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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Semantic relation mining of solid compounds in medical corpora.

Dimitrios Kokkinakis¹

¹Department of Swedish Language, Språkdata, University of Gothenburg, Sweden.

Studies in Health Technology and Informatics

|May 20, 2008

Summary

This study introduces a method for analyzing Swedish medical noun compounds to understand word relationships. It applies semantic descriptors to identify these relations, improving document understanding technologies.

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Area of Science:

Computational linguistics
Medical informatics
Natural language processing

Background:

Scientific texts often embed meaning in noun compounds.
Semantic relation mining enhances document understanding technologies like Information Extraction and Question Answering.
Analyzing Swedish medical compounds presents unique challenges due to their solid (closed-form) nature.

Purpose of the Study:

To explore assigning semantic descriptors from a multilingual medical thesaurus to Swedish medical compounds.
To determine the semantic relations between constituents of these compounds.
To address the challenges of segmenting and interpreting solid compounds in Swedish medical corpora.

Main Methods:

Utilized a large sample of solid compounds from Swedish medical corpora.
Applied semantic descriptors from a multilingual medical thesaurus.
Investigated lexical hierarchies for relation identification, adapting previous research to Swedish.

Main Results:

Successfully assigned semantic descriptors to Swedish medical compounds.
Identified semantic relations between compound constituents.
Demonstrated a method for handling the challenges of solid compound segmentation in Swedish.

Conclusions:

The approach enhances the understanding of semantic relations in Swedish medical noun compounds.
This contributes to improved natural language processing for medical texts.
The findings offer a foundation for further research in cross-lingual and specialized domain NLP.