1. Terminology problems in literature mining and NLP
John MacMullen
SILS Bioinformatics Journal Club
Fall 2003

2. Assumptions of the paper
“knowledge encoded in textual documents is organized around sets of domain-specific terms, which are used as a basis for sophisticated knowledge acquisition.” [938]
“Terms represent the most important concepts in a domain and characterize documents semantically.” [939]
“the basic problem is to recognize domain-specific concepts and to extract instances of specific relationships among them.” [938]
SILS Bioinformatics Journal Club – Fall 2003

3. Current approaches to auto term recognition
Morpho-syntactic feature identification
Hybrid linguistic and statistical approaches
Machine learning techniques
Problems
Terms are ambiguous and have variation; they are hardly ever mono-referential
The lack of naming conventions (controlled vocabularies), the existence of acronyms, and the large existing heterogeneous literatures increase complexity.
What are they really trying to do with these measures? Are they effective?
SILS Bioinformatics Journal Club – Fall 2003

4. Context: Term variation problems in NLP
SILS Bioinformatics Journal Club – Fall 2003

5. Terminology Processing Workflow
2,082 MEDLINE
abstracts related
to ‘nuclear
receptors’
What are the assumptions of this workflow?
Nenadic, Spacsic & Ananiadou (2003), Fig 1
SILS Bioinformatics Journal Club – Fall 2003

6. SILS Bioinformatics Journal Club – Fall 2003
ATR approach
C-values (“termhoods”) [940]
Term frequency
“Frequency of occurrence as a substring of other candidate terms” (receptor)
“Number of candidate terms containing the given candidate term as a substring”
“Number of words contained in the candidate term”
NC-values (“termhood estimations”) [940]
Includes context of candidate terms
“Frequency of co-occurrence with top-ranked context words”
NC-values = a linear combination of C-values and context factors for each term
What are they really trying to do with these measures? Are they effective?
SILS Bioinformatics Journal Club – Fall 2003

7. Clustering & Evaluation
CSL (contextual, syntactical, lexical)
Clustering implies underlying perspectives or queries
Evaluation
Recall – the probability a relevant item will be retrieved
Precision – the probability that a retrieved item will be relevant
SILS Bioinformatics Journal Club – Fall 2003

8. SILS Bioinformatics Journal Club – Fall 2003
Other questions
Corpus construction: “a larger corpus does not have a proportionally higher number of acronyms” [942] True?
“All term variants are considered jointly for the calculation of termhood” [942] What would happen if they weren’t?
In what ways is the hybrid similarity measure corpus dependent? [942]
Is there bias in the sample due to the construction of the corpus?
SILS Bioinformatics Journal Club – Fall 2003

9. SILS Bioinformatics Journal Club – Fall 2003
References
Nenadic, G., Spasic, I., & Ananiadou, S. (2003). Terminology-driven mining of biomedical literature. Bioinformatics 19(8),
SILS Bioinformatics Journal Club – Fall 2003