The selection is made according to the appropriateness of the alteration to the query context (using a bigram language model), or according to its expected impact on the retrieval effectiveness (using a regression model ).

In this paper, we will use a regression model to predict the impact on retrieval effectiveness.

This method develops a regression model from a set of training data, and it is capable of predicting the expected change in performance when the original query is augmented by this alteration.

5.1 Linear Regression Model

The goal of the regression model is to predict the performance change when a query term is augmented with an alteration.

Our proposed semiparametric copula regression model takes a different perspective.

Then we describe the proposed semiparametric Gaussian copula text regression model .

We formulate the copula regression model as follows.

In the first experiment, we compare the proposed semiparametric Gaussian copula regression model to three baselines on three datasets with all features.

On the post—2009 dataset, none of results from the linear and nonlinear SVM models can match up with the linear regression model , but our proposed copula model still improves over all baselines by a large margin.

To understand the learning curve of our proposed copula regression model , we use the 25%, 50%, 75% subsets from the training data, and evaluate all four models.

Moreover, we have introduced a regression model that boosts the observations of word co-occurrences used in the context-based projection method.

We then present an extension of this approach based on regression models .

First, while they experienced the linear regression model, we propose to contrast different regression models .

As most regression models have already been described in great detail (Christensen, 1997; Agresti, 2007), the derivation of most models is only briefly introduced in this work.

Table 6: Results (MAP %) of the standard approach using different regression models on the balanced breast cancer and diabetes corpora

4.2.1 Regression Models Comparison

We contrast the simple linear regression model (Lin) with the second and the third order polynomial regressions (Poly2 and P0ly3) and the logistic regression model (Logit).

To make them more reliable, our second contribution is to contrast different regression models in order to boost the observations of word co-occurrences.

For each bigram, a regression model is used to estimate its frequency in the reference summary.

The regression model uses a variety of indicative features and is trained discriminatively to minimize the distance between the estimated and the ground truth bigram frequency in the reference summary.

We used the estimated value from the regression model ; the ICSI system just uses the bigram’s document frequency in the original text as weight.

# bigrams used in our regression model 2140.7 (i.e., in selected sentences)

In our method, we first extract all the bigrams from the selected sentences and then estimate each bigram’s N We f using the regression model .

When training our bigram regression model , we use each of the 4 reference summaries separately, i.e., the bigram frequency is obtained from one reference summary.

To estimate the bigram frequency in the summary, we propose to use a supervised regression model that is discriminatively trained using a variety of features.

2.2 Regression Model for Bigram Frequency Estimation

We propose to use a regression model for this.

To train this regression model using the given reference abstractive summaries, rather than trying to minimize the squared error as typically done, we propose a new objective function.

In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference.

Then, using these scores, we train a regression model based on the lexical and structural characteristics of the sentences, and use the model to score sentences of new documents to form a summary.

Our approach differs from the early work, in that, we combine a generative hierarchical model and regression model to score sentences in new documents, eliminating the need for building a generative model for new document clusters.

Later, we build a regression model with the same features as our HybHSum to create a summary.

We keep the parameters and the features of the regression model of hierarchical HybHSum intact for consistency.

In this paper, we present a novel approach that formulates MDS as a prediction problem based on a two-step hybrid model: a generative model for hierarchical topic discovery and a regression model for inference.

We construct a hybrid learning algorithm by extracting salient features to characterize summary sentences, and implement a regression model for inference (Fig.3).

Our aim is to find features that can best represent summary sentences as described in § 5, — implementation of a feasible inference method based on a regression model to enable scoring of sentences in test document clusters without retraining, (which has not been investigated in generative summarization models) described in § 5.2.

We build a regression model using sentence scores as output and selected salient features as input variables described below:

(4), we train a regression model .

Once the SVR model is trained, we use it to predict the scores of ntest number of sentences in test (unseen) document clusters, Otest = {01, “plowstl Our HybHSum captures the sentence characteristics with a regression model using sentences in different document clusters.

Through experiments carried out on the developed datasets, we showed that the proposed polarity classification and valence regression models significantly improve baselines (from 11.90% to 39.69% depending on the language) and work well for all four languages.

5.2 Regression Model

Full details of the regression model and its implementation are beyond the scope of this paper; for more details see (Scho'lkopf and Smola, 2001; Smola et al., 2003).

Evaluation Measures: To evaluate the quality of the valence prediction model, we compare the actual valence score of the metaphor given by human annotators denoted with 3/ against those valence scores predicted by the regression model denoted with ac.

