Kernelized Hashcode Representations for Relation Extraction

Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-of-the-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.Comment: To appear in the proceedings of conference, AAAI-1

Information Extraction in Illicit Domains

Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18\% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.Comment: 10 pages, ACM WWW 201

Polarized Broad-Line Emission from Low-Luminosity Active Galactic Nuclei

In order to determine whether unified models of active galactic nuclei apply to low-luminosity objects, we have undertaken a spectropolarimetric survey of of LINERs and Seyfert nuclei at the Keck Observatory. The 14 objects observed have a median H-alpha luminosity of 8x10^{39} erg/s, well below the typical value of ~10^{41} erg/s for Markarian Seyfert nuclei. Polarized broad H-alpha emission is detected in three LINERs: NGC 315, NGC 1052, and NGC 4261. Each of these is an elliptical galaxy with a double-sided radio jet, and the emission-line polarization in each case is oriented roughly perpendicular to the jet axis, as expected for the obscuring torus model. NGC 4261 and NGC 315 are known to contain dusty circumnuclear disks, which may be the outer extensions of the obscuring tori. The detection of polarized broad-line emission suggests that these objects are nearby, low-luminosity analogs of obscured quasars residing in narrow-line radio galaxies. The nuclear continuum of the low-luminosity Seyfert 1 galaxy NGC 4395 is polarized at p = 0.67%, possibly the result of an electron scattering region near the nucleus. Continuum polarization is detected in other objects, with a median level of p = 0.36% over 5100-6100 A, but in most cases this is likely to be the result of transmission through foreground dust. The lack of significant broad-line polarization in most type 1 LINERs is consistent with the hypothesis that we view the broad-line regions of these objects directly, rather than in scattered light.Comment: 28 pages, including 3 tables and 16 figures. Uses the emulateapj latex style file. Accepted for publication in The Astrophysical Journa

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset