We introduce relational grams (r-grams). They upgrade n-grams for modeling relational sequences of atoms. As n-grams, r-grams are based on smoothed n-th order Markov chains. Smoothed distributions can be obtained by decreasing the order of the Markov chain as well as by relational generalization of the r-gram. To avoid sampling object identifiers in sequences, r-grams are generative models at t...

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...

In this paper we introduce a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner of what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking the words as they appear in the text. Dependency trees fit directly into this idea, while in case of constituency...

In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sngrams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following ...