Dependency parsing for medical language and concept representation

Area of Science:

• Computational linguistics
• Medical informatics
• Knowledge representation

Background:

• Conceptual structures underpin natural language processing (NLP) and medical concept representation.
• Existing NLP frameworks may not fully integrate with medical knowledge representation needs.

Purpose of the Study:

• To present a PROLOG-based formalization of dependency grammar.
• To enable the accommodation of conceptual structures within dependency rules.
• To provide an operational basis for medical NLP tools.

Main Methods:

• Formalization of dependency grammar using PROLOG.
• Integration of conceptual structures into dependency rules.
• Development of a computational framework.

Main Results:

• Demonstrated the feasibility of a PROLOG-based dependency grammar formalization.
• Showcased the ability to incorporate conceptual structures into grammatical rules.
• Indicated an operational basis for medical language parsers.

Conclusions:

• The proposed formalization offers a foundation for implementing medical language parsers.
• This approach facilitates the design of robust medical concept representation languages.
• Advances NLP in the medical domain through integrated conceptual understanding.