THE GEMINI NICI PLANET-FINDING CAMPAIGN: DISCOVERY OF A CLOSE SUBSTELLAR COMPANION TO THE YOUNG DEBRIS DISK STAR PZ Tel

We report the discovery of a tight substellar companion to the young solar analog PZ Tel, a member of the beta Pic moving group observed with high-contrast adaptive optics imaging as part of the Gemini Near-Infrared Coronagraphic Imager Planet-Finding Campaign. The companion was detected at a projected separation of 16.4 +/- 1.0 AU (0.'' 33 +/- 0.'' 01) in 2009 April. Second-epoch observations in 2010 May demonstrate that the companion is physically associated and shows significant orbital motion. Monte Carlo modeling constrains the orbit of PZ Tel B to eccentricities >0.6. The near-IR colors of PZ Tel B indicate a spectral type of M7 +/- 2 and thus this object will be a new benchmark companion for studies of ultracool, low-gravity photospheres. Adopting an age of 12(-4)(+8) Myr for the system, we estimate a mass of 36 +/- 6 M(Jup) based on the Lyon/DUSTY evolutionary models. PZ Tel B is one of the few young substellar companions directly imaged at orbital separations similar to those of giant planets in our own solar system. Additionally, the primary star PZ Tel A shows a 70 mu m emission excess, evidence for a significant quantity of circumstellar dust that has not been disrupted by the orbital motion of the companion.

An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.