Ensemble methods are widely used in machine learning and have been shown to be often very effective (Breiman, 1996; Freund and Schapire, 1997; Smyth and Wolpert, 1999; MacKay, 1991; Freund et al., 2004).

These models may have been derived using other machine learning algorithms or they may be based on

Variants of the ensemble problem just formulated have been studied in the past in the natural language processing and machine learning literature.

With the help of supervised machine learning , we achieve an accuracy of .87 for this task.

We have presented a machine learning system to automatically detect corresponding edit-turn-pairs.

We used DKPro TC (Daxenberger et al., 2014) to carry out the machine learning experiments on edit-turn-pairs.

Many machine learning datasets are noisy with a substantial number of mislabeled instances.

ing, better classification and regression models can be built by using the feature weights generated by these models as a pre-weight on the data points for other machine learning algorithms.

It uses the difference between the low quality label for each data point and a prediction of the label using supervised machine learning models built upon the low quality labels.

In NLP, many statistical machine learning methods that capture the dependencies among random variables, including topic models (Blei et al., 2003; Lafferty and Blei, 2005; Wang et al., 2012), always have to make assumptions with the underlying distributions of the random variables, and make use of informative priors.

This mixed form of formal statement and informal speech brought difficulties to machine learning algorithms.

Copula models (Schweizer and Sklar, 1983; Nelsen, 1999) are often used by statisticians (Genest and Favre, 2007; Liu et al., 2012; Masarotto and Varin, 2012) andecononfiMB(Chenandfbn,2006)u)Mudythe bivariate and multivariate stochastic dependency among random variables, but they are very new to the machine learning (Ghahramani et al., 2012; Han et al., 2012; Xiang and Neville, 2013; Lopez-paz et al., 2013) and related communities (Eick-hoff et al., 2013).

Most existing machine learning approaches suffer from limitations in the modeling of complex linguistic structures across sentences and often fail to capture nonlocal contextual cues that are important for sentiment interpretation.

machine learning algorithms with rich features and take into account the interactions between words to handle compositional effects such as polarity reversal (e.g.

Existing machine learning approaches for the task can be classified based on the use of two ideas.