Automatic term list generation for entity tagging.
BACKGROUND
Many entity taggers and information extraction systems make use of lists of terms of entities such as people, places, genes or chemicals. These lists have traditionally been constructed manually. We show that distributional clustering methods which group words based on the contexts that they appear in, including neighboring words and syntactic relations extracted using a shallow parser, can be used to aid in the construction of term lists.


RESULTS
Experiments on learning lists of terms and using them as part of a gene tagger on a corpus of abstracts from the scientific literature show that our automatically generated term lists significantly boost the precision of a state-of-the-art CRF-based gene tagger to a degree that is competitive with using hand curated lists and boosts recall to a degree that surpasses that of the hand-curated lists. Our results also show that these distributional clustering methods do not generate lists as helpful as those generated by supervised techniques, but that they can be used to complement supervised techniques so as to obtain better performance.


BACKGROUND
The code used in this paper is available from http://www.cis.upenn.edu/datamining/software_dist/autoterm/
Sandler
Ted
T
Department of Computer and Information Science, University of Pennsylvania 3330 Walnut Street, Philadelphia, 19104, USA. tsandler@seas.upenn.edu
Schein
Andrew I
AI
Ungar
Lyle H
LH
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D004282:Documentation / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D011506:Proteins / Q000145:classification*
D011506:Proteins