We refer to this classification task as the target task.

For each of the nine target-language-category-combinations a text classification task is created by taking the training set of the product category in S and the test set of the same product category in ’2'.

Stated precisely: We are given a text classification task 7 in a target language ’2' for which no labeled documents are available.

7 may be a spam filtering task, a topic categorization task, or a sentiment classification task .

In this connection we compile extensive corpora in the languages English, German, French, and Japanese, and for different sentiment classification tasks .

In the argument classification task , the similarity between two argument heads hl and fig observed in FrameNet can be computed over l7; and

Table 3: Accuracy on Arg classification tasks wrt different clustering policies

We measured the performance on the argument classification tasks of different models obtained by combing different choices of o with Eq.

More recently, the state-of-art frame-based semantic role labeling system discussed in (Johansson and Nugues, 2008b) reports a 19% drop in accuracy for the argument classification task when a different test domain is targeted (i.e.

Based on an observation that expe-rience-revealing sentences have a certain linguistic style, we formulate the problem of detecting experience as a classification task using various features including tense, mood, aspect, modality, experiencer, and verb classes.

Having converted the problem of experience detection for sentences to a classification task , we focus on the extent to which various linguistic features contribute to the performance of the binary classif1er for sentences.

the problem as a classification task using various linguistic features including tense, mood, aspect, modality, experiencer, and verb classes.

The other one is based on Support Vector Machine (Chang and Lin, 2001) which is the state-of-the-art algorithm for many classification tasks .

Given that structural learning can help in topical classification tasks (Tsochantaridis et al., 2005; Dekel et al., 2004), the lack of success on genres is surprising.

We used character n-grams because they are very easy to extract, language-independent (no need to rely on parsing or even stemming), and they are known to have the best performance in genre classification tasks (Kanaris and Stamatatos, 2009; Sharoff et al., 2010).

But many implementations are not publicly available, and their scalability to real-life text classification tasks is unknown.