In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: An extended, revised form of Tim Buckwalter’s Arabic lexical and morphological resource AraMorph, eXtended Revised AraMorph (henceforth XRAM), is presented which addresses a number of weaknesses and inconsistencies of the original model by allowing a wider coverage of real-world Classical and contemporary (both formal and informal) Arabic texts. Building upon previous research, XRAM enhancements include (i) flag-selectable usage markers, (ii) probabilistic mildly context-sensitive POS tagging, filtering, disambiguation and ranking of alternative morphological analyses, (iii) semi-automatic increment of lexical coverage through extraction of lexical and morphological information from existing lexical resources. Testing of XRAM through a front-end Python module showed a remarkable success level.

Answer: Semi-Automatic Data Annotation, POS Tagging and Mildly Context- Sensitive Disambiguation: the eXtended Revised AraMorph (XRAM)


Question: Joint matching over a collection of objects aims at aggregating information from a large collection of similar instances (e.g. images, graphs, shapes) to improve maps between pairs of them. Given multiple objects and matches computed between a few object pairs in isolation, the goal is to recover an entire collection of maps that are (1) globally consistent, and (2) close to the provided maps — and under certain conditions provably the ground-truth maps. Despite recent advances on this problem, the best-known recovery guarantees are limited to a small constant barrier — none of the existing methods find theoretical support when more than 50% of input correspondences are corrupted. Moreover, prior approaches focus mostly on fully similar objects, while it is practically more demanding to match instances that are only partially similar to each other (e.g., different views of a single physical object). In this paper, we propose an algorithm to jointly match multiple objects that exhibit only partial similarities, given a few (possibly highly incomplete) pairwise matches that are densely corrupted. By encoding a consistent partial map collection into a 0-1 semidefinite matrix, we propose to recover the ground-truth maps via a parameter-free convex program called MatchLift, following a spectral method that pre-estimates the total number of distinct elements to be matched. Numerically, this program can be efficiently solved via alternating direction methods of multipliers (ADMM) along with a greedy rounding strategy. Theoretically, MatchLift exhibits near-optimal error-correction ability, i.e. in the asymptotic regime it is guaranteed to work even when a dominant fraction 1 − Θ ( log n √ n ) of the input maps behave like random outliers. Furthermore, MatchLift succeeds with minimal input complexity, namely, perfect matching can be achieved as soon as the provided maps form a connected map graph. We evaluate the proposed algorithm on various benchmark data sets including synthetic examples and real-world examples, all of which confirm the practical applicability and usefulness of MatchLift.

Answer: Near-Optimal Joint Object Matching via Convex Relaxation


Question: Abstract The nearest neighbor rule is one of the most widely used models for classification, and selecting a compact set of prototype instances is a primary challenges for its applications. Many existing approaches for prototype selection exploit instance-based analyses and locally-defined criteria on the class distribution, which are intractable for numerical optimization techniques. In this paper, we explore a parametric framework with an adjusted nearest neighbor rule, in which the selection of the neighboring prototypes is modified by their respective parameters. The framework allows us to formulate a minimization problem of the violation of the adjusted nearest neighbor rule over the training set with regards to numerical parameters. We show that the problem reduces to a large-margin principled learning and demonstrate its advantage by empirical comparisons with recent state-ofthe-art methods using public benchmark data.

Answer:
Discriminative Learning of the Prototype Set for Nearest Neighbor Classification