Specifically, we begin by training an inductive SVM on one labeled example from each class, iteratively labeling the most uncertain unlabeled point on each side of the hyperplane and retraining the SVM until 100 points are labeled.

In self-training, we iteratively train a classifier on the data labeled so far, use it to classify the unlabeled instances, and augment the labeled data with the most confidently labeled instances.

In our algorithm, we start with an initial clustering of all of the data points, and then iteratively remove the 04 most ambiguous points from the dataset and cluster the remaining points.

Reported inter-annotator agreement ( ITA ) for fine-grained word sense assignment tasks has ranged between 69% (Kilgarriff and Rosenzweig, 2000) for a lexical sample using the HECTOR dictionary and 78.6% using WordNet (Landes et al., 1998) in all-words annotation.

The use of more coarse-grained senses alleviates the problem: In OntoNotes (Hovy et al., 2006), an ITA of 90% is used as the criterion for the construction of coarse-grained sense distinctions.

However, intriguingly, for some high-frequency lemmas such as leave this ITA threshold is not reached even after multiple re-partitionings of the semantic space (Chen and Palmer, 2009).

First we propose to employ an iteratively trained target grammar parser to perform grammar formalism conversion, eliminating predefined heuristic rules as required in previous methods.

The procedure of tree conversion and parser retraining will be run iteratively until a stopping condition is satisfied.

Previous DS to PS conversion methods built a converted tree by iteratively attaching nodes and edges to the tree with the help of conversion rules and heuristic rules, based on current head-dependent pair from a source dependency tree and the structure of the built tree (Collins et al., 1999; Covington, 1994; Xia and Palmer, 2001; Xia et al., 2008).