In order to evaluate the effectiveness of the cluster-based feature sets , we conducted dependency parsing experiments in English and Czech.

In our English experiments, we tested eight different parsing configurations, representing all possible choices between baseline or cluster-based feature sets , first-order (Eisner, 2000) or second-order (Carreras, 2007) factorizations, and labeled or unlabeled parsing.

Second, note that the parsers using cluster-based feature sets consistently outperform the models using the baseline features, regardless of model order or label usage.

The feature sets we used are similar to other feature sets in the literature (McDonald et al., 2005a; Carreras, 2007), so we will not attempt to give a exhaustive description of the features in this section.

In our experiments, we employed two different feature sets: a baseline feature set which draws upon “normal” information sources such as word forms and parts of speech, and a cluster-based feature set that also uses information derived from the Brown cluster hierarchy.

Our first-order baseline feature set is similar to the feature set of McDonald et al.

We evaluate six different feature sets for their effectiveness in AVC: SCF, DR, CO, ACO, SCF+CO, and J OANISO7 .

The other four feature sets include both syntactic and lexical information.

JOANISO7: We use the feature set proposed in J oanis et al.

We develop feature sets that combine syntactic and lexical information, which are in principle useful for any Levin-style verb classification.

We test the general applicability and scalability of each feature set to the distinctions among 48 verb classes involving 1,300 verbs, which is, to our knowledge, the largest investigation on English verb classification by far.

To preview our results, a feature set that combines both syntactic information and lexical information works much better than either of them used alone.

We construct a semantic space with each feature set .

Except for J ONAISO7 which only contains 224 features, all the other feature sets lead to a very high-dimensional space.

The deeper linguistic analysis allows their feature set to cover a variety of indicators of verb semantics, beyond that of frame information.

We derive two types of feature sets from the responses: features derived from each user model and features derived from attributes of the query/ response pair itself.

The five feature sets for the user model are:

0 allUtz’lz’ty: 12 features consisting of the high, low, and average utility scores from the previous three feature sets .

To answer the second research objective we will analyze the contribution of the proposed feature set to this function.

For completeness we also include in the feature set the value of the t f - idf similarity measure.

Feature Set MRR P@1

The algorithm incrementally adds to the feature set the feature that provides the highest MRR improvement in the development partition.

This approach allowed us to perform a systematic feature analysis on a large-scale real-world corpus and a comprehensive feature set .

Our model uses a larger feature set that includes correlation and transformation-based features and five different content representations.

This problem of over-fitting is exacerbated in discriminative models with large, expressive, feature sets .

Learning with a large feature set requires many training examples and typically many iterations of a solver during training.

To do this we use our own implementation of Hiero (Chiang, 2007), with the same grammar but with the traditional generative feature set trained in a linear model with minimum BLEU training.

The feature set includes: a trigram language model (lm) trained

The relative scores confirm that our model, with its minimalist feature set, achieves comparable performance to the standard feature set without the language model.

In this section, we will discuss the results of our best emission feature set (five-gram features with a context window of eleven letters) on held-out unseen test sets.

With SVM-HMM, the crux of the task is to create a tag scheme and feature set that produce good results.

After experimenting with the development set, we decided to include in our feature set a window of eleven characters around the focus character, five on either side.

As is apparent from Figure 2, we see a substantial improvement by adding bigrams to our feature set .

We are also interested to see how well this feature set performs on speech data, as in (Aoki et al., 2003).

They motivate a richer feature set , which, however, does not yet appear to be implemented.

(2005) adds word repetition to their feature set .

Our feature set incorporates information which has proven useful in meeting segmentation (Galley et al., 2003) and the task of detecting addressees of a specific utterance in a meeting (J ovanovic et al., 2006).

Finally, further efforts to engineer a grammar suitable for realization from the CCGbank should provide richer feature sets , which, as our feature ablation study suggests, are useful for boosting hypertagging performance, hence for finding better and more complete realizations.

The the whole feature set was found in feature ablation testing on the development set to outperform all other feature subsets significantly (p < 2.2 - 10—16).

The full feature set outperforms all others significantly (p < 2.2 - 10—16).

The results for the full feature set on Sections ()0 and 23 are outlined in Table 2.

In Table 2 we have shown the accuracy values for few feature sets .

Again when wi_2 and rut-+2 are deducted from the feature set (i.e.

When suffix, prefix and digit information are added to the feature set , the f-value is increased upto 74.26.

Suspecting that a noisy feature set had contributed to the apparent drop in performance, these authors then proceed to apply a variety of noise filters to reduce the set of feature values to 51,345, which in turn leads to an improved cluster purity measure of 62.7%.

In experiment 2, we see a similar ratio of feature quantities before filtering; after some initial filtering, Almuhareb and Poesio reduce their feature set to just under 10 times the size of the simile-derived feature set .

First, the feature representations do not need to be hand-filtered and noise-free to be effective; we see from the above results that the raw values extracted from the simile pattern prove slightly more effective than filtered feature sets used by Almuhareb and Poesio.

As noted by the latter authors, this results in a much smaller yet more diagnostic feature set for each concept.

Table 1: Performance of EDITDIST and our model with various features sets on EN -ES-W. See section 5.

We will use MCCA (for matching CCA) to denote our model using the optimal feature set (see section 5.3).

As an example of the performance of the system, in English-Spanish induction with our best feature set , using corpora derived from topically similar but nonparallel sources, the system obtains 89.0% precision at 33% recall.

Our feature set is summarized in Table 2, which closely follows Chamiak and Johnson (2005), except that we excluded the nonlocal features Edges, NGram, and CoPar, and simplified Rule and NGramTree features, since they were too complicated to compute.4 We also added four unlexicalized local features from Collins (2000) to cope with data-sparsity.

tures in the updated version.5 However, our initial experiments show that, even with this much simpler feature set , our 50-best reranker performed equally well as theirs (both with an F-score of 91.4, see Tables 3 and 4).

This result confirms that our feature set design is appropriate, and the averaged perceptron learner is a reasonable candidate for reranking.