10483406
A maximum coherence model for dictionary-based cross-language information retrieval
One key to cross-language information retrieval is how to efficiently resolve the translation ambiguity of queries given their short length. This problem is even more challenging when only bilingual dictionaries are available, which is the focus of this paper. In the previous research of cross-language information retrieval using bilingual dictionaries, the word co-occurrence statistics is used to determine the most likely translations of queries. In this paper, we propose a novel statistical model, named ``maximum coherence model'', which estimates the translation probabilities of query words that are consistent with the word co-occurrence statistics. Unlike the previous work, where a binary decision is made for the selection of translations, the new model maintains the uncertainty in translating query words when their sense ambiguity is difficult to resolve. Furthermore, this new model is able to estimate translations of multiple query words simultaneously. This is in contrast to many previous approaches where translations of individual query words are determined independently. Empirical studies with TREC datasets have shown that the maximum coherence model achieves a relative 10% - 40% improvement in cross-language information retrieval, comparing to other approaches that also use word co-occurrence statistics for sense disambiguation.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
14575854
Query Translation Disambiguation as Graph Partitioning
Resolving ambiguity in the process of query translation is crucial to cross-language information retrieval when only a bilingual dictionary is available. In this paper we propose a novel approach for query translation disambiguation, named "spectral query translation model". The proposed approach views the problem of query translation disambiguation as a graph partitioning problem. For a given query, a weighted graph is first created for all possible translations of query words based on the co-occurrence statistics of the translation words. The best translation of the query is then determined by the most strongly connected component within the graph. The proposed approach distinguishes from previous approaches in that the translations of all query words are estimated simultaneously. Furthermore, translation probabilities are introduced in the proposed approach to capture the uncertainty in translating queries. Empirical studies with TREC datasets have shown that the spectral query translation model achieves a relative 20% -50% improvement in cross-language information retrieval, compared to other approaches that also exploit word co-occurrence statistics for query translation disambiguation.
34486970
Simulation of miscible binary mixtures based on lattice Boltzmann method
Miscible fluid mixtures, like pouring honey into water, Coca Cola into strong wine, are common phenomena in our daily life. While two miscible fluids are mixed together, their appearances in terms of colors and shapes will change due to their mixing interaction. The interaction between the mixture components could be regarded as a combination of the diffusing process and demixing process. If the former dominates the interaction, it is miscible; otherwise, it is immiscible. The complex microscopic interplay between the mixture components makes the simulation highly challenging. So far, there have been some dedicated research in computer graphics dealing with immiscible mixtures, but few works have been done focusing on miscible mixtures. In this paper, for the first time, we introduce a two-fluid lattice Boltzmann method (LBM), called TFLBM, applied to miscible binary mixtures. Different from other similar methods, the viscous and diffusing properties of the fluid in our work are considered separately, so that the physical insight is exposed more clearly and rationally. In addition, the operation of LBM is mostly a linear local computation, and graphics processing unit (GPU) has been utilized to achieve real-time simulation. Copyright © 2006 John Wiley & Sons, Ltd.
6564204
A robust method for shape-based 3D model retrieval
Proliferation of 3D models necessitates developing efficient methods for indexing or retrieving the models in a large database. Many previous methods for this purpose defined functions on concentric spheres as approximation of 3D geometry for spherical harmonic transform (SHT). In this paper, we point out that this is not robust as the surface of a model may shift between different shells under perturbation, and multi-layer of surfaces may exist in one shell, making the function definition ambiguous. To solve these problems, we propose a method to characterize 3D shape using delta functions. Then, spherical functions are defined by sampling in the frequency domain of the delta functions for SHT. By doing so, our method can support retrieval with controllable acuity, which benefits wider range of applications and facilitates customization to different users. Experiments have shown that our method is more robust than previous approaches.
16112032
Fast Evolution Strategies
Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. These results, along with those obtained by fast evolutionary programming
6989862
Using Prosody for Automatic Sentence Segmentation of Multi-party Meetings
We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter) We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR) We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement.
15838270
Eliminating Dead Code on Recursive Data
This paper describes a general and powerful method for dead code analysis and elimination in the presence of recursive data constructions. We represent partially dead recursive data using liveness patterns based on general regular tree grammars extended with the notion of live and dead, and we formulate the analysis as computing liveness patterns at all program points based on program semantics. This analysis yields a most precise liveness pattern for the data at each program point, which is significantly more precise than results from previous methods. The analysis algorithm takes cubic time in terms of the size of the program in the worst case but is very efficient in practice, as shown by our prototype implementation. The analysis results are used to identify and eliminate dead code. The general framework for representing and analyzing properties of recursive data structures using general regular tree grammars applies to other analyses as well.