Abstract
In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.
| Original language | English |
|---|---|
| Title of host publication | 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings |
| Publisher | Association for Computing Machinery |
| Pages | 58-65 |
| Number of pages | 8 |
| Volume | Part F134476 |
| ISBN (Electronic) | 9781450352994 |
| DOIs | |
| Publication status | Published - 2017 Dec 4 |
| Event | 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Salzburg, Austria Duration: 2017 Dec 4 → 2017 Dec 6 |
Other
| Other | 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 |
|---|---|
| Country | Austria |
| City | Salzburg |
| Period | 17/12/4 → 17/12/6 |
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Keywords
- Concept
- Data mining
- Document descriptor
- Information retrieval
- Medical documents processing
- Ontology
- Summary generation
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach. / Dudko, Alexander; Endrjukaite, Tatiana; Kiyoki, Yasushi.
19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings. Vol. Part F134476 Association for Computing Machinery, 2017. p. 58-65.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach
AU - Dudko, Alexander
AU - Endrjukaite, Tatiana
AU - Kiyoki, Yasushi
PY - 2017/12/4
Y1 - 2017/12/4
N2 - In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.
AB - In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.
KW - Concept
KW - Data mining
KW - Document descriptor
KW - Information retrieval
KW - Medical documents processing
KW - Ontology
KW - Summary generation
UR - http://www.scopus.com/inward/record.url?scp=85044281145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044281145&partnerID=8YFLogxK
U2 - 10.1145/3151759.3151784
DO - 10.1145/3151759.3151784
M3 - Conference contribution
AN - SCOPUS:85044281145
VL - Part F134476
SP - 58
EP - 65
BT - 19th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2017 - Proceedings
PB - Association for Computing Machinery
ER -