In this section we review the leXicon learning algorithm introduced by Chen and Mooney (2011) as well as the overall task they designed to test semantic understanding of navigation instructions.

We evaluate our new lexicon learning algorithm as well as the other modifications to the navigation system using the same three tasks as Chen and Mooney (2011).

Here SGOLL has a decidedly large advantage over the lexicon learning algorithm from Chen and Mooney, requiring an order of magnitude less time to run.

We have introduced a novel, online lexicon learning algorithm that is much faster than the one proposed by Chen and Mooney and also performs better on the navigation tasks they devised.

Their lexicon learning algorithm finds the common connected subgraph that occurs with a word by taking intersections of the graphs that represent the different contexts in which the word appears.

In addition to the new lexicon learning algorithm , we also look at modifying the meaning representation grammar (MRG) for their formal semantic language.

In addition to introducing a new lexicon learning algorithm , we also made another modification to the original system proposed by Chen and Mooney (2011).

extension of the model and learning algorithm to word sequences and (2) feature functions that relate acoustic measurements to sub-word units.

We compare the CRF4, Passive-Aggressive (PA), and Pegasos learning algorithms .

4We use the term “CRF” since the learning algorithm corresponds to CRF learning, although the task is multiclass classification rather than a sequence or structure prediction task.

Models labeled X/Y use learning algorithm X and feature set Y.

In the next section we present a learning algorithm that aims to minimize the expected zero-one loss.

We introduce a spectral learning algorithm for latent-variable PCFGs (Petrov et al., 2006).

We now state a PAC-style theorem for the learning algorithm .

Recent work has introduced polynomial-time learning algorithms (and consistent estimation methods) for two important cases of hidden-variable models: Gaussian mixture models (Dasgupta, 1999; Vempala and Wang, 2004) and hidden Markov models (Hsu et al., 2009).

Figure 4: The spectral learning algorithm .

(2011) consider spectral learning algorithms of tree-structured directed bayes nets.

ADF learning algorithm : procedure ADF(q, c, a, [3)

Figure 1: The proposed ADF online learning algorithm .

Prior work on convergence analysis of existing online learning algorithms (Murata, 1998; Hsu et

We have proposed new approaches to characterize verb classes in learning algorithms .

The advantage of kernel methods is that they can be directly used in some learning algorithms , e.g., SVMs, to train verb classifiers.

Note that their coding in learning algorithms is rather complex: we need to take into account syntactic structures, which may require an exponential number of syntactic features (i.e., all their possible substructures).