Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Semantic Text Processing is a subfield of Natural Language Processing (NLP) that attempts to derive meaning from natural language and helps machines interpret textual data semantically. It involves developing algorithms and models that can understand and represent the semantics of words and sentences, and the relationships between them.

Knowledge Representation is a subfield of Semantic Text Processing that deals with the representation, organization, and management of knowledge and information in a form that can be processed and used by computers in conjunction with natural language texts. It involves developing formal languages and frameworks, such as ontologies, semantic networks, and knowledge graphs, that can capture and represent the semantics, structure, and relations of concepts and entities in a domain-specific or general knowledge base.

We introduce a cross-document annotation toolset that serves as a corpus-wide knowledge base for linguistic annotations. This implemented system is designed to address the unique cognitive demands placed on human annotators who must relate information that is expressed across document boundaries.

Publication:

A Framework for Cross-Document Annotation

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