We formulate syllabification as a tagging problem, and learn a discriminative tagger from labeled data using a structured support vector machine ( SVM ) (Tsochantaridis et al., 2004).

A structured support vector machine ( SVM ) is a large-margin training method that can learn to predict structured outputs, such as tag sequences or parse trees, instead of performing binary classification (Tsochantaridis et al., 2004).

We employ a structured SVM that predicts tag sequences, called an SVM Hidden Markov Model, or SVM-HMM.

This approach can be considered an SVM because the model parameters are trained discrimi-natively to separate correct tag sequences from incorrect ones by as large a margin as possible.

The SVM framework is less restrictive: we can include 0 as an emission feature, but we can also include features indicating that the preceding and following letters are m and r respectively.

They applied an out-of-domain-trained SVM classifier to label examples from the target domain and then retrained the classifier using these new examples.

Depending on the similarity between domains, this method brought up to 15% gain compared to the baseline SVM .

Dataset Movie News Blogs PRs Dataset size 1066 800 800 1200 unigrams SVM 68.5 61.5 63.85 76.9 NB 60.2 59.5 60.5 74.25 nb features 5410 4544 3615 2832 bigrams SVM 59.9 63.2 61.5 75.9 NB 57.0 58.4 59.5 67.8 nb features 16286 14633 15182 12951 trigrams SVM 54.3 55.4 52.7 64.4 NB 53.3 57.0 56.0 69.7 nb features 20837 18738 19847 19132

Table 4: Accuracy of SVM with unigram model

results depends on the genre and size of the n-gram: on product reviews, all results are statistically significant at oz 2 0.025 level; on movie reviews, the difference between NaVe Bayes and SVM is statistically significant at oz 2 0.01 but the significance diminishes as the size of the n- gram increases; on news, only bigrams produce a statistically significant (a = 0.01) difference between the two machine learning methods, while on blogs the difference between SVMs and NaVe Bayes is most pronounced when unigrams are used (a = 0.025).

To our knowledge, the only work that describes the application of statistical classifiers ( SVM ) to sentence-level sentiment classification is (Gamon and Aue, 2005)1.

Using then an SVM meta-classifier trained on a small number of target domain examples to combine the nine base classifiers, they obtained a statistically significant improvement on out-of-domain texts from book reviews, knowledge-base feedback, and product support services survey data.

The baseline performance of the Lexicon-Based System (LBS) described above is presented in Table 5, along with the performance results of the in-domain- and out-of-domain-trained SVM classifier.

Movies ‘ News ‘ Blogs ‘ PRs LBS 57.5 62.3 63.3 59.3 SVM in-dom.

68.5 61.5 63.85 76.9 SVM out-of-dom.

SVM , can be employed, where each pair of question and candidate context will be treated as an instance.

Model H Prec(%) l Rec(%) 1 F1(%) 1 Context Detection SVM 75.27 68.80 71.32 C4.5 70.16 64.30 67.21 L—CRF 75.75 72.84 74.45 Answer Detection SVM 73.31 47.35 57.52 C4.5 65.36 46.55 54.37 L—CRF 63.92 58.74 61.22

This experiment is to evaluate Linear CRF model (Section 3.1) for context and answer detection by comparing with SVM and C4.5(Quinlan, 1993).

For SVM , we use SVMlightanchims, 1999).

Experimental results show that 1) Linear CRFs outperform SVM and decision tree in both context and answer detection; 2) Skip-chain CRFs outperform Linear CRFs for answer finding, which demonstrates that context improves answer finding; 3) 2D CRF model improves the performance of Linear CRFs and the combination of 2D CRFs and Skip-chain CRFs achieves better performance for context detection.

(2007) used SVM to extract input-reply pairs from forums for chatbot knowledge.

Unlike previous proposed approaches, we introduce a convex objective for the semi-supervised learning algorithm by combining a convex structured SVM loss and a convex least square loss.

In particular, they present an algorithm for multi-class unsupervised and semi-supervised SVM learning, which relaxes the original non-convex objective into a close convex approximation, thereby allowing a global solution to be obtained.

More specifically, for the loss on the unlabeled data part, we substitute the original unsupervised structured SVM loss with a least squares loss, but keep constraints on the inferred prediction targets, which avoids trivialization.

ing semi-supervised convex objective to dependency parsing, and obtain significant improvement over the corresponding supervised structured SVM .

Although semi-supervised structured SVM learning has been an active research area, semi-supervised structured SVMs have not been used in many real applications to date.

By combining the convex structured SVM loss on labeled data (shown in Equation (5)) and the convex least squares loss on unlabeled data (shown in Equation (8)), we obtain a semi-supervised structured large margin loss

The objective of standard semi-supervised structured SVM is a combination of structured large margin losses on both labeled and unlabeled data.

: Maximum Entropy, SVM and C50.

An accuracy of 96.09% has been achieved by SVM .

Several well-known supervised ML methods have been selected: artificial neural networks (ANN), Nai've Bayes (NB), Support Vector Machines ( SVM ) and J48 (Witten and Frank, 1999) an improved variant of the C4.5 decision tree induction.

Table 2 shows that SVM achieved the best result with 96.09% accuracy.

ants ML Method ANN NB SVM J48

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 ).

We test a Naive Bayes classifier (NB), a j48 decision tree (J48), a nearest-neighbor classifier using one neighbor (NN), a Java implementation of the RIPPER rule-based learner (J RIP), the AdaBoost boosting algorithm (ADA), and a support vector machines classifier with a linear kernel ( SVM ).

Figure 3: SVM model with a linear kernel predicting the CONTENT POLARITY parameter.