## 1 Introduction  
Morphological analysis studies the structure of the words in a language, and lemmatization is a major part of this. The lemmatization process aims to identify the root form of a word from its inflected form(s). Inflected words are words that are derived from the actual root word. The root form is called the lemma of a word. Essentially, lemmatization provides a word's root or base form. For example, in English, the words \"shine\" and \"shining\" produce the lemma \"shine\". Similarly, in the Urdu language, the words \"\(\lambda\hat{\mathcal{G}}_{\tau}^{\prime}\)\" (shining) and \"\(\iota\hat{\mathcal{G}}_{\tau}^{\prime}\)\" (shines) reduce to \"\(\zeta\)\" (shine) as their lemma. Part-of-Speech (PoS) tags play an important role in the lemmatization process, as a strong relationship exists between the lemmas and PoS tags [1,2]. Due to the rich morphology of the Urdu language, one word may have more than one PoS tag. So, with the change of a PoS tag, the lemma of a word also changes. For example, in English, the word \"fly\" can be a noun or verb. Similarly, in the Urdu language \"\(\iota\hat{\mathcal{G}}_{\tau}^{\prime}\)\" can be a noun as well as a verb. If \"\(\iota\hat{\mathcal{G}}_{\tau}^{\prime}\)\" (food) is a noun, its lemma is \"\(\zeta\)\", and if \"\(\iota\hat{\mathcal{G}}_{\tau}^{\prime}\)\" (eat) is a verb, its lemma is \"\(\zeta\)\". Lemmatization has potential applications in many Natural Language Processing (NLP)tasks, including Information Retrieval (IR), Word Sense Disambiguation (WSD), Machine Translation, and Text Reuse Detection. Lemmatization of morphologically rich languages has significant influence compared to that of less inflectional languages. A single word in morphologically rich languages can have many inflectional forms. The Urdu language contains words from many different languages, such as Persian and Turkish. This makes Urdu a complex and highly rich morphological language. Further, it is one of the most important languages in South Asia, as it is spoken by more than 175 million people in Pakistan, India, and other South Asian countries [3; 4; 5]. Lemmatization algorithms are categorized into two approaches: (1) manual approach (2) automatic approach [6]. In the manual approach, lemmas are generated manually by a human. It is also called the dictionary-based approach. This approach is accurate because it involves humans, and there are fewer chances of error or wrongly predicting the lemma, but it takes much effort. It is impossible to use this approach on a large amount of data. On the other hand, in the automatic approach, lemmas are generated by using rules. Therefore, it is also called the rule-based approach. This approach takes less time, but it is not as accurate as the manual approach. As part of our contribution to NLP research in the Urdu language, this study presents an Urdu lemmatization system, based on a manually developed dictionary-based approach. In this approach, every word of Urdu contains its PoS tag and its suitable root word (lemma). The proposed dictionary contains 25,000 unique words with their most frequent parts of speech tags. The main contributions of this research are described below. * The first significant contribution of this study is the development of a large benchmark corpus for the Urdu dictionary. The proposed corpus was developed in the following steps: (1) data collection from two different sources, urnono corpus [7] and Wikipedia dump; (2) pre-processing and tokenizing of the collected data; (3) frequency counts of words; (4) selection of the most frequent words; (5) assignment of parts of speech tags to the selected words; (6) manual annotation of data (assignment of lemma to each word); (7) standardization of CSV storage format. * The second significant contribution is in the exploration of the relationship between the PoS tag and the lemma of a word. This is achieved through training the PoS tagger and assigning the most frequently used tag to the word.