When accurately tagging corpora for training and testing automatic POS taggers, research issues tend to focus on how to ensure the integrity and consistency of the data produced. The issues we address here are instead: Provided that integrity and consistency are guaranteed, how to evaluate

The abbreviations used are: SILAC: Stable isotope labeling by amino acids in cell culture, 2DE: two dimensional (isoelectric focusing/SDS-PAGE) gel electrophoresis: ICATTM: isotope-coded affinity tag; MS: mass spectrometry; MALDI-TOF: matrix assisted laser desorption ionization-time of flight; PMF

according to the current number of tags. Thus it is important to estimate the number of tags accurately. In this paper, we propose a novel tag estimation method for DFSA. We compare the performance of the proposed method with those of other existing methods. And, simulation results show that our scheme

We describe a platform for context-aware computing which enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies

We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies

do not necessarily involve trading off accuracy against speed—they may in fact be more accurate than exhaustive-search stochastic contextfree parsers. 1 Finite-State Cascades Of current interest in corpus-oriented computational linguistics are techniques for bootstrapping broad-coverage parsers from

Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive

-localization). In addition to de novo predictions, it can integrate often noisy, experimental interaction datasets. We observe that at given levels of sensitivity our predictions are more accurate than the existing highthroughput experimental datasets. We validate our predictions with new TAP-tagging experiments. Our

Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. In this paper, we present a dynamic analysis methodology that automates

for combining unsupervised and supervised rule-based training algorithms to create a highly accurate tagger using only a small amount of manually tagged text.