Name
Abstract | ||
|---|---|---|
The number of publicly available clinical studies is constantly increasing, formulating a rather promising corpus of documents for clinical research purposes. However, the abbreviations used in these studies pose a serious barrier to any text mining technique. This paper presents a study conducted in the above domain, which used specifically developed tools and mechanisms in order to process a number of randomly selected documents from clinicaltrialsregister.eu. The analysis performed indicated that abbreviations appear at a large scale without their long form (aka expansion). In order to assess the abbreviations' true meaning, it is necessary to utilize the appropriate corpus of documents, apply innovative algorithms and techniques to detect their possible expansions, and accordingly select the appropriate ones. Furthermore, the discrimination power of tokens has a distinctive role in abbreviations construction, and hence, it can facilitate the detection of acronym-type abbreviations. Additionally, the expressions in which abbreviations appear, as well as the preceding or following text are of primary importance for selecting the appropriate meaning. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-27653-3_4 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Abbreviations,Expansion,Clinical studies,Semantic analysis,Corpus annotation | Expression (mathematics),Inference,Computer science,Natural language processing,Artificial intelligence,AKA | Conference |
Volume | ISSN | Citations |
563 | 1865-0929 | 2 |
PageRank | References | Authors |
0.38 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Efthymios Chondrogiannis | 1 | 24 | 5.60 |
| Vassiliki Andronikou | 2 | 79 | 10.06 |
| Efstathios Karanastasis | 3 | 22 | 5.19 |
| Theodora A. Varvarigou | 4 | 25 | 9.04 |