This work presents a new model, called a partial n-gram, in which probability estimates for only some patterns from the full joint distribution are kept. The main experimental result shows that a partial n-gram model for one value of n can have better predictive performance than a full n-gram model for a smaller n where the two models have the same number of parameters. N-grams serve as one of ...

We describe, analyze, and experimentally evaluate a new probabilistic model for wordsequence prediction in natural languages, based on prediction suffi~v trees (PSTs). By using efficient data structures, we extend the notion of PST to unbounded vocabularies. We also show how to use a Bayesian approach based on recursive priors over all possible PSTs to efficiently maintain tree mixtures. These ...

Our proposal, identifying sentiments in N -grams (ISN), focuses on both word order and phrases, and the interdependency between specific ratings and corresponding sentiments in texts to detect subjective information.

Motivation Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce...

n-grams have been used widely and successfully for approximate string matching in many areas. s-grams have been introduced recently as an n-gram based matching technique, where di-grams are formed of both adjacent and non-adjacent characters. s-grams have proved successful in approximate string matching across language boundaries in Information Retrieval (IR). s-grams however lack precise defin...

In this paper, we discuss a specific type of mixed syntactic ngrams: syntactic n-grams with relation names, snr-grams. This type of syntactic n-grams combines lexical elements of the sentence with the syntactic data, but it keeps the properties of traditional n-grams and syntactic n-grams. We discuss two possibilities related to labelling of the relation names for snrgrams: based on dependencie...

The rate of occurrence of words is not uniform but varies from document to document. Despite this observation, parameters for conventional n-gram language models are usually derived using the assumption of a constant word rate. In this paper we investigate the use of variable word rate assumption, modelled by a Poisson distribution or a continuous mixture of Poissons. We present an approach to ...