## 3 Natural Language Processing
### Relationship Extraction  
Relation extraction (RE) allows the extraction of relations between two or more previously identified entities [34] (e.g., persons, places, ID), in order to classify them into a set of characteristics (e.g., \"son of\", \"employee of\", or \"lives in\") using semantic relations in the text. This process allows structured knowledge to be acquired from unstructured data. For example, in Figure 4 the phrase \"Madrid is in Spain\" is establishing an \"is in\" relationship between Madrid and Spain. One way to represent the detected relationships and facilitate understanding is by means of a diagram. Figure 5 shows the graph of the identified relations in a text, in which some characteristics are added to facilitate the interpretation of the entities, for example \"_born in\"_, \"_occupation\"_, \"_developed\"_, \"_part of\"_. There are a few different methods to perform relationship extraction:  
* RE rule-based: In rule-based methods, a manual analysis of a set of sentences is carried out in order to identify what sentences that include a relation look like [34]. This method seeks to identify patterns of the type 1, where \(X\)1_ and \(Y\)1_ are identified entities and alpha (\(\alpha\)) is intermediate words. These types of patterns are called word sequence patterns, because they follow a coherent syntactic and semantic order in the text, however, the implemented rules are inefficient for sequences of larger scope and variety, e.g., \"[PERSON] and [PERSON] got married\". In this example, the rule specifies a pattern that follows the sequence of the text, which defines easily identifiable patterns in word sequences. \[(X\_1\ \alpha\ Y\_1)\] (1)
* Weakly supervised RE: This method starts with a set of manually created rules and, based on these, finds new ones from the unmarked text data. One way to start is to create a set of \"seed tuples\" that describe specific relationships between entities [35]. For example, seed = seed = {(PER: Bob, LOC: USA), (PER: Joe, LOC: Spain)} these seeds establish entities that have a relationship based on the \"is in\" relationship PER is in LOC), from this pattern new ones are generated from the text recursively, (PER:  
Figure 4: Relationship Example. Figure 5: Graphical Representation of Relationships. Alice, LOC: Germany). This method is increasingly error-prone and new seeds will be needed when new types of relations are needed. * Supervised RE: The most common way to extract existing relationships is to train a binary classifier that determines whether a relationship exists between two entities [36; 37]. These binary classifiers take as input semantic and syntactic features of the text, which requires the text to be previously marked by other NLP methods. * Distantly supervised RE: This method combines the techniques of using a classifier and seed data, with the difference that instead of using a set of tuples [38], all knowledge is taken from an existing knowledge base such as Freebase, DBpedia, Ontonotes, Wikipedia, WNUT, Yago. It allows for reduced manual effort, as well as the scalability to use a large number and variety of tags and relations. This method is limited to the knowledge base used and, in case it is needed for the research in question, an adjustment of the trained data will be necessary. * Unsupervised RE: This method, unlike the previous ones, is based on a very general and heuristic set of constraints, so there is no need to use labelled data, seed sets or rules are used to capture the different relationships in the text [39]. In this method, more general rules of thumb are used to find the tuples. For some cases, even taking advantage of small labelled text datasets to design and modify systems. In general, these methods tend to require less supervision overall. There are currently multiple works related to the extraction of relationships based on identified entities.