18794722
Word Sense Language Model for Information Retrieval
This paper proposes a word sense language model based method for information retrieval. This method, differing from most of traditional ones, combines word senses defined in a thesaurus with a classic statistical model. The word sense language model regards the word sense as a form of linguistic knowledge, which is helpful in handling mismatch caused by synonym and data sparseness due to data limit. Experimental results based on TREC-Mandarin corpus show that this method gains 12.5% improvement on MAP over traditional tf-idf retrieval method but 5.82% decrease on MAP compared to a classic language model. A combination result of this method and the language model yields 8.92% and 7.93% increases over either respectively. We present analysis and discussions on the not-so-exciting results and conclude that a higher performance of word sense language model will owe to high accurate of word sense labeling. We believe that linguistic knowledge such as word sense of a thesaurus will help IR improve ultimately in many ways.
16416654
Topic Tracking Based on Keywords Dependency Profile
Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is "known" by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25% on training dataset and 7.49% on testing dataset comparing to baseline.
15125228
Web Mining for Lexical Context-Specific Paraphrasing
In most applications of paraphrasing, contextual information should be considered since a word may have different paraphrases in different contexts. This paper presents a method that automatically acquires lexical context-specific paraphrases from the web. The method includes two main stages, candidate paraphrase extraction and paraphrase validation. Evaluations were conducted on a news title corpus whereby the context-specific paraphrasing method was compared with the Chinese synonymous thesaurus. Results show that the precision of our method is above 60% and the recall is above 55%, which outperforms the thesaurus significantly.
656139
HIT: Web based Scoring Method for English Lexical Substitution
This paper describes the HIT system and its participation in SemEval-2007 English Lexical Substitution Task. Two main steps are included in our method: candidate substitute extraction and candidate scoring. In the first step, candidate substitutes for each target word in a given sentence are extracted from WordNet. In the second step, the extracted candidates are scored and ranked using a web-based scoring method. The substitute ranked first is selected as the best substitute. For the multiword subtask, a simple WordNet-based approach is employed.
16395245
Hierarchical face modeling and fast 3D facial expression synthesis
This paper presents a new hierarchical facial model that conforms to the human anatomy for realistic and fast 3D facial expression synthesis. The facial model has a skin/muscle/skull structure. The deformable skin model uses a kind of nonlinear spring to directly simulate the nonlinear visco-elastic behavior of soft tissue, and a new kind of edge repulsion spring is developed to prevent model collapse. The incorporation of the skull extends the scope of facial motion and facilitates facial muscle construction. The construction of facial muscles is achieved by using an efficient muscle mapping approach that ensures different muscles to be located at the anatomically correct positions. For computational efficiency, we devise an adaptive simulation algorithm which uses either a semi-implicit integration scheme or a quasi-static solver to compute the relaxation by traversing the designed data structures in a breadth-first order. The algorithm runs in real-time and has successfully synthesized realistic facial expressions.
34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
12263549
Tree-guided Bayesian inference of population structures
MOTIVATION
Inferring population structures using genetic data sampled from a group of individuals is a challenging task. Many methods either consider a fixed population number or ignore the correlation between populations. As a result, they can lose sensitivity and specificity in detecting subtle stratifications. In addition, when a large number of genetic markers are used, many existing algorithms perform rather inefficiently.


RESULT
We propose a new Bayesian method to infer population structures using multiple unlinked single nucleotide polymorphisms (SNPs). Our approach explicitly considers the population correlation through a tree hierarchy, and treat the population number as a random variable. Using both simulated and real datasets of worldwide samples, we demonstrate that an incorporated tree can consistently improve the power in detecting subtle population stratifications. A tree-based model often involves a large number of unknown parameters, and the corresponding estimation procedure can be highly inefficient. We further implement a partition method to analytically integrate out all nuisance parameters in the tree. As a result, our method can analyze large SNP datasets with significantly improved convergence rate.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/tips.tar.
5397904
Modeling and Throughput Analysis for Wireless Ad Hoc Networks
This paper presents a novel analytical model for wireless ad hoc networks. Based on the strong points in the literature, an accurate performance model for IEEE 802.11 MAC protocol is proposed. Moreover, by introducing the properties of spatial reuse, we extend the proposed model to support the throughput analysis of wireless ad hoc networks with directional antenna. An extensive set of simulations and numerical calculations validate our model by showing high accurate results.
2325109
Exploiting upper and lower bounds in top-down query optimization
System R's bottom-up query optimizer architecture forms the basis of most current commercial database managers. The paper compares the performance of top-down and bottom-up optimizers, using the measure of the number of plans generated during optimization. Top down optimizers are superior according to this measure because they can use upper and lower bounds to avoid generating groups of plans. Early during the optimization of a query, a top-down optimizer can derive upper bounds for the costs of the plans it generates. These bounds are not available to typical bottom-up optimizers since such optimizers generate and cost all subplans before considering larger containing plans. These upper bounds can be combined with lower bounds, based solely on logical properties of groups of logically equivalent subqueries, to eliminate entire groups of plans from consideration. We have implemented such a search strategy, in a top-down optimizer called Columbia. Our performance results show that the use of these bounds is quite effective, while preserving the optimality of the resulting plans. In many circumstances this new search strategy is even more effective than heuristics such as considering only left deep plans.