In this paper we consider models for predicting the helpfulness rating of Amazon book reviews. We examine features such as the review’s star rating, the length of the review text, the readability of the review text, and the amount of comparisons made in the review. We compare Support Vector Machine and Random Forests models both for regression and classification.

For a singular Riemannian foliation [Formula: see text] on manifold, curve is called horizontal if it meets the leaves of perpendicularly. unit sphere text], we show that polar or given by orbits an infinitesimally action, then diameter i.e. any two points in can be connected length text].

The Apostolico-Giancarlo string-matching algorithm is analyzed precisely. We give a tight upper bound of 3 2 n text character comparisons when searching for a pattern in a text of length n. We exhibit a family of patterns and texts reaching this bound. We also provide a slightly improved version of the algorithm.

This paper describes an experiment in example-based machine translation (EBMT) on full text. The unit of translation is a text chunk of arbitrary length, in contrast to sentence-level EBMT experiments. Intra-and inter-language matching techniques and metrics used in the experiment are described.

In this paper we describe KnCe2013-CORE, a system to compute the semantic similarity of two short text snippets. The system computes a number of features which are gathered from different knowledge bases, namely WordNet, Wikipedia and Wiktionary. The similarity scores derived from these features are then fed into several multilayer perceptron neuronal networks. Depending on the size of the text...

We present a document compression system that uses a hierarchical noisy-channel model of text production. Our compression system first automatically derives the syntactic structure of each sentence and the overall discourse structure of the text given as input. The system then uses a statistical hierarchical model of text production in order to drop non-important syntactic and discourse constit...

Language models assign probabilities to strings of symbols. Their interpretation is reviewed and applied to text classification. A language recogniser is constructed from Bayes’ theorem and a simple bigram model. This provides near perfect results on sentences of text and motivates a mixture language model. Hidden Markov models (HMM) are reviewed as a method of capturing order over different le...

Generative Adversarial Networks (GANs) have shown great promise recently in image generation. Training GANs for text generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximumlikelihood or used convolutional networks for generation. In this work, ...

Speech input is a natural text entry method for handheld devices that are used in different contexts. We conducted an experiment to understand effects of input (speaking) style (phrasal vs. sentence input) on Chinese text entry rates and user satisfaction with other two variables: recognition rate (50%, 70% and 90%) and message length (10 vs. 20 characters). Wizard of Oz was applied in the expe...