2408931
Recognizing Textual Entailment Using a Subsequence Kernel Method
We present a novel approach to recognizing Textual Entailment. Structural features are constructed from abstract tree descriptions, which are automatically extracted from syntactic dependency trees. These features are then applied in a subsequence-kernel-based classifier to learn whether an entailment relation holds between two texts. Our method makes use of machine learning techniques using a limited data set, no external knowledge bases (e.g. WordNet), and no handcrafted inference rules. We achieve an accuracy of 74.5% for text pairs in the Information Extraction and Question Answering task, 63.6% for the RTE-2 test data, and 66.9% for the RET-3 test data.
192535
Information Synthesis for Answer Validation
This paper proposes an integration of Recognizing Textual Entailment (RTE) with other additional information to deal with the Answer Validation task. The additional information used in our participation in the Answer Validation Exercise (AVE 2008) is from named-entity (NE) recognizer, question analysis component, etc. We have submitted two runs, one run for English and the other for German, achieving f-measures of 0.64 and 0.61 respectively. Compared with our system last year, which purely depends on the output of the RTE system, the extra information does show its effectiveness.
15843432
Hybrid Constituent and Dependency Parsing with Tsinghua Chinese Treebank
In this paper, we describe our hybrid parsing model on Mandarin Chinese processing. The model combines the mainstream constitute and dependency parsing and the dataset we use it the Tsinghua Chinese Treebank, whose annotation has both constitutes and head information. We show the adaption of this annotation scheme to the normal constitute structure, dependency structure, and the integration of both. We achieve a f1-score of 85.23% for the constitute parsing, 82.35% for the partial head information, and 74.27% for the complete head information.
18797022
Using Recognizing Textual Entailment as a Core Engine for Answer Validation
This paper is about our approach to answer validation, which centered by a Recognizing Textual Entailment (RTE) core engine. We first combined the question and the answer into Hypothesis (H) and view the document as Text (T); then, we used our RTE system to check whether the entailment relation holds between them. Our system was evaluated on the Answer Validation Exercise (AVE) task and achieved f-measures of 0.46 and 0.55 for two submission runs, which both outperformed others' results for the English language.
1393275
Fast, realistic lighting and material design using nonlinear cut approximation
We present an efficient computational algorithm for functions represented by a nonlinear piecewise constant approximation called cuts. Our main contribution is a single traversal algorithm for merging cuts that allows for arbitrary pointwise computation, such as addition, multiplication, linear interpolation, and multi-product integration. A theoretical error bound of this approach can be proved using a statistical interpretation of cuts. Our algorithm extends naturally to computation with many cuts and maps easily to modern GPUs, leading to significant advantages over existing methods based on wavelet approximation. We apply this technique to the problem of realistic lighting and material design under complex illumination with arbitrary BRDFs. Our system smoothly integrates all-frequency relighting of shadows and reflections with dynamic per-pixel shading effects, such as bump mapping and spatially varying BRDFs. This combination of capabilities is typically missing in current systems. We represent illumination and precomputed visibility as nonlinear sparse vectors; we then use our cut merging algorithm to simultaneously interpolate visibility cuts at each pixel, and compute the triple product integral of the illumination, interpolated visibility, and dynamic BRDF samples. Finally, we present a two-pass, data-driven approach that exploits pilot visibility samples to optimize the construction of the light tree, leading to more efficient cuts and reduced datasets.
2756749
Privacy-preserving genomic computation through program specialization
In this paper, we present a new approach to performing important classes of genomic computations (e.g., search for homologous genes) that makes a significant step towards privacy protection in this domain. Our approach leverages a key property of the human genome, namely that the vast majority of it is shared across humans (and hence public), and consequently relatively little of it is sensitive. Based on this observation, we propose a privacy-protection framework that partitions a genomic computation, distributing the part on sensitive data to the data provider and the part on the pubic data to the user of the data. Such a partition is achieved through program specialization that enables a biocomputing program to perform a concrete execution on public data and a symbolic execution on sensitive data. As a result, the program is simplified into an efficient query program that takes only sensitive genetic data as inputs. We prove the effectiveness of our techniques on a set of dynamic programming algorithms common in genomic computing. We develop a program transformation tool that automatically instruments a legacy program for specialization operations. We also demonstrate that our techniques can greatly facilitate secure multi-party computations on large biocomputing problems.
1789572
Precomputed Visibility Cuts for Interactive Relighting with Dynamic BRDFs
This paper presents a novel PRT-based method that uses precomputed visibility cuts for interactive relighting with all-frequency environment maps and arbitrary dynamic BRDFs. Our method is inspired by the recent Lightcuts approach [24] and we parameterize distant environment lighting onto uniformly distributed sample points over the sphere. Using a binary tree structure of the points, we precompute and approximate each vertex's visibility function into clusters that we call the precomputed visibility cuts. These cuts are iteratively selected with bounded approximation error and confined cluster size. At run-time, a GPU-based relighting algorithm quickly computes the view-dependent shading color by accessing a dynamically built light tree, the precomputed visibility cuts, and a direct sampling of an arbitrary BRDF using each visibility cluster's average direction and the dynamic view direction. Compared to existing PRT techniques, our method guarantees uniform sampling of the lighting, requires no precomputed BRDF data, and can be easily extended to handle one-bounce glossy indirect transfer effects in real-time.
42705433
Interactive Time-Dependent Tone Mapping Using Programmable Graphics Hardware
3390974
All-frequency interactive relighting of translucent objects with single and multiple scattering
We present a technique, based on precomputed light transport, for interactive rendering of translucent objects under all-frequency environment maps. We consider the complete BSSRDF model proposed by Jensen et al. [2001]. which includes both single and diffuse multiple scattering components. The challenge is how to efficiently precompute all-frequency light transport functions due to subsurface scattering. We apply the two-pass hierarchical technique by Jensen et al. [2002] in the space of non-linearly approximated transport vectors, which allows us to efficiently evaluate transport vectors due to diffuse multiple scattering. We then include an approximated single scattering term in the precomputation, which previous interactive systems have ignored. For an isotropic phase function, this approximation produces a diffuse transport vector per vertex, and is combined with the multiple scattering component. For a general phase function, we introduce a technique from BRDF rendering to factor the phase function using a separable decomposition to allow for view-dependent rendering. We show that our rendering results qualitatively match the appearance of translucent objects, achieving a high level of realism at interactive rates.