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.
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.
16309430
Modeling, Analysis and Improvement for BitTorrent-Like File Sharing Networks
In this paper, a simple mathematical model is presented for studying the performance of the BitTorrent (1) file sharing system. We are especially interested in the distribution of the peers with different states of the download job completedness. With the model we find that in the stable state the distribution of the download peers follows a U-shaped curve, and the parameters such as the departure rate of the seeds and the abort rate of the download peers will influence the peer distribution in different ways notably. We also analyze the file availability and the dying process of the BitTorrent file sharing system. We find that the system's stability deteriorates with the clustering of the peers, and BitTorrent's built-in "tit-for-tat" unchoking strategy could not help to preserve the integrity of the file among the download peers when the size of the community is small. An innovative peer selection strategy which enables more peers to finish the download job and prolongs the system's lifetime is proposed, in which the peers cooperate to improve the stability of the system by making a tradeoff between the current download rate and the future service availability. Finally, experimental results are presented to validate our analysis and findings.
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.
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.
12598488
MLP Internal Representation as Discriminative Features for Improved Speaker Recognition
Feature projection by non-linear discriminant analysis (NLDA) can substantially increase classification performance. In automatic speech recognition (ASR) the projection provided by the pre-squashed outputs from a one hidden layer multi-layer perceptron (MLP) trained to recognise speech sub-units (phonemes) has previously been shown to significantly increase ASR performance. An analogous approach cannot be applied directly to speaker recognition because there is no recognised set of "speaker sub-units" to provide a finite set of MLP target classes, and for many applications it is not practical to train an MLP with one output for each target speaker. In this paper we show that the output from the second hidden layer (compression layer) of an MLP with three hidden layers trained to identify a subset of 100 speakers selected at random from a set of 300 training speakers in Timit, can provide a 77% relative error reduction for common Gaussian mixture model (GMM) based speaker identification.