We have used an off-the-shelf RTE system to compute these features, and demonstrated that a regression model over these features can outperform an ensemble of traditional MT metrics in two experiments on different datasets.

We optimize the weights of our regression models on two languages and then predict the human scores on the third language.

They are small regression models as described in Section 2 over component scores of four widely used MT metrics.

2The regression models can simulate the behaviour of each component by setting the weights appropriately, but are strictly more powerful.

We therefore verified that the three nontrivial “baseline” regression models indeed confer a benefit over the default component combination scores: BLEU—1 (which outperformed BLEU-4 in the MetricsMATR 2008 evaluation), NIST-4, and TER (with all costs set to 1).

6We also experimented with a logistic regression model that predicts binary preferences directly.

This allows us to use an off-the-shelf RTE system to obtain features, and to combine them using a regression model as described in Section 2.

In this analysis, we train a regression model to fit the activation profile for the 12 phrase stimuli.

The regression model examined to what extent the semantic feature vectors (explanatory variables) can account for the variation in neural activity (response variable) across the 12 stimuli.

All explanatory variables were entered into the regression model simultaneously.

When building prediction models of human judgments using previously proposed automatic measures, we find that we cannot reliably predict human ratings using a regression model , but we can predict human rankings by a ranking model.

We would also want to include more automatic measures that may be available in the richer corpora to improve the ranking and the regression models .

Similarly, when we use previously proposed automatic measures to predict human judgments, we cannot reliably predict human ratings using a regression model , but we can consistently mimic human judges’ rankings using a ranking model.

Some studies (e.g., (Walker et al., 1997)) build regression models to predict user satisfaction scores from the system log as well as the user survey.

In this study, we build both a regression model and a ranking model to evaluate user simulation.

6.1 The Regression Model

The above QE regression model is trained on a portion of the sentences from the input document, and evaluated on the remaining sentences from the same document.

Therefore it is necessary to build a QB regression model that’s robust to different document-specific translation models.

We compute the TER of Tq using Rq as the reference, and train a QB regression model with the 26 features proposed in section 4.1.

Soricut and Echihabi (2010b) proposed various regression models to predict the expected BLEU score of a given sentence translation hypothesis.

We experiment with several classifiers: linear regression model, decision tree based regression model and SVM model.

Our experiments show that the decision tree-based regression model obtains the highest correlation coefficients (0.53) and lowest RMSE (0.23) in both the training and test sets.

Figure 3 shows a Precision-Recall (PR) curve for MATCHER and three baselines: a “Frequency” model that ranks candidate matches for TD by their frequency during the candidate identification step; a “Pattern” model that uses MATCHER’s linear regression model for ranking, but is restricted to only the pattern-based features; and an “Extractions” model that similarly restricts the ranking model to ReVerb features.

All regression models for learning alignments outperform the Frequency ranking by a wide margin.

For W, we use a linear regression model whose features are the score from MATCHER, the probabilities from the Syn and Sem NBC models, and the average weight of all lexical entries in UBL with matching syntax and semantics.

3.5 Regression models for scoring candidates

MATCHER uses a regression model to combine these various statistics into a score for (77,719).

The regression model is a linear regression with least-squares parameter estimation; we experimented with support vector regression models with nonlinear kernels, with no significant improvements in accuracy.

The difference between the persona regression model and the Dirichlet persona model here is not

by the persona regression model , along with links fn

In practice, we find that while the Dirichlet model distinguishes between character personas in different movies, the persona regression model helps distinguish between different personas within the same movie.

To illustrate this, we present results from the persona regression model learned above, with 50 latent lexical classes and 100 latent personas.

Distribution over topics for persona p in role 7“ 0d Movie d’s distribution over personas pe Character e’s persona (integer, p E {1..P}) j A specific (7“, w) tuple in the data Zj Word topic for tuple j 1113' Word for tuple j oz Concentration parameter for Dirichlet model 6 Feature weights for regression model [1,02 Gaussian mean and variance (for regularizing B) md Movie features (from movie metadata) me Entity features (from movie actor metadata) VT, 7 Dirichlet concentration parameters

Figure 2: Above: Dirichlet persona model (left) and persona regression model (right).

From this set, pl-p6 were used for feature selection, data visualization, and estimation of the regression models (training), while sets p7-p9 were reserved for a blind test.

To evaluate the usefulness of WAP in improving automated scoring of essays, we estimate a linear regression model using the human score as a dependent variable (label) and e-rater score and the HAT as the two independent variables (features).

