## 3 Natural Language Processing
### Unsupervised RE  
Unsupervised relationship extraction models are used in a variety of applications where it is necessary to discover unknown patterns and relationships in unstructured data, such as text. Examples of use are text clustering, social network analysis, discovery of additional relationships, and anomaly detection. In this method, more general rules of thumb are used to find the tuples. In some cases, even small labelled text datasets are exploited to design and modify systems. However, these models can be more difficult to interpret and validate than supervised models, as there is no labelled dataset to compare the results with. [PERSON] et al. [42] propose a method, PromptORE, that adapts an embedding framework to work in an unsupervised statement-based environment, and is used to embed operations that express relationships and does not require hyperparameter tuning. The embeddings are then grouped into clusters to discover relationships and the appropriate number of clusters is automatically estimated. In addition, in [43] a method using sentence supervision for unsupervised relation extraction is presented, which uses the SBERT-based pretrained model for unsupervised relation extraction and sentence encoding. The model uses a clustering algorithm to classify identical patterns and the extraction of relationships between entities in a sentence, calculating a confidence value to avoid semantic drift between sentences. In [44], general domain knowledge is incorporated, which allows first-order rules to be encoded and automatically combined with the model developed for relation extraction. The paper proposes an unsupervised approach to relation extraction that does not require relation training data and allows the incorporation of global constraints expressing domain knowledge, encoding it as first-order logic rules and integrating it automatically with a thematic model, to produce clusters formed by the available data and constraints. In [45], the authors present GraphRel, a graph convolutional network (GCN)-based model for extracting and learning entities and relationships. This model handles three key aspects of relation extraction. GraphRel automatically extracts the features of each sentence by stacking a Bi-LSTM sentence encoder and a dependency tree encoder, labels the words, and predicts triplets of relations connecting the mentions. The first phase of the proposed model extracts the hidden features of the nodes along the dependencies and a new connected graph is established with edges weighted by relations, and the second phase considers the interaction of entities and relations before the final classification. In [46], a method for relationship extraction is created using the BERT model, which consists of predicting the relationship between two entities, given a sentence and two non-overlapping entity sections. First, the input sequence of type [[CLS] sentence [SEP] subject [SEP] object [SEP]] is constructed, to avoid overfitting, the entities are replaced by masks composed of argument type, entity argument type, and entity type. For the extraction of relations at document level, there is the proposal given in [47], which makes use of different nodes and edges to generate a document-level graph. Inferences on the edges of the graph allow the learning of intra- and inter-entity relationships using multi-instance learning. The model extracts neural relationships based on a partially connected graph of entities, where entity mentions constitute the nodes, and directed edges correspond to ordered pairs of entity mentions. It uses multi-instance learning when mention-level annotations are available. An important aspect to consider, is that in order to achieve the extraction of document-level relationships, representative datasets related to the research topic are necessary, in this sense [PERSON] et al. [48] propose DocRed, a dataset derived from Wikipedia and Wikidata, this dataset allows the extraction of entities and relationships between them.