5.1 Effect of Feature Set Choice

Table 3 illustrates the result of taking a baseline feature set (containing word as the only feature) and adding a single feature from the Simple set to it.

Feature Set F-score %Imp word 43.85 —word+nw 43.86 N0 word+na 44.78 2.1 word+lem 45.85 4.6 word+pos 45.91 4.7 word+nw+pos+lem+na 46.34 5.7

Previous work in traditional text classification and its variants — such as sentiment analysis — has achieved successful results by using the bag-of-words representation; that is, by treating text as a collection of words with no interdependencies, training a classifier on a large feature set of word unigrams which appear in the corpus.

To illustrate, consider the following feature set , a bigram and a trigram (each term in the n-gram either has the form word or Atag):

While the feature set was too small to produce notable results, we identified which features actually were indicative of lect.

Second, note that the parsers incorporating the N-gram feature sets consistently outperform the models using the baseline features in all test data sets, regardless of model order or label usage.

In this paper, we employ two different feature sets: a baseline feature set3 which draw upon “normal” information source, such as word forms and part-of-speech (POS) without including the web-derived selectional preference4 features, a feature set conjoins the baseline features and the web-derived selectional preference features.

3 This kind of feature sets are similar to other feature sets in the literature (McDonald et al., 2005; Carreras, 2007), so we will not attempt to give a exhaustive description.

For example, the baseline feature set includes indicators for word-to-word and tag-to-tag interactions between the head and modifier of a dependency.

3.2 Feature set

Our full feature set is as follows:

The addition of an incoherence metric to the feature set of an AA system has been shown to improve performance significantly (Miltsakaki and Kukich, 2000; Miltsakaki and Kukich, 2004).

Although the above modifications do not exhaust the potential challenges a deployed AA system might face, they represent a threat to the validity of our system since we are using a highly related feature set .

The held-out portion is used to tune the feature set and results are reported for the test split only, i.e., using unseen instances.

We improve BASIC with an extended feature set which targets especially A1 and the verb (Table 5).

The main contributions are: (l) interpretation of negation using focus detection; (2) focus of negation annotation over all PropBank negated sen-tencesl; (3) feature set to detect the focus of negation; and (4) model to semantically represent negation and reveal its underlying positive meaning.

Specifically, we consider the following three n-gram feature sets, with the corresponding features lowercased and unstemmed: UNIGRAMS, BIGRAMS+, TRIGRAMS+, where the superscript + indicates that the feature set subsumes the preceding feature set .

We consider all three n-gram feature sets , namely UNIGRAMS, BIGRAMS+, and TRIGRAMS+, with corresponding language models smoothed using the interpolated Kneser-Ney method (Chen and Goodman, 1996).

We use SVMlight (Joachims, 1999) to train our linear SVM models on all three approaches and feature sets described above, namely POS, LIWC, UNIGRAMS, BIGRAMS+, and TRIGRAMS+.