# Named Entity Recognition Using Conditional Random Fields
## 5 Urdu Uner Datasets
### The UNER-I Dataset  
This section discusses our second contribution, i.e., the development of a large Urdu NER dataset, namely, UNER-I. The developed dataset is substantially superior to its counterparts across three key dimensions: NE count, token count, and diversity of text. In terms of the NE count, the number of named entities in IJCNLP-Urdu is merely 1115, whereas the newly developed UNER-I dataset included 5283 NEs, which was nearly a five-fold increase. For the second dimension, i.e., token count, the IJCNLP-Urdu dataset is composed of 48,673 tokens, whereas the newly developed UNER-I dataset was composed of 58,633 tokens, which was a substantial increase. Finally, in terms of the diversity of the text, the IJCNLP-Urdu dataset includes text from a single genre, whereas UNER-I included Urdu text from four genres: national news, international news, sports news, and art news. In particular, it included 50 international news, 60 national news, 40 sports news, and 50 art news articles. Additionally, to ensure the quality of the annotation, the random samples from the four domains were reviewed by two independent Urdu language experts. \begin{table}
\begin{tabular}{c c} \hline Total No. of Words & 40,408 \\ Total No. of Sentences & 1097 \\ Total No. of Named Entities & 1115 \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of the Specification of the IJCNLP-Urdu Dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**NE Type** & **Count** \\ \hline Person & 277 \\ Location & 490 \\ Organization & 48 \\ Designation & 69 \\ Date & 123 \\ Number & 123 \\ Total & 1115 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Entity-Wise Statistics of the IJCNLP-Urdu Dataset. Three annotators investigated the UNER-I dataset. The first two annotators separately labeled the named entity tags in each sentence, while the third annotator resolved any conflicts. Only the sentences in which the first two annotators disagreed were shown to the third annotator. The interface showed the tags that the first two annotators allocated and allowed the final tag decision to be made by the third annotator. Three annotators physically tagged the UNER-I dataset. All the experts were fluent Urdu speakers with strong speaking ability and expertise with the NER task. Annotators 1 and 2 tagged a sample of 100 phrases in the first stage, the inter-annotator agreement was estimated, and their annotations were examined in detail. The remainder of the dataset was annotated by annotators 1 and 2, and the inter-annotator agreement was calculated for the complete dataset in the second stage. The third annotator annotated sentences where the first two annotators differed. The cumulative kappa [39] result was 0.80 and the inter-annotator agreement was 89.1 percent, where kappa is used as a standard measure of interrater agreement that assesses the extent of agreement between two variables. During the development process, all the entities were tagged from right to left and the text was stored as sentences. Typically, a \"_\" sign was used to mark the sentence boundary, whereas in some cases, a \"?\" sign was also used to mark the sentence boundary. The entity boundary was marked with start and end tags. The tagged data was stored in a notepad using UTF-8 encoding. Furthermore, the text in the files was organized sentence-wise because most of the machine learning models take input in a sentence-wise manner, making it a sequence-labeling problem. A notable characteristic of UNER-I is that it is freely available for research purposes. Therefore, we contend that it will be a valuable resource for promoting NER research in Urdu, which is a low-resource language. The summary of the characteristics of our UNER-I dataset is presented in Table 8, whereas Table 9 contains the detailed statistics of the UNER-I dataset. In particular, Table 9 contains the distribution of NE and their respective genres.