# Iterative Named Entity Recognition with Conditional Random Fields
## 1 Introduction  
The analysis of large amounts of unstructured text information can be supported and typed up by machine-learning-based methods. The successful use of such methods requires, however, the training of underlying machine learning models with accurately annotated or labelled text datasets [1]--that is, datasets containing metadata information on predefined information units. Model training needs to be conducted on domain-specific information or datasets that are appropriate to the information needs. For numerous applications regarding the identification and annotation of information units or entities such as persons, organisations, or location names in texts, a process called named entity recognition (NER) is highly relevant [2]. Accurate labelling of such entities is generally achieved by performing the annotation manually, because domain-specific knowledge is often required [3]--for example, to annotate medical terms across large amounts of medical records. Such manual annotation processing is, however, time-intensive, requiring a significant amount of human work. This becomes even more critical when analysing and annotating large numbers of text documents. Similar to other domains, the security sector also faces the challenge of analysing increasing amounts of heterogeneous information in an unstructured form. Reduction of the time and effort required to label text datasets has been sought by employing machine-learning-based methods to perform NER automatically. Comprehensive reviews on NER discussing traditional approaches as well as machine-learning- and deep-learning-based approaches for NER can be found in [2, 4]. State-of-the-art models to perform supervised NER include Conditional Random Fields (CRF) [3, 5, 6, 7] and LongShort-Term Memory Networks (LSTMs) [7; 8; 9; 10]. These have been reported to achieve good performance for English news texts (e.g., F1 Score of 0.9; [8; 9; 10]) but to perform poorer for German texts [11; 12]. Ref. [7] trained and tested CRFs and Bilateral LSTMS (BiLSTM) on a dataset composed of German legal documents. They reported that BiLSTMs achieved superior performance with F1-scores of 95.95 for coarse-grained NER classes, compared with the 93.23 achieved by CRFs; further, they reported on the effect of unbalanced entity representations on model performance. In addition to this, and despite the good performance reported, these models still need to be trained with manually labelled datasets in a supervised approach, with manual labelling remaining important due to domain specificities. This means that they only partially solve the need for reducing manual labour. In order to address this issue, previous works have complemented the above models with self-learning and or active learning approaches [13; 14]. The former starts with an initial small set of manually labelled sentences for training and extends this set iteratively with sentences that are automatically labelled by the model in an unsupervised way. Generally, sentences with a high likelihood of a correct annotation are added. For instance, for probabilistic models such as CRFs, sentences are added with a high probability of correct classification of entities by the model [14; 15]. Implementation of self-training, however, depends on several parameters, and generalisation of the trained models is not given [16]. In active learning, sentences are chosen from the pool of data for manual annotation before being added to the set of sentences for training. Sentences are selected by the model such as to maximise information learning by the model and minimise the amount of manual annotation required for training. This approach has been successfully employed by [PERSON] and colleagues on Twitter posts in English [17]. The extent to which this method can contribute to substantially reduce the manual annotation of texts in other domains or languages (e.g., German) remains unclear, since the language usage and diversity of information that are dealt with are very different from that of Twitter posts.