Difference between revisions of "Cohen Courses:Dmovshov abbreviations"
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Abbreviations, synonyms and acronyms are heavily used in biomedical literature, for describing names of genes, diseases, biological processes and more. Recognizing short or alternative name forms and mapping them to the full (long) form is important to the full understanding of scientific text. In the context of information extraction tasks, recognizing abbreviated forms can lead to a great increase in recall. This task is especially challenging since abbreviations are often reused, for example, names of genes and systems are shared across species, and since researchers often do not adhere to standard naming conventions. In this project we wish to provide a model for linking an abbreviated or short form biomedical terms to full terms as well as recognize abbreviations that may relate to more than a single entity. | Abbreviations, synonyms and acronyms are heavily used in biomedical literature, for describing names of genes, diseases, biological processes and more. Recognizing short or alternative name forms and mapping them to the full (long) form is important to the full understanding of scientific text. In the context of information extraction tasks, recognizing abbreviated forms can lead to a great increase in recall. This task is especially challenging since abbreviations are often reused, for example, names of genes and systems are shared across species, and since researchers often do not adhere to standard naming conventions. In this project we wish to provide a model for linking an abbreviated or short form biomedical terms to full terms as well as recognize abbreviations that may relate to more than a single entity. | ||
| − | === | + | Currently used approaches only recognize abbreviations within a single sentence that contains both the short and long form. |
| + | |||
| + | === Approach === | ||
* Recognize candidate ''<short-form, long-form>'' pairs from text | * Recognize candidate ''<short-form, long-form>'' pairs from text | ||
* Extract possible long-form versions for each of the abbreviated short-forms | * Extract possible long-form versions for each of the abbreviated short-forms | ||
| − | * Suggest the most probable long-form of | + | * Suggest the most probable long-form of each abbreviation in a set of documents (the base assumption will be that in a single document an abbreviation may only refer to a single long-form, even if it may have many more possible long-forms in other, even closely related, context). |
== Team == | == Team == | ||
[[ User:Dmovshov | Dana Movshovitz-Attias]] | [[ User:Dmovshov | Dana Movshovitz-Attias]] | ||
| − | == Dataset == | + | == Data == |
| + | |||
| + | === Abbreviations Dataset === | ||
[http://medstract.com/ MEDSTRACT] is a collection of automatically extracted acronym pairs from MEDLINE databases. | [http://medstract.com/ MEDSTRACT] is a collection of automatically extracted acronym pairs from MEDLINE databases. | ||
The data includes: | The data includes: | ||
| Line 20: | Line 24: | ||
:* [http://medstract.com/index.php?f=gold-result Gold Standard Results]: Pairs of ''<abbreviation, full form name>'' that appear in the data. | :* [http://medstract.com/index.php?f=gold-result Gold Standard Results]: Pairs of ''<abbreviation, full form name>'' that appear in the data. | ||
:*:* The data does not seem completely coherent and may need to be "cleaned". | :*:* The data does not seem completely coherent and may need to be "cleaned". | ||
| + | |||
| + | === Corpus === | ||
| + | Full length document taken from: | ||
| + | :* [http://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/ PubMed Central open access archive] documents | ||
| + | |||
| + | So far found no data for long-forms of abbreviations in full documents - may have to manually label some. Alternatively, can use the MEDSTRACT gold standard list as a "complete" list of known abbreviations and ignore all others in the corpus. | ||
| + | |||
== Related Work == | == Related Work == | ||
Revision as of 11:15, 12 September 2011
Contents
Identifying Abbreviations in Biomedical Text
Idea
Abbreviations, synonyms and acronyms are heavily used in biomedical literature, for describing names of genes, diseases, biological processes and more. Recognizing short or alternative name forms and mapping them to the full (long) form is important to the full understanding of scientific text. In the context of information extraction tasks, recognizing abbreviated forms can lead to a great increase in recall. This task is especially challenging since abbreviations are often reused, for example, names of genes and systems are shared across species, and since researchers often do not adhere to standard naming conventions. In this project we wish to provide a model for linking an abbreviated or short form biomedical terms to full terms as well as recognize abbreviations that may relate to more than a single entity.
Currently used approaches only recognize abbreviations within a single sentence that contains both the short and long form.
Approach
- Recognize candidate <short-form, long-form> pairs from text
- Extract possible long-form versions for each of the abbreviated short-forms
- Suggest the most probable long-form of each abbreviation in a set of documents (the base assumption will be that in a single document an abbreviation may only refer to a single long-form, even if it may have many more possible long-forms in other, even closely related, context).
Team
Data
Abbreviations Dataset
MEDSTRACT is a collection of automatically extracted acronym pairs from MEDLINE databases. The data includes:
- Gold Standard Data: Sentences including abbreviations.
- Gold Standard Results: Pairs of <abbreviation, full form name> that appear in the data.
- The data does not seem completely coherent and may need to be "cleaned".
Corpus
Full length document taken from:
- PubMed Central open access archive documents
So far found no data for long-forms of abbreviations in full documents - may have to manually label some. Alternatively, can use the MEDSTRACT gold standard list as a "complete" list of known abbreviations and ignore all others in the corpus.
Related Work
- Possible Baseline: A simple algorithm for identifying abbreviation definitions in biomedical text by A. S. Schwartz and M. A. Hearst
- Candidate-pairs extraction algorithm: Hybrid text mining for finding abbreviations and their definitions by Youngja Park and Roy J. Byrd
- An Automatic Identification and Resolution System for Protein-Related Abbreviations in Scientific Papers by Paolo Atzeni, Fabio Polticelli and Daniele Toti
- Mapping Abbreviations to Full Forms in Biomedical Articles by Hong Yu, George Hripcsak and Carol Friedman
