9436642
A Kernel PCA Method for Superior Word Sense Disambiguation
We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best published individual models. We present empirical results demonstrating significantly better accuracy compared to the state-of-the-art achieved by either naive Bayes or maximum entropy models, on Senseval-2 data. We also contrast against another type of kernel method, the support vector machine (SVM) model, and show that our KPCA-based model outperforms the SVM-based model. It is hoped that these highly encouraging first results on KPCA for natural language processing tasks will inspire further development of these directions.
37163274
Concretion Inferences in NAtural Language Understanding
This paper introduces the architecture of FRESCO, an integrated inference mechanism and knowledge representation suited to a cognitive model of natural language understanding processes. A particular type of disambiguation inference known as concretion which occurs in language interpretation is examined within this framework. I put forth some principles resolving several outstanding issues surrounding concretion inferences, and describe a technique called probabilistic map passing which is employed within FRESCO to implement these principles.
74294
A Polynomial-Time Algorithm for Statistical Machine Translation
We introduce a polynomial-time algorithm for statistical machine translation. This algorithm can be used in place of the expensive, slow best-first search strategies in current statistical translation architectures. The approach employs the stochastic bracketing transduction grammar (SBTG) model we recently introduced to replace earlier word alignment channel models, while retaining a bigram language model. The new algorithm in our experience yields major speed improvement with no significant loss of accuracy.
1713067
Improving Chinese Tokenization With Linguistic Filters On Statistical Lexical Acquisition
The first step in Chinese NLP is to tokenize or segment character sequences into words, since the text contains no word delimiters. Recent heavy activity in this area has shown the biggest stumbling block to be words that are absent from the lexicon, since successful tokenizers to date have been based on dictionary lookup (e.g., Chang & Chen 1993; Chiang et al. 1992; Lin et al. 1993; Wu & Tseng 1993; Sproat et al. 1994).We present empirical evidence for four points concerning tokenization of Chinese text: (1) More rigorous "blind" evaluation methodology is needed to avoid inflated accuracy measurements; we introduce the nk-blind method. (2) The extent of the unknown-word problem is far more serious than generally thought, when tokenizing unrestricted texts in realistic domains. (3) Statistical lexical acquisition is a practical means to greatly improve tokenization accuracy with unknown words, reducing error rates as much as 32.0%. (4) When augmenting the lexicon, linguistic constraints can provide simple inexpensive filters yielding significantly better precision, reducing error rates as much as 49.4%.
8762213
An Algorithm for Simultaneously Bracketing Parallel Texts by Aligning Words
We describe a grammarless method for simultaneously bracketing both halves of a parallel text and giving word alignments, assuming only a translation lexicon for the language pair. We introduce inversion-invariant transduction grammars which serve as generative models for parallel bilingual sentences with weak order constraints. Focusing on transduction grammars for bracketing, we formulate a normal form, and a stochastic version amenable to a maximum-likelihood bracketing algorithm. Several extensions and experiments are discussed.
8360657
Machine Translation with a Stochastic Grammatical Channel
We introduce a stochastic grammatical channel model for machine translation, that synthesizes several desirable characteristics of both statistical and grammatical machine translation. As with the pure statistical translation model described by Wu (1996) (in which a bracketing transduction grammar models the channel), alternative hypotheses compete probabilistically, exhaustive search of the translation hypothesis space can be performed in polynomial time, and robustness heuristics arise naturally from a language-independent inversion-transduction model. However, unlike pure statistical translation models, the generated output string is guaranteed to conform to a given target grammar. The model employs only (1) a translation lexicon, (2) a context-free grammar for the target language, and (3) a bigram language model. The fact that no explicit bilingual translation rules are used makes the model easily portable to a variety of source languages. Initial experiments show that it also achieves significant speed gains over our earlier model.
26320424
Aurelia: Building Locality-Preserving Overlay Network over Heterogeneous P2P Environments
Traditional DHT-based overlays destroy data locality and make it hard to support complex queries (e.g., range query, similarity query, etc) in P2P systems. Additionally, the node heterogeneity is widely ignored in most existing overlay designs. In this paper, we propose a locality-preserving overlay design, called Aurelia, which can adapt to the heterogeneous P2P environment and exploit node heterogeneity to realize efficient routing and robust indexing. Aurelia preserves the data locality by abandoning the use of hashing for data placement and each peer is responsible for a continuous range of value. In Aurelia, the routing table size and index range are proportional to the node capacity, and multicasting is adopted for scalable routing table maintenance.
12664404
Size Does Matter in Computer Collaboration: Heterogeneous Platform Effects on Human-Human Interaction
Because today's workforce is highly mobile, small wireless devices are being used to support mobile work collaboration. However, do computer platform differences affect such collaborations? This question is investigated through a controlled experiment that examines collaborative problem solving on different combinations of small and large computers. Experiment participants in the study work together on solving 2- and 3-dimensional variations of the popular Tetris™ game. Gender is used as a moderating variable to ascertain if prior observed effects on groups of males would be found among females. The findings indicate that platform differences affect communication and social behavior among both groups. Unexpectedly, collaboration amongst partners using small handhelds was the most cooperative and friendly because of the difficulty of solving the problem with the small device.
16461681
Analytical Study on Improving DHT Lookup Performance under Churn
The phenomenon of churn degrades the lookup performance of DHT-based P2P systems greatly. To date, a number of approaches have been proposed to handle it from both the system side and the client side. However, there lacks theoretical analysis to direct how to make design choices under different churn levels and how to configure their parameters optimally. In this paper, we analytically study three important aspects on improving DHT lookup performance under churn, i.e., lookup strategy, lookup parallelism and lookup key replication. Our objective is to build a theoretical basis for DHT designers to make better design choices in the future. We first compare the performance of two representative lookup strategies - recursive routing and iterative routing, and explore the existence of better alternatives. Then we show the effectiveness of parallel lookup in systems with different churn levels and how to select the optimal degree of parallelism. Due to the importance of key replication on lookup performance, we also analyze the reliability of replicated keys under two different replication policies, and discuss how to make configuration in different environments. Besides analytical study, our results are also validated by simulation, and Kad is taken as a case to show the meaningfulness of our analysis
1529609
Roogle: Supporting Efficient High-Dimensional Range Queries in P2P Systems
Multi-dimensional range query is an important query type and especially useful when the user doesn't know exactly what he is looking for. However, due to improper indexing method and high routing latency, existing schemes cannot perform well under high-dimensional situations. In this paper, we propose Roogle, a decentralized non-flooding P2P search engine that can efficiently support high-dimensional range queries in P2P systems. Roogle makes improvements on both indexing and routing. The high-dimensional data is indexed based on the maximum or minimum value among all dimensions. This simple indexing method performs rather well under high-dimensional situations and tolerates data points with missing values or different dimensionality. To speed query routing, Roogle is built on top of our proposed structured overlay – Aurelia, which has better routing performance by exploiting node heterogeneity. Aurelia also guarantees the data locality and efficiently support range queries. Experimental results from simulation validate the scalability and efficiency of Roogle.
16094294
Exploring a Two-Population Genetic Algorithm
In a two-market genetic algorithm applied to a constrained optimization problem, two 'markets' are maintained. One market establishes fitness in terms of the objective function only; the other market measures fitness in terms of the problem constraints only. Previous work on knapsack problems has shown promise for the two-market approach. In this paper we: (1) extend the investigation of two-market GAs to non-linear optimization, (2) introduce a new, two-population variant on the two-market idea, and (3) report on experiments with the two-population, two-market GA that help explain how and why it works.