## 6 Results and Analysis
### Error Analysis of the Without-PoS-DLA Approach  
Table 9 shows the statistical details of the error analysis of the without-PoS-DLA approach concerning single-word and multi-word expressions. A total of 2023 words were incorrectly lemmatized, out of which 45% were single-words, and 55% were multi-word expressions. The possible reason for the low performance (55% incorrectly classified instances) of multi-word expression was the presence of junk, compound words that cause a boundary identification issue during segmentation, and proper nouns. As expected, this may cause a decrease in the proposed system's performance. Similarly, Table 10 shows the statistical details of error analysis of the without-PoS-DLA approach concerning PoS tags. A total of 2023 words were incorrectly lemmatized, out of which 39% had noun tags (NNs), 9% were proper nouns (PNs), 3.5% were case maker words (P), 5% were personal pronoun (PPs), 15% were verbs (VBs), 6.3% were adjectives (ADJs), 5.5% were aspectual auxiliaries (AAs). The remaining words had other tags. The portion of words with noun tags (NNs) is significant because of the PoS tags assigned during the annotation process. When we use words with their respective PoS tags, it might \begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Analysis** & **Total Words** \\ \hline Total Incorrectly Lemmatized Words & **2852** \\ \hline Incorrectly Lemmatized Single Word Expression & **1864 (65\%)** \\ \hline Incorrectly Lemmatized Multi-Word Expression & **988 (35\%)** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Error analysis of the with-PoS-DLA approach with respect to the single-word and multi-word expressions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PoS Tag** & **Error** & **PoS Tag** & **Error** \\ \hline PD & 43 (1\%) & OR & 0 \\ \hline RD & 29 (1\%) & FR & 3 (0.07\%) \\ \hline KD & 0 & MUL & 0 \\ \hline AD & 0 & U & 0 \\ \hline NN & 954 (36\%) & CC & 33 (1\%) \\ \hline PN & 306 (11\%) & SC & 65 (2\%) \\ \hline PP & 136 (8\%) & I & 17 (0.59\%) \\ \hline RP & 8 (0.28\%) & AP & 0 \\ \hline REP & 2 (0.07\%) & KER & 0 \\ \hline AD & 0 & PRT & 0 \\ \hline KP & 0 & POT & 0 \\ \hline AKP & 0 & P & 366 (13\%) \\ \hline GR & 11 (0.3\%) & SE & 0 \\ \hline G & 0 & WALA & 9 (0.3\%) \\ \hline VB & 367 (13\%) & NEG & 16 (0.4\%) \\ \hline ADJ & 200 (7\%) & INT & 9 (0.3\%) \\ \hline Q & 47 (1\%) & QW & 0 \\ \hline AA & 164 (8\%) & SM & 9 \\ \hline TA & 0 & PM & 0 \\ \hline ADV & 58 (2\%) & DATE & 0 \\ \hline CA & 0 & EXP & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Error analysis of with-PoS-DLA approach with respect to the PoS tags. be possible that the respective word is present in the dictionary with another PoS tag. Some of the potential causes we delve into are the following:  
1. **Morphological variations**: The complex morphological structure of the Urdu language, which includes prefixes, suffixes, and infixes, can result in multiple valid forms of a single lemma. This can lead to mismatches in the dictionary lookup process. 2. **Ambiguity in tokenization**: Tokenization in Urdu can be challenging due to the lack of clear word boundaries and the presence of compound words. This can cause issues in accurately identifying individual words, leading to errors in the lemmatization process. 3. **Out-of-vocabulary words**: Words not present in the dictionary due to the dynamic nature of language or limited dictionary coverage can result in errors, as they cannot be matched to any lemma. 4. **Homographs**: Urdu words with the same spelling but different meanings can confuse the lemmatization process, as it may be difficult to determine the correct lemma without proper context. 5. **Proper nouns and named entities**: The vast number of proper nouns and named entities in the language makes it challenging to include them all in the dictionary, leading to reduced performance when encountering these words. Therefore, these reasons explain the decrease in the accuracy of the proposed system.