# Named Entity Recognition Using Conditional Random Fields
## 2 Related Work  
The NER research for English has a long history that dates back to the early 1990s [4]. Since then, several studies have been conducted to address the NER problem, which ranges from rule-based techniques [7; 17] to purely supervised techniques [18]. Moreover, attempts were made to use hybrid approaches for NER in texts [1; 19]. It was observed that NER techniques that were developed for particular domains were typically not equally effective for other domains [20]. Similarly, the techniques developed for NER in one language may not be equally effective for other languages. For instance, a NER system developed for English is not useable for Hindi or Chinese. Most of the NER research has been done for the Western languages, whereas little attention has been paid to Urdu NER. An overview of the three types of NER techniques discussed in the preceding paragraph is as follows. The rule-based approaches, such as [7; 13], use a set of rules or patterns (i.e., grammars) that are designed using linguistics. In this type of approach, different rules are designed linguistically for each class of named entity. These rules are then executed on the given text. Whenever the system finds some text for recognition, it first searches for the named entity and then compares it with the rules that are used. Once the rule is matched, the system fetches the classification and gives the required output. An auspicious aspect of systems that are formulated by applying the linguistic-rule-supported approach is the guaranteed improved accuracy [7; 13; 21]. However, rule-based methods generally lack potency and manageability [17]. This is due to several reasons: (a) they need to be constantly updated with new guidelines, such as those of the corresponding domain modifications; (b) introducing guidelines for a specific task necessarily requires expertise in the corresponding language, along with experience in and knowledge of rule synthetization; (c) rules established for a language domain typically will not port toward other language subject areas; and (d) rule-based techniques take much longer to develop than other techniques. Currently, the most dominant techniques for tackling the NER problem are machine learning (ML) approaches [22]. The basic idea behind supervised models is their capability to automatically learn rules from training data. The recent trend of analyzing large datasets with supervised learning approaches is particularly reflective of the general trend toward machine learning methodologies [23; 24]. Supervised systems infer rules through pre-labeled input, referred to as training data, and conform to estimation methods, non-parametric, or kernel-based learning algorithms, as well as logic-based algorithms [23; 25]. NER frameworks based on machine learning are often more customizable than rule-based techniques [26]. An ML technique could adjust to new contexts with little cost provided that training data is freely available [18]. If large, annotated corpora are not available, then semi-supervised machine learning approaches are more effective. Semi-supervised approaches are frequently built on a learning technique that is bootstrapped [27; 28]. In this scheme, an initial seed model is first built by using a modest set of tokens with preset categories. This seed model is then used to classify all the tokens in the text. Classifications with high confidence are fed back into the training data in an iterative manner. Hybrid methods, as the name suggests, are usually a hybrid of rule-based and machine-learning systems [29], with a trade-off of strengths and weaknesses of both approaches. In the literature, the hybrid approach is followed for NER to overcome the challenges presented by the two earlier approaches once applied on an individual basis, and as a consequence, accomplish finer functioning [24]. To the best of our knowledge, no study has been conducted that employs a purely hybrid approach for the Urdu NER task. As a baseline model for this study, we used the works of [PERSON], [PERSON], and [PERSON] [15].