Index of papers in Proc. ACL 2009 that mention
  • binary classification
duVerle, David and Prendinger, Helmut
Building a Discourse Parser
o S: A binary classifier , for structure (existence of a connecting node between the two input sub-trees).
Building a Discourse Parser
Because the original SVM algorithms build binary classifiers , multi-label classification requires some adaptation.
Building a Discourse Parser
A possible approach is to reduce the multi-classification problem through a set of binary classifiers , each trained either on a single class (“one vs. all”) or by pair (“one vs. one”).
Evaluation
Binary classifier S is trained on 52,683 instances (split approximately 1/3, 2 / 3 between positive and negative examples), extracted from 350 documents, and tested on 8,558 instances extracted from 50 documents.
binary classification is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Persing, Isaac and Ng, Vincent
Baseline Approaches
More specifically, both baselines recast the cause identification problem as a set of 14 binary classification problems, one for predicting each shaper.
Baseline Approaches
In the binary classification problem for predicting shaper 3,, we create one training instance from each document in the training set, labeling the instance as positive if the document has 3,- as one of its labels, and negative otherwise.
Baseline Approaches
After creating training instances, we train a binary classifier , Ci, for predicting 3i, employing as features the top 50 unigrams that are selected according to information gain computed over the training data (see Yang and Pedersen (1997)).
Introduction
Second, the fact that this is a 14-class classification problem makes it more challenging than a binary classification problem.
binary classification is mentioned in 4 sentences in this paper.
Topics mentioned in this paper: