## 3 Natural Language Processing
### Named-Entity Recognition  
The first step is to detect a word or phrase that forms entities, where each word represents a token, e.g., \"The bluebirds fly high\" is an entity formed of four tokens. One form of labelling is inside-outside-outside-principle, which allows the indication of where words begin and end [13]. The second step is the generation of entity categories. These categories allow the algorithms to identify the different ways of writing a certain type of entity, e.g., dates could be \"mm/dd/yyyy or yy/mm/dd\". An NER tool allows text to be tagged according to its context, and this tagged text is assigned a label to differentiate it from other categories. NER models, in their most basic version, seek to identify only some types of entities such as people, organisations, or places, however, models can also be found that allow the identification of streets and dates, among others. Figure 3 shows an example of the recognition of entities in a given text carried out by an NER tool which is able to identify in the text certain patterns that refer to people (_PER_), locations (_LOC_), dates (_DATE_), and organisations (_ORG_). An important aspect of NER models is that previously they have only been able to focus on a single language such as Spanish, English, or Portuguese and a single subject such as sports, news, or justice. An important aspect to consider to be successful is to have quality labelling for learning models, such as evaluation. A labelled corpus is a theme-specific text containing annotations of one or more entities [14]. Table 1 shows a summary of the datasets and tools to perform the named-entity recognition task. Recently, the data sources used for corpus creation include text conversations such as Twitter comments, film reviews, or social media comments. In addition, Wikipedia articles allow the number of tags per corpus type to increase. Figure 3: Example of Labelling Entities. Figure 2: General Diagram of Operation of Named-Entity Extraction and Relationship Extraction. Thanks to the use of modern data sources such as digital encyclopaedias or Twitter, it is possible to increase both the number of tags and the size of corpora for training NLP models. An example of this is the WikiFiger dataset [22], which has 112 tags of different topics. This improvement has also been noticed in corpora with the same subject matter, for example in corpora dedicated to medicine, NCBI disease [17], with 14 tags compared to GENIA [19], which has 36 tags. In addition to the datasets available to train your own models, there are now pre-trained online tools that allow one to perform automatic or semi-automatic named-entity recognition of any text on a language-dependent basis. Table 2 shows some of these tools. These make use of one or more of the datasets mentioned in Table 1 and usually have an API with which developers can create their own applications using relationship extraction models. However, these tools may inherit the language limitations of the datasets they were trained on. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Language** & **Text Source** & **\#Tags** \\ \hline CoNLL 2003 [15] & English, German, Dutch & Reuters news & 4 \\ \hline Ontonotes V5 [16] & English, Chinese, Arabic & Mobile conversations, religious texts, newsgroups, broadcast news and conversation weblogs, newscast & 18 \\ \hline NCBI disease [17] & English & PubMed & 14 \\ \hline WNUT 2017 [18] & English & Emerging discussions & 6 \\ \hline GENIA [19] & English & PubMed & 36 \\ \hline WNUT 2020 [20] & English & Twitter & 2 \\ \hline LeNER-Br [21] & Portuguese & Legal text & 6 \\ \hline WikiFiger [22] & English & Wikipedia & 112 \\ \hline WikiNEuRal [23] & English, Italian, German, Dutch, French, Portuguese, Russian, Polish & PubMed & 5 \\ \hline Broad Twitter corpus [24] & English & Twitter & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NER Datasets.