Show all info regardless research infrastructures. Switch to CLARIN environment and show only relevant info to CLARIN, e.g. format recommendations by CLARIN centres. Switch to Text+ environment and show only relevant info to Text+, e.g. format recommendations by Text+ centres. Switch to DARIAH environment and show only relevant info to DARIAH, e.g. format recommendations by DARIAH centres.
Semantic Annotation

In contrast to human a computer cannot understand the meaning of the words or the sentences in a text. However in some working areas, such as informational retrieval, named entity recognition, event extraction or sentiment analysis, it is necessary to find, extract, manipulate or manage some specific knowledge contained in heterogenous information documents in short time. To achieve this, there are many statistics, or machine learning methods to extract and identify automatically the information, which are based on the semantic annotated corpora.

What the entities mean in the text, how they are related to each other or how these relations can be evaluated automatically, can be decoded during the tag of semantic labels.

Semantic annotation provides description of various kind of the knowledge contained in a document and it’s semantics in the domain. The purpose of semantic annotation is to assign the entities in the text and to link to their semantic descriptions. The semantic annotation can provide information about the type of named entity, temporal and event information, information about the discourse and semantic relation between entities, etc.

Standards dealing with this topic:
  1. Semantic role markup language
  2. Language resource management — Semantic annotation framework
  3. Dialogue Act Markup Language
  4. Markup Language for events and temporal expressions in natural language