The Frequency Distribution of Semi-major Axis of Wide Binaries. Cosmogony and Dynamical Evolution

The frequency distribution f(a) of semi-major axis of double and multiple systems, as well as their eccentricities and mass ratios, contain valuable fossil information about the process of star formation and the dynamical history of the systems. In order to advance in the understanding of these questions, we have made an extensive analysis of the frequency distribution f (a) for wide binaries (a>25 AU) in the various published catalogues, as well as in our own (Poveda et al., 1994; Allen et al., 2000; Poveda & Hernandez, 2003). Based upon all these studies we have established that the frequency f(a) is function of the age of the system and follows Oepik's distribution f(a) ~ 1/a in the range of 100 AU < a < a[c](t), where a[c](t) is a critical semi-major axis beyond which binaries have dissociated by encounters with massive objects. We argue that the physics behind the distribution f(a) ~ 1/a is a process of energy relaxation, analogous to that present in stellar clusters (secular relaxation) or in spherical galaxies (violent relaxation). The frequency distribution of mass ratios in triple systems as well as the existence of runaway stars, indicate that both types of relaxation are important in the process of binary and multiple star formation.Comment: International Astronomical Union. Symposium no. 240, held 22-25 August, 2006 in Prague, Czech Republi

A Bootstrapping architecture for time expression recognition in unlabelled corpora via syntactic-semantic patterns

In this paper we describe a semi-supervised approach to the extraction of time expression mentions in large unlabelled corpora based on bootstrapping. Bootstrapping techniques rely on a relatively small amount of initial human-supplied examples (termed “seeds”) of the type of entity or concept to be learned, in order to capture an initial set of patterns or rules from the unlabelled text that extract the supplied data. In turn, the learned patterns are employed to find new potential examples, and the process is repeated to grow the set of patterns and (optionally) the set of examples. In order to prevent the learned pattern set from producing spurious results, it becomes essential to implement a ranking and selection procedure to filter out “bad” patterns and, depending on the case, new candidate examples. Therefore, the type of patterns employed (knowledge representation) as well as the ranking and selection procedure are paramount to the quality of the results. We present a complete bootstrapping algorithm for recognition of time expressions, with a special emphasis on the type of patterns used (a combination of semantic and morpho- syntantic elements) and the ranking and selection criteria. Bootstrap- ping techniques have been previously employed with limited success for several NLP problems, both of recognition and classification, but their application to time expression recognition is, to the best of our knowledge, novel. As of this writing, the described architecture is in the final stages of implementation, with experimention and evalution being already underway.Postprint (published version