12061046
Parsing the Penn Chinese Treebank with Semantic Knowledge
We build a class-based selection preference sub-model to incorporate external semantic knowledge from two Chinese electronic semantic dictionaries. This sub-model is combined with modifier-head generation sub-model. After being optimized on the held out data by the EM algorithm, our improved parser achieves 79.4% (F1 measure), as well as a 4.4% relative decrease in error rate on the Penn Chinese Treebank (CTB). Further analysis of performance improvement indicates that semantic knowledge is helpful for nominal compounds, coordination, and N⋄V tagging disambiguation, as well as alleviating the sparseness of information available in treebank.
6832980
Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation
We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reordering events of neighbor blocks from bilingual data. In our experiments on Chinese-to-English translation, this MaxEnt-based reordering model obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks.
3416923
Efficient Computation of the Skyline Cube
Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper. we consider the problem of efficiently computing a SKYCUBE, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such "shared-nothing" algorithms are inefficient. We develop several computation sharing strategies based on effectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute SKYCUBE efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is shown that new algorithms significantly outperform the naive ones.
15497881
A congestion-driven placement framework with local congestion prediction
In this paper, we present a novel congestion-driven placement methodology. We analyze netlist structure and perform local congestion prediction to determine the amount of white space which will be attached to each cell. We place the modified netlist using a total-wire-length-driven placer and obtain an initial legal placement. We extract the congestion information and determine the target white space distribution. Finally, we apply placement migration, an incremental placement technique, to transform the initial placement into a placement which satisfies the target white space distribution. We provide experimental data to demonstrate the effectiveness of our congestion driven placement framework. Compared with the recent academic routability-driven placer Dragon [12], we achieve significant routability improvements in much shorter cpu run time.
8945696
Synthesis and placement flow for gain-based programmable regular fabrics
In this paper we present the Gain-based Logic Block Array (GLA), a new via-programmable regular fabric. GLA is an array of Gain-based Logic Blocks (GLBs). The GLB is a semi-universal logic block designed based on logical effort theory[12]. Customization of the GLBs is provided by programmable vias. To achieve the best performance, appropriate fabric has to be selected from a family of GLAs with different performance-area trade-offs. We describe a synthesis and placement flow which, for a given design to be implemented, allows us to select the best GLA from the candidate family.
8549309
Wire length prediction in constraint driven placement
Experiments show that lengths of individual wires are different for different placement algorithms. To achieve accurate wire length prediction, some knowledge of a placer's details is necessary. We postulate that wire length prediction should be coupled with placement flow to obtain accurate results. In this paper, we embed wire length prediction into our constraint-driven placer, developed in Fast Placer Implementation (FPI) framework [15]. We predict individual wire lengths during the clustering step. The predicted wire lengths act as constraints for the simulated annealing refinement stage, which guides the placement towards a solution fulfilling the predictions. Experimental results show that our wire length prediction process yields accurate results without quality loss at a small cost of placement effort. This is the first time that constraints have been used to guide placement and thus increase the accuracy of wire length prediction.
32477053
lambda-Level Rough Equality Relation and the Inference of Rough Paramodulation
In the paper, it defines a λ-level rough equality relation in rough logic and establishes a rough sets on real-valued information systems with it. We obtain some related properties and relative rough paramodulation inference rules. They will be used in the approximate reasoning and deductive resolutions of rough logic. Finally, it proves that λ-level rough paramodulant reasoning is sound.