We estimate a regression model on each of setA-pi, i E {1, .., 6}, and evaluate them on each of setA-pj, j E {7, .., 9}, and compare the performance with that of e-rater alone on setA-pj.

11We also performed a cross-validation test on setA p1-p6, where we estimated a regression model on setA-pi and evaluate it on setA-pj, for all i,j E {1, ..,6},i 7E j, and compared the performance with that of e-rater alone on setA-pj, yielding 30 different train-test combinations.

Regression model 10K 75K 500K Ridge 0.063 0.060 0.053

Table 4: Root mean squared error of the linear regression models for each anchor size

Table 4 shows these results for Ridge and Lasso regression models at the three anchor sizes.

We consider such observations to be generated by a regression model of the form:

We trained a logistic regression model on a feature vector for every word that appears in the NPOV sentences from the training set, with the bias-inducing words as the positive class, and all the other words as the negative class.

The types of features used in the logistic regression model are listed in Table 3, together with their value space.

Logistic regression model that only uses the features based on Liu et al.’s (2005) lexicons of positive and negative words (i.e., features 26—29).

However, our logistic regression model reveals that epistemological and other features can usefully augment the traditional sentiment and subjectivity features for addressing the difficult task of identifying the bias-inducing word in a biased sentence.

Our logistic regression model uses the following two types of features: Thread only features and Aggregated post features.

p,- and h,- represent the posts of the thread and their latent categories respectively; 7“ represents the instructor’s intervention and gb(t) represent the nonstructural features used by the logistic regression model .

The logistic regression model is good at exploiting the thread level features but not the content of individual posts.

The first uses a logistic regression model that primarily incorporates high level information about threads and posts.

Two representative methods were used as baselines: the generative model proposed by (Brill and Moore, 2000) referred to as generative and the logistic regression model proposed by (Okazaki et al., 2008)

When using their method for ranking, we used outputs of the logistic regression model as rank scores.

(2008) proposed using a logistic regression model for approximate dictionary matching.

(2008) utilized substring substitution rules and incorporated the rules into a L1-regularized logistic regression model .

Continuous parameters are modeled with a linear regression model (LR), an M5’ model tree (M5), and a model based on support vector machines with a linear kernel (SVM).

As regression models can extrapolate beyond the [0, 1] interval, the output parameter values are truncated if needed—at generation time—before being sent to the base generator.

Table 3: Pearson’s correlation between parameter model predictions and continuous parameter values, for different regression models .

The SVM with linear kernels and the linear regression model used the same features as the manifold models.

By integrating unlabeled data, the manifold model under setting (1) made a 15% improvement over linear regression model on F1 score, where the improvement was significant across all relations.

Our model goes beyond regular regression models in that it applies constraints to those coefficients, such that the topology of the given data manifold will be respected.

Computing the optimal weights in a regression model and preserving manifold topology are conflicting objectives, we

We train the isotonic regression model on each type of system output (i.e., alignment scores, SVM output, BOW scores).

This is likely due to the different objective function in the corresponding optimization formulations: while the ranking model attempts to ensure a correct ordering between the grades, the regression model seeks to minimize an error objective that is closer to the RMSE.

For each fold, one additional fold is held out for later use in the development of an isotonic regression model (see Figure 3).

In contrast, the Persona Regression model of Bamman et al.

The Persona Regression model of Bamman et al.

As expected, the Persona Regression model performs best at hypothesis class B (correctly judging two characters from the same author to be more similar to each other than to a character from a different author); this behavior is encouraged in this model by allowing an author (as an external metadata variable) to directly influence

The results reported are averaged over a 5-fold cross validation of the multiple regression model , where 80% of the SM data

Subsequently, the feature extraction stage (a VSM or a MaxEnt model as the case may be) generates the syntactic complexity feature which is then incorporated in a multiple linear regression model to generate a score.

As in prior studies, here too the level of agreement is evaluated by means of the weighted kappa measure as well as unrounded and rounded Pearson’s correlations between machine and human scores (since the output of the regression model can either be rounded or regarded as is).

Therefore, we learn a linear regression model with time (an operationalization of annotation costs) as the dependent variable.

We learned a simple linear regression model with the annotation time as dependent variable and the features described above as independent variables.

This optimization may include both exploration of additional features (such as domain-specific ones) as well as experimentation with other, presumably nonlinear, regression models .

The fitted logistic regression model (black line) has a statistically significant coefficient for response entropy (p < 0.001).

Figure 5 plots the relationship between the response entropy and the accuracy of our decision procedure, along with a fitted logistic regression model using response entropy to predict whether our system’s inference was correct.

A logistic regression model can capture these facts.