How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.Comment: 19 pages. Accepted for publication in Artificial Intelligence Review. This update only adds the DOI link to comply with journal's term

Book review: accelerating democracy: transforming governance through technology

John O. McGinnis demonstrates how new technologies combine to address a problem as old as democracy itself: how to help citizens better evaluate the consequences of their political choices. Ana Polo Alonso thinks we can support or dismiss McGinnis’s proposals, but we cannot deny that the author makes a major effort to bring forth ingenious measures to really ‘accelerate democracy.’ Accelerating Democracy: Transforming Governance through Technology. John O. McGinnis. Princeton University Press. December 2012

A mid-infrared exploration of the dusty environments of active galactic nuclei

We present the first results from a mid-infrared survey of local Active Galactic Nuclei (AGN) using the CanariCam (CC) instrument on the 10.4m Gran Telescopio Canarias (GTC). We are obtaining sub-arcsecond angular resolution (0.3-0.6 arcsec) mid-IR imaging and spectroscopic observations of a sample of 100 local AGN, which are complemented with data taken with T-ReCS, VISIR, and Michelle. The full sample contains approximately 140 AGN, covers nearly six orders of magnitude in AGN luminosity, and includes low-luminosity AGN (LLAGN), Seyfert 1s and 2s, QSO, radio galaxies, and (U)LIRGs. The main goals of this project are: (1) to test whether the properties of the dusty tori of the AGN Unified Model depend on the AGN type, (2) to study the nuclear star formation activity and obscuration of local AGN, and (3) to explore the role of the dusty torus in LLAGN.Comment: Conference proceedings of IAU Symposium 304: Multiwavelength AGN surveys and studie