AlvisNLP data structure

The AlvisNLP data structure is an object shared by all processing steps. It is passed from one processing step to the next. The data structure contains the document structure and content, as well as annotations produced by successive steps. The understanding of the data structure is crucial to to use AlvisNLP since this object allows the steps to communicate with each other.

The following figure presents an UML-like specification of the AlvisNLP data structure.

• Corpus: a Corpus object represents a collection of documents. In an AlvisNLP run, the corpus is a unique object passed from module to module. A Corpus object has features and documents.

• Document: a Document object represents a single document. Each document has an identifier which is unique in the corpus. A Document object has features and sections.

• Section: a Section object contains a piece of the document’s text contents. Each section has a name, a contents, features, layers, and relations.

• Layer: a Layer object is an annotation container. A Layer object has a name unique in the section.

• Annotation: an Annotation object represents a span of text created by a module. Each annotation is included in at least one layer. An Annotation object has a start and end which represent the coordinates of the annotation in the section’s contents, and features.

• Relation: a Relation object is a tuple container. A Relation object has a name unique in the section and features.

• Tuple: a Tuple object represents a relation between several elements in the data structure. A Tuple object has several arguments, each argument is an element (Corpus, Document, Section, Relation, but most often Annotation or Tuple) accessible through a role name. A Tuple object also has features.

• Features are key-value pairs that contain information on an element type, tag or property. Feature keys are not unique in an element, though when accessing a feature key, the last value is returned.