Origami is the art of paper folding (or put art paper) originating from Japan. The word in Japanese origami is derived from two words: oru folding or loading paper and kami. Origami only been used since 1880; earlier, the Japanese used letters orikata. Simple combination of folding origami to turn rectangular piece of paper (two-way), which is usually square, into the complex (3-way), do not cut and paste in the folding process, this is also the trend of modern origami.
Quilting is a fun hobby -- but where do you begin? From selecting fabrics and designing a quilt to stitching by hand or machine, this friendly guide shows you how to put all the pieces together -- and create a wide variety of quilted keepsakes for your home. We'll have you in stitches in no time!
In this paper, we view coreference resolution as a problem of ranking candidate partitions generated by different coreference systems. We propose a set of partition-based features to learn a ranking model for distinguishing good and bad partitions. Our approach compares favorably to two state-of-the-art coreference systems when evaluated on three standard coreference data sets.
Origami is the art of paper folding (origami or).Chu originating from Japan in Japanese origami is derived from two words: oru and kami is ranked higher or paper. Origami only uses 1880; earlier, the Japanese use the word orikata.
We present a simple yet powerful hierarchical search algorithm for automatic word alignment. Our algorithm induces a forest of alignments from which we can eﬃciently extract a ranked k-best list. We score a given alignment within the forest with a ﬂexible, linear discriminative model incorporating hundreds of features, and trained on a relatively small amount of annotated data. We report results on Arabic-English word alignment and translation tasks. Our model outperforms a GIZA++ Model-4 baseline by 6.3 points in F-measure, yielding a 1.
This paper considers the problem of automatic assessment of local coherence. We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text. We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function. Our experiments demonstrate that the induced model achieves signiﬁcantly higher accuracy than a state-of-the-art coherence model. ...
In this paper, we examined click patterns produced by users of Yahoo! search engine when prompting deﬁnition questions. Regularities across these click patterns are then utilized for constructing a large and heterogeneous training corpus for answer ranking. In a nutshell, answers are extracted from clicked web-snippets originating from any class of web-site, including Knowledge Bases (KBs). On the other hand, nonanswers are acquired from redundant pieces of text across web-snippets.
In this paper we describe a method for simplifying sentences using Phrase Based Machine Translation, augmented with a re-ranking heuristic based on dissimilarity, and trained on a monolingual parallel corpus. We compare our system to a word-substitution baseline and two state-of-the-art systems, all trained and tested on paired sentences from the English part of Wikipedia and Simple Wikipedia.
In this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training.