13137405
Estimating Probability Distributions over Hypotheses with Variable Unification
We analyze the difficulties in applying Bayesian belief networks to language interpretation domains, which typically involve many unification hypotheses that posit variable bindings. As an alternative, we observe that the structure of the underlying hypothesis space permits an approximate encoding of the joint distribution based on marginal rather than conditional probabilities. This suggests an implicit binding approach that circumvents the problems with explicit unification hypotheses, while still allowing hypotheses with alternative unifications to interact probabilistically. The proposed method accepts arbitrary subsets of hypotheses and marginal probability constraints, is robust, and is readily incorporated into standard unification-based and frame-based models.
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.
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.
8337549
Exploring A Two-market Genetic Algorithm
The ordinary genetic algorithm may be thought of as conducting a single market in which solutions compete for success, as measured by the fitness funtion. We introduce a two-market genetic algorithm, consisting of two phases, each of which is an ordinary single-market genetic algorithm. The two-market genetic algorithm has a natural interpretation as a method of solving constrained optimization problems. Phase 1 is optimality improvement; it works on the problem without regard to constraints. Phase 2 is feasibility improvement; it works on the existing population of solutions and drives it towards feasibility. We tested this concept on 14 standard knapsack test problems for genetic algorithms, with excellent results. The paper concludes with discussions of why the two-market genetic algorithm is successful and of how this work can be extended.
5599051
Constructivist learning with participatory examinations
Can we improve the examination process, making it more enjoyable and increasing perceived learning? The participatory exam process provides opportunities for students to learn from creating, reading, answering and grading exam questions. An online asynchronous learning network system facilitates the process. This paper presents the process framework, our research model and experimental results. A majority of students preferred the participatory exam and believed that it increased their learning.
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.
207751
PIDD: database for Protein Inter-atomic Distance Distributions
Protein Inter-atomic Distance Distributions (PIDD) is a dedicated database and structural bio-informatics system for distance based protein modeling. The database is developed to host and analyze the statistical data for protein inter-atomic distances based on their distributions in databases of known protein structures such as in the Protein Data Bank (PDB). PIDD is capable of generating, caching, and displaying the statistical distributions of the distances of various types and ranges. The collected information can be used to extract geometric restraints or mean-force potentials for protein structure determination including nuclear magnetic resonance structure determination and comparative model refinement. PIDD is supported with a friendly designed web interface so that users can easily specify the distance types and ranges, and retrieve, visualize or download the distributions of the distances as they desire. PIDD is freely accessible at
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.