This makes supervised machine learning difficult, through a combination of noisy features and unbalanced class distributions.

We ground this paper’s discussion of machine learning with a real problem, turning to the annotation of empowerment language in chatl.

Users, of course, do not express empowerment in every thread in which they participate, which leads to a challenge for machine learning .

Our experiments show that this model significantly improves machine learning performance.

While machine learning is highly effective for annotation tasks with relatively balanced labels, such as sentiment analysis (Pang and Lee, 2004), more complex social functions are often rarer.

We propose adaptations to existing machine learning algorithms which improve recognition of rare annotations in conversational text data.

We introduce the domain of empowerment in support contexts, along with previous studies on the challenges that these annotations (and similar others) bring to machine learning .

This approach is designed to bias the prediction of our machine learning algorithms in favor of minority classes in a coherent manner.

We have conducted exhaustive evaluation with multiple machine learning classifiers and different features sets spanning from lexical information to psychological categories developed by (Tausczik and Pennebaker, 2010).

Multiple techniques have been employed, from various machine learning classifiers, to clustering and topic models.

We tested five different machine learning algorithms such as Nave Bayes, SVM with polynomial kernel, SVM with RBF kernel, AdaBoost and Stacking, out of which AdaBoost performed the best.

For our metaphor polarity task, we use LIWC’s statistics of all 64 categories and feed this information as features for the machine learning classifiers.

To summarize, in this section we have defined the task of polarity classification and we have presented a machine learning solution.

The learned lessons from this study are: (l) valence prediction is a much harder task than polarity classification both for human annotation and for the machine learning algorithms; (2) the obtained results showed that despite its difficulty this is still a plausible problem; (3) similarly to the polarity classification task, valence prediction with LIWC is improved when shorter contexts (the metaphor/source/target information source) are considered.

We now propose a machine learning based approach to detect thwarting in documents.

In this paper, we propose a working definition of thwarting amenable to machine learning and create a system that detects if the document is thwarted or not.

We show that machine learning with annotated corpora (thwarted/non-thwarted) is more effective than the rule based system.

In section 5 we discuss a machine learning based approach which could be used to identify whether a document is thwarted or not.

Table 1 shows the results for the experiments with the machine learning model.

Table 1: Results of the machine learning based approach to thwarting detection

To detect and correct grammatical errors, two different approaches are typically used — knowledge engineering or machine learning .

In contrast, the machine learning approach formulates the task as a classification problem based on learning from training data.

On the other hand, the machine learning approach can learn from texts written by ESL learners where grammatical errors have been annotated.

As such, the machine learning approach has become the dominant approach in grammatical error correction.

Previous work in the machine learning approach typically formulates the task as a classification problem.

In every SMT system, and in machine learning in general, the goal of learning is to find a

Now, recent advances in machine learning have shown that the generalization ability of these learners can be improved by utilizing second order information, as in the Second Order Percep-tron (Cesa-Bianchi et al., 2005), Gaussian Margin Machines (Crammer et al., 2009b), confidence-weighted learning (Dredze and Crammer, 2008), AROW (Crammer et al., 2009a; Chiang, 2012) and Relative Margin Machines (RMM) (Shivaswamy and Jebara, 2009b).

Unfortunately, not all advances in machine learning are easy to apply to structured prediction problems such as SMT; the latter often involve latent variables and surrogate references, resulting in loss functions that have not been well explored in machine learning (Mcallester and Keshet, 2011; Gimpel and Smith, 2012).

RAMPION aims to address the disconnect between MT and machine learning by optimizing a structured ramp loss with a concave-convex procedure.

Our work, however, focuses on developing a novel method which explores the relationship between machine learning model with physical world, in order to investigate these models by physical rule which describe our universe.

We hope our attempt will shed some light upon the application of quantum theory into the field of machine learning .

Some researchers have employed the principle and technology of quantum computation to improve the studies on Machine Learning (ML) (Aimeur et al., 2006; A'imeur et al., 2007; Chen et al., 2008; Gambs, 2008; Horn and Gottlieb, 2001; Nasios and Bors, 2007), a field which studies theories and constructions of systems that can learn from data, among which classification is a typical task.

build a computational model based on quantum computation theory to handle classification tasks in order to prove the feasibility of applying the QM model to machine learning .

Machine learning models for quality estimation typically treat the problem as regression, seeking to model the relationship between features of the text input and the human quality judgement as a continuous response variable.

In this paper we consider Gaussian Processes (GP) (Rasmussen and Williams, 2006), a probabilistic machine learning framework incorporating kernels and Bayesian non-parametrics, widely considered state-of-the-art for regression.

Most empirical work in Natural Language Processing (NLP) is based on supervised machine learning techniques which rely on human annotated data of some form or another.

From this grid Barzilay and Lapata (2008) derive probabilities of transitions between adjacent sentences which are used as features for machine learning algorithms.

To make this representation accessible to machine learning algorithms, Barzilay and Lapata (2008) compute for each document the probability of each transition and generate feature vectors representing the sentences.

(2011) use discourse relations to transform the entity grid representation into a discourse role matrix that is used to generate feature vectors for machine learning algorithms similarly to Barzilay and Lapata (2008).

This paper represents one step towards the reconciliation of traditional formal approaches to compositional semantics with modern machine learning .

In this paper we bridge the gap between recent advances in machine learning and more traditional approaches within computational linguistics.

We show that this combination of state of the art machine learning and an advanced linguistic formalism translates into concise models with competitive performance on a variety of tasks.

Later, with the release of manually annotated corpus, such as Penn Discourse Treebank 2.0 (PDTB) (Prasad et al., 2008), recent studies performed implicit discourse relation recognition on natural (i.e., genuine) implicit discourse data (Pitler et al., 2009) (Lin et al., 2009) (Wang et al., 2010) with the use of linguistically informed features and machine learning algorithms.

In their work, they collected word pairs from synthetic data set as features and used machine learning method to classify implicit discourse relation.

Multitask learning is a kind of machine learning method, which learns a main task together with

Informative catenae are selected using supervised machine learning with linguistically informed features and compared to both nonlinguistic terms and catenae selected heuristically with filters derived from work on paths.

We also extend previous work with development of a linguistically informed, supervised machine learning technique for selection of informative catenae.

We also develop a linguistically informed machine learning technique for catenae selection that captures both key aspects of heuristic filters, and novel characteristics of catenae and paths.