Category Archives: Machine Learning

Review of “A Machine Learning Approach to Recognizing Acronyms and their Expansions”


Article Title: A Machine Learning Approach to Recognizing Acronyms and their Expansions


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Authors: Jun XuYa-Lou Huang

Keywords: Acronym extraction; expansion; text mining; machine learning

Scott on Technology classifications
Reproducible research: No
Additional Keywords: Support Vector Machines, SVM
Programming language used: Unknown
Number of pages: 6

It is a decent overview paper, but lacking in sufficient details to implement. Generally, the approach uses a rules based approach to identify likely acronyms and candidate expansions and uses support vector machines (SVM) for “selecting genuine expansions from candidates.”

Sections 1-3 are the introduction, related works, and observations on “recognizing acronyms and expansions from text.”

Sections in detail

4.1 Identify Likely Acronyms
The overall process here is clear.  Interestingly, an observation they made earlier stated that, “acronyms are generally three to ten characters in length…” yet allow the likely acronym length to be between 2 to 10 characters.  Also for Step 3, they don’t indicate which dictionary they used or how they determine person name or location name. I also found it odd that they check the acronym candidate against an additional stop word list as the stop words should already be in the dictionary and I don’t know what purpose it serves in this step.  However, I have found that ignoring stop words is an important in a pattern based expansion generation step.

4.2 Generate Candidate Expansions
“We observed that expansions always occur in surrounding text where acronyms appear in and always in the same sentence.”  I have found this to be not true.  For example the following text:

During World War II, a number of Army personnel were stationed at the Orlando Army Air Base and nearby Pinecastle Army Air Field. Some of these servicemen stayed in Orlando to settle and raise families. In 1956 the aerospace and defense company Martin Marietta (now Lockheed Martin) established a plant in the city. Orlando AAB and Pinecastle AAF were transferred to the United States Air Force in 1947 when it became a separate service and were re-designated as air force bases (AFB).

Army Air Field and AAF are not near each other and not within the same sentence. If measured from after the “d” in field and up to the “A” in AAF, there are 215 characters between them.  A more relaxed statement is that the  expansions usually appear in the same paragraph as the acronyms.

4.3.2 Features

“lower case[sic], numeric, special characters, and white spaces…” doesn’t specify which factors are binary or real valued.



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  16. Acronym Finder:
  17. The Canonical Abbreviation/Acronym List: (dead link) Current link:


  • Amsterdam is misspelled in the References and corrected here
  • The link to The Canonical Abbreviation/Acronym List was dead and a current link is provided
  • Some of the information presented here was generated through OCR methods.  If you see any errors just drop me a note or add to the comments and we’ll get it corrected.


  • Use of the term “candidate” for potential expansions
  • Use of the term “token” (this is common in the SVM/ML domain)

Interesting features to examine:

  • Length of acronym
  • Length of candidate expansion
  • Expansion distance from acronym


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