Experimental Evaluation of the Value of Structure: How to Efficiently Exploit Interdependencies in Sequence Labeling
G. Wisniewski and Patrick Gallinari
In: ICDM, Italy(2008).
Many problems in natural language processing, information
extraction or bioinformatics consist in predicting a
label for each element of a sequence of observations. The
sequence of labels generally presents multiple dependencies
that restrict the possible labels the elements can take.
Therefore, relations between labels intuitively provide information
valuable for the prediction.
Several approaches have been proposed to take advantage
of this additional information. However, experimental
results show that taking relations into account does not
always improve prediction performances, while it significantly
increases the computational cost of both learning
and prediction. In this work, we aim at both explaining
these surprising results and proposing a simple but computationnaly
efficient approach for labeling sequences.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Patrick Gallinari|
|Deposited On:||24 March 2009|