Rational stochastic languages

The goal of the present paper is to provide a systematic and comprehensive study of rational stochastic languages over a semiring K \in {Q, Q +, R, R+}. A rational stochastic language is a probability distribution over a free monoid \Sigma^* which is rational over K, that is which can be generated by a multiplicity automata with parameters in K. We study the relations between the classes of rational stochastic languages S rat K (\Sigma). We define the notion of residual of a stochastic language and we use it to investigate properties of several subclasses of rational stochastic languages. Lastly, we study the representation of rational stochastic languages by means of multiplicity automata.Comment: 35 page

The acquisition of the English dative alternation by Russian foreign language learners

Ditransitive verbs include a “recipient” and a “theme” argument (in addition to the subject). The choice of putting one argument before the other (i.e., either recipient-theme, or theme-recipient) is associated with multiple discourse-pragmatic factors. Language have different options to code the ditransitive construction. In English, a ditransitive verb can take two alternating patterns (“the dative alternation”): the Double Object Construction (DOC) (John gives Mary a book) and the to-dative construction (to-dative) (John gives a book to Mary). In Russian, theme and recipient are marked by accusative and dative, respectively. In addition, word order is flexible and either the accusative-marked theme (Pjotr dal knigu Marii), or the dative-marked recipient (Pjotr dal Marii knigu) can come first. This article reports on two sentence rating experiments (acceptability judgments) to test whether Russian learners of English transfer their preferences about the theme-recipient order in Russian to the ditransitive construction in English. A total of 284 Russian students were tested. Results for both tests showed a great variability in the ratings. A comparison of the ratings seems to suggest a small positive correlation, but no statistically significant relation was found between the order preferences in both languages. However, we found a small preference for the use of the to-dative, which we relate to the language acquisition process as proposed by Processability Theory

IgTM: An algorithm to predict transmembrane domains and topology in proteins

<p>Abstract</p> <p>Background</p> <p>Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.</p> <p>Results</p> <p>We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: <url>http://www.dsic.upv.es/users/tlcc/bio/bio.html</url></p> <p>Conclusion</p> <p>We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.</p

Calibrating Generative Models: The Probabilistic Chomsky-Schützenberger Hierarchy

A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic tools adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning