This paper proposes an efficient algorithm for the generation of new features that enrich the known bagof -words document representation. New features are generated based on word sequences of different length. Learning is performed using Naive Bayesian classifier on feature-vectors, where only

problems. Recently, Lodhi et al. (2002) proposed the use of string kernels, a novel way of computing document similarity based of matching non-consecutive subsequences of characters. In this article, we propose the use of this technique with sequences of words rather than characters. This approach has

There are certain sequences of words in the generators of a two-generator subgroup of SL(2,C) that frequently arise in the Teichmüller theory of hyperbolic three-manifolds and Kleinian groups. In this pa-per we establish the connection between two such families, the family of Farey words that have

is primarily neither a syntactic nor a semantic peculiarity. It is instead in their morphology. Mastering numerals and names for days of the week is assigning them a certain non-standard morphology, whereby any numeral is a mandatorily a non-independent part of a longer sequence. It is hypothesized

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Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences

Abstract. We have developed a method that extracts all maximal frequent word sequences from the documents of a collection. A sequence is said to be frequent if it appears in more than σ documents, in which σ is the frequency threshold given. Furthermore, a sequence is maximal, if no other frequent

on the idea that a proper identification of authors must consider both stylistic and topic features of texts. This method characterizes documents by a set of word sequences that combine functional and content words. The experimental results on poem classification demonstrated that this method outperforms most

propose to tackle this problem representing the sentences by word sequences (n-grams), a widely used representation in text categorization. The experiments demonstrated that this simple representation not only diminishes the domain and language dependency but also enhances the summarization performance. 1

, and it is a problem that one always encounters while collecting fre-quency statistics on words and word sequences (m-grams) from a text of finite size. This means that even for a very large data col-lection, the maximum likelihood estimation method does not allow Turing’s estimate PT for a probability of a