recognition Using Conditional Random Fields
## 7 Results and Discussion
### Results of NE Types  
Further analysis of the performance of the CRF-based approach was performed to investigate the impact of the frequency of each NE type on the performance of the proposed approach. In contrast to the preceding section, the details of the NE types for both datasets, namely, IJCNLP-Urdu and UNER-I, are available. Therefore, the results of both datasets are discussed separately.  
Table 12 presents the results of each NE type for the IJCNLP-Urdu dataset. It can be observed from the table that our proposed approach showed promising results for all the NE types with higher frequencies than the NE types with low frequencies. For instance, the two NE types with the highest frequencies were location and person, and these were also the two NE types that achieved the highest F1 scores. Similarly, the most scarce NE type was organization, with merely 48 instances and the proposed approach achieved the lowest F1 score for this NE type. Table 13 presents the results of each NE type for the UNER-I dataset. Similar to the results of the IJCNLP-Urdu dataset, the results of the UNER-I dataset indicated that the CRF-based approach surpassed the baseline approach by achieving a higher precision, recall, and F1 score.  
As discussed earlier, the news in the IJCNLP-Urdu dataset was collected from a single genre, whereas the UNER-I dataset included news from four genres. Therefore, the results of the UNER-I dataset were synthesized across genres and NE types. The synthesized results of the UNER-I dataset are presented in Table 14. It can be observed from this table that for the national news, our proposed approach surpassed the baseline model for all NE types. In contrast, for the news from the other three genres, our proposed approach surpassed the baseline model for a majority of the NE types, whereas it achieved  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**NE Type**} & \multicolumn{3}{c}{**Baseline Approach**} & \multicolumn{3}{c}{**Proposed Approach**} \\  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline Person & 81.94 & 94.82 & 87.16 & 82.84 & 95.14 & 87.94 \\ Location & 84.76 & 86.40 & 85.17 & 84.49 & 86.32 & 84.84 \\ Organization & 73.17 & 70.15 & 70.31 & 75.68 & 70.60 & 71.54 \\ Date & 79.66 & 63.71 & 68.38 & 82.00 & 66.77 & 70.77 \\ Designation & 83.18 & 80.28 & 80.403 & 86.163 & 80.12 & 81.50 \\ Number & 72.99 & 77.46 & 72.66 & 76.16 & 81.10 & 76.33 \\ Time & 55.64 & 46.59 & 46.42 & 55.35 & 50.08 & 48.72 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Entity-wise results of the UNER-I dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Genres**} & \multicolumn{3}{c}{**Baseline Approach**} & \multicolumn{3}{c}{**Proposed Approach**} \\  & **Precision** & **Recall** & **F-Measure** & **Precision** & **Recall** & **F-Measure** \\ \hline National & 87.14 & 81.01 & 82.32 & 88.21 & 84.05 & 84.68 \\ Sports & 75.48 & 73.39 & 73.23 & 77.44 & 75.02 & 74.92 \\ International & 73.45 & 71.66 & 69.65 & 75.84 & 72.62 & 71.56 \\ Art & 70.28 & 70.83 & 67.57 & 71.33 & 71.46 & 68.09 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of the UNER-I dataset across genres.  
a comparable score for the remaining types. In particular, for sports news, the proposed approach outperformed the baseline with higher margins for two NE types: organization and date. For international news, the F1 score achieved by our approach was substantially higher than the baseline for three NE types: designation, number, and time. Similarly, for the art news, only the date and number classes superseded the baseline model with a high margin.