How to Do Things With Words

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Summary: Since the introduction of penicillin, β-lactam antibiotics have been the antimicrobial agents of choice. Unfortunately, the efficacy of these life-saving antibiotics is significantly threatened by bacterial β-lactamases. β-Lactamases are now responsible for resistance to penicillins, extended-spectrum cephalosporins, monobactams, and carbapenems. In order to overcome β-lactamase-mediated resistance, β-lactamase inhibitors (clavulanate, sulbactam, and tazobactam) were introduced into clinical practice. These inhibitors greatly enhance the efficacy of their partner β-lactams (amoxicillin, ampicillin, piperacillin, and ticarcillin) in the treatment of serious Enterobacteriaceae and penicillin-resistant staphylococcal infections. However, selective pressure from excess antibiotic use accelerated the emergence of resistance to β-lactam-β-lactamase inhibitor combinations. Furthermore, the prevalence of clinically relevant β-lactamases from other classes that are resistant to inhibition is rapidly increasing. There is an urgent need for effective inhibitors that can restore the activity of β-lactams. Here, we review the catalytic mechanisms of each β-lactamase class. We then discuss approaches for circumventing β-lactamase-mediated resistance, including properties and characteristics of mechanism-based inactivators. We next highlight the mechanisms of action and salient clinical and microbiological features of β-lactamase inhibitors. We also emphasize their therapeutic applications. We close by focusing on novel compounds and the chemical features of these agents that may contribute to a “second generation” of inhibitors. The goal for the next 3 decades will be to design inhibitors that will be effective for more than a single class of β-lactamases.

Three Decades of β-Lactamase Inhibitors

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech recognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a framework. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems.

/pdf/pomdp-based-statistical-spoken-dialog-systems-a-review-52td9wfczr.pdf

POMDP-Based Statistical Spoken Dialog Systems: A Review

Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs.

Whereas the basic principles underlying HMM-based LVCSR are rather straightforward, the approximations and simplifying assumptions involved in a direct implementation of these principles would result in a system which has poor accuracy and unacceptable sensitivity to changes in operating environment. Thus, the practical application of HMMs in modern systems involves considerable sophistication.

The aim of this review is first to present the core architecture of a HMM-based LVCSR system and then describe the various refinements which are needed to achieve state-of-the-art performance. These refinements include feature projection, improved covariance modelling, discriminative parameter estimation, adaptation and normalisation, noise compensation and multi-pass system combination. The review concludes with a case study of LVCSR for Broadcast News and Conversation transcription in order to illustrate the techniques described.

/pdf/application-of-hidden-markov-models-in-speech-recognition-4ic3ad3d5g.pdf

Application of Hidden Markov Models in Speech Recognition

One of the first steps in building a spoken language understanding (SLU) module for dialogue systems is the extraction of flat concepts out of a given word sequence, usually provided by an automatic speech recognition (ASR) system. In this paper, six different modeling approaches are investigated to tackle the task of concept tagging. These methods include classical, well-known generative and discriminative methods like Finite State Transducers (FSTs), Statistical Machine Translation (SMT), Maximum Entropy Markov Models (MEMMs), or Support Vector Machines (SVMs) as well as techniques recently applied to natural language processing such as Conditional Random Fields (CRFs) or Dynamic Bayesian Networks (DBNs). Following a detailed description of the models, experimental and comparative results are presented on three corpora in different languages and with different complexity. The French MEDIA corpus has already been exploited during an evaluation campaign and so a direct comparison with existing benchmarks is possible. Recently collected Italian and Polish corpora are used to test the robustness and portability of the modeling approaches. For all tasks, manual transcriptions as well as ASR inputs are considered. Additionally to single systems, methods for system combination are investigated. The best performing model on all tasks is based on conditional random fields. On the MEDIA evaluation corpus, a concept error rate of 12.6% could be achieved. Here, additionally to attribute names, attribute values have been extracted using a combination of a rule-based and a statistical approach. Applying system combination using weighted ROVER with all six systems, the concept error rate (CER) drops to 12.0%.

/pdf/comparing-stochastic-approaches-to-spoken-language-4uklbye9o7.pdf

Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages

We have developed a foolproof method to substitute a stretch of residues by alanines. After the introduction of aPstI site by IPCR, thus creating two alanine codons, additional codons are introduced at this site through the use of an 'alanine-stretch cartridge'. These cartridges comprise an antibiotic resistance gene flanked on both sides by alanine codons. Excision of the resistance gene byPvuII then yields the correct insertion of codons. The method is both highly reliable and flexible and should be of general use.

Alanine-stretch scanning mutagenesis: a simple and efficient method to probe protein structure and function

Spoken Dialogue Systems (SDS) are man-machine interfaces which use natural language as the medium of interaction. Dialogue corpora collection for the purpose of training and evaluating dialogue systems is an expensive process. User simulators aim at simulating human users in order to generate synthetic data. Existing methods for user simulation mainly focus on generating data with the same statistical consistency as in some reference dialogue corpus. This paper outlines a novel approach for user simulation based on Inverse Reinforcement Learning (IRL). The task of building the user simulator is perceived as a task of imitation learning.

https://hal-supelec.archives-ouvertes.fr/hal-00652446/document

User Simulation in Dialogue Systems Using Inverse Reinforcement Learning.

The invention concerns a method for producing in vitro recombined polynucleotide sequences from a bank of polynucleotide sequences, characterised in that it comprises the following steps: fragmenting an initial bank of double-strand polynucleotide sequences; (b) denaturation of the fragments resulting from step (a) optionally in the presence of one or several assembling matrix or matrices; (c) hybridising said fragments with one or several of the assembling matrix or matrices if the latter is/are not present in step (b); (d) ligation of said fragments; (e) optionally cloning said recombined polynucleotide sequences having advantageous properties with respect to one or several reference sequences.

Method for obtaining in vitro recombined polynucleotide sequences, sequence banks and resulting sequences

The aim of the MEDIA-EVALDA project is to evaluate the understanding capabilities of dialog systems. This paper presents the MEDIA protocol for speech understanding evaluation and describes the results of the June 2005 literal evaluation campaign. Five systems, both symbolic or corpus-based participated to the evaluation which is based on a common semantic representation. Different scorings have been performed on the system results. The understanding error rate, for the Full scoring is, depending on the systems, from 29% to 41.3%. A diagnosis analysis of these results is proposed.

/pdf/results-of-the-french-evalda-media-evaluation-campaign-for-28k77umm64.pdf

Fabrice Lefèvre

Papers

Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages

Alanine-stretch scanning mutagenesis: a simple and efficient method to probe protein structure and function

User Simulation in Dialogue Systems Using Inverse Reinforcement Learning.

Method for obtaining in vitro recombined polynucleotide sequences, sequence banks and resulting sequences

Results of the French Evalda-Media evaluation campaign for literal understanding