Learning to transform

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  • We propose a novel technique of learning how to transform the source parse trees to improve the translation qualities of syntax-based translation models using synchronous context-free grammars. We transform the source tree phrasal structure into a set of simpler structures, expose such decisions to the decoding process, and find the least expensive transformation operation to better model word reordering.

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  • 1. The fox was unsuccessful in reaching the grapes.(VAIN) →The fox tried in vain to reach the grapes. 2. The crops were badly affected by the storm. (EFFECT) →The storm had a badly effect on the crops. 3. The project received unanimous approval of the committee.(FAVOUR) →The whole committee was/were in favour of the project. 4. The personnel officer promised him that she wouldn’t tell any one that he had been in the prison. (WORD) →The personnel officer gave him her word that she… 5. Nobody could possibly believe the story...

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  • his book shows how to transform the information in the USB 3.0 specifications into functioning devices and application software that communicates with the devices. To help build a foundation for design decisions, developers are guided in selecting device-controller hardware. Developers will also learn the benefits of the USB interface, its limitations, and how certain design choices made at the beginning of the project can reduce development time. Recent developments in host and device hardware, more detail on the standard USB classes, application examples using Microsoft's .

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  • Chapter 2: DSP, filters and the fourier transform. In this chapter, you learned to: Digital signal processing and digital audio recap from CM2202; relationship between amplitude, frequency and phase; basic DSP concepts and definitions; Why use decibel scales?...

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  • If we report what another person has said, we usually do not use the speaker’s exact words (direct speech), but reported (indirect) speech. Therefore, you need to learn how to transform direct speech into reported speech. The structure is a little different depending on whether you want to transform a statement, question or request

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  • For every four change efforts undertaken in business today- three of them fail to change anything at all, or they actually make things worse To learn strategies for correcting this alarming trend,

    pdf21p thuthuy 20-07-2009 183 48   Download

  • Teachers and educational leaders are extraordinarily busy, inundated with demands for more work and better results with fewer resources—and less time. You will decide within the next few paragraphs whether this book is worth your time. Let me come straight to the point. Accountability for Learning equips teachers and leaders with the ability to transform educational accountability policies from destructive and demoralizing accounting drills into meaningful and constructive decision making in the classroom, school, and district.

    pdf169p bimap_5 28-12-2012 61 16   Download

  • Robotics deals with the control of actuators using various types of sensors and control schemes. The availability of precise sensorimotor mappings – able to transform between various involved motor, joint, sensor, and physical spaces – is a crucial issue. These mappings are often highly nonlinear and sometimes hard to derive analytically. Consequently, there

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  • Evidence shows that social media are already affecting the ways in which people find, create, share and learn knowledge, through rich media opportunities and in collaboration with each other. These practices are at the core of Education and Training, as they promote the competences needed for future jobs and enable new tools for educational institutions to transform themselves into places that support the competences needed for participation in a knowledge-based society.

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  • We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by previously proposed unsupervised dimensionality reduction methods (e.g., PCA). Through a variety of experiments on different realworld datasets, we find IDML-IT, a semisupervised metric learning algorithm to be the most effective.

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  • Word Sense Disambiguation suffers from a long-standing problem of knowledge acquisition bottleneck. Although state of the art supervised systems report good accuracies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an approach for learning coarser and more general set of concepts from a sense tagged corpus, in order to alleviate the knowledge acquisition bottleneck.

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  • The best way to learn about a subject, I now realise, is to write a book about it. Another good way is to teach it. In 1999, University College London (UCL) started a postgraduate programme in Health Informatics. As the programme director it was largely my responsibility to define the curriculum, a somewhat daunting task in a new and ill-defined subject.

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  • We demonstrate that transformation-based learning can be used to correct noisy speech recognition transcripts in the lecture domain with an average word error rate reduction of 12.9%. Our method is distinguished from earlier related work by its robustness to small amounts of training data, and its resulting efficiency, in spite of its use of true word error rate computations as a rule scoring function.

    pdf9p hongphan_1 14-04-2013 34 2   Download

  • For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice.

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  • In this paper we describe a new technique for parsing free text: a transformational grammar I is automatically learned that is capable of accurately parsing text into binary-branching syntactic trees with nonterminals unlabelled. The algorithm works by beginning in a very naive state of knowledge about phrase structure. By repeatedly comparing the results of bracketing in the current state to proper bracketing provided in the training corpus, the system learns a set of simple structural transformations that can be applied to reduce error.

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  • We investigate the possibility of exploiting character-based dependency for Chinese information processing. As Chinese text is made up of character sequences rather than word sequences, word in Chinese is not so natural a concept as in English, nor is word easy to be defined without argument for such a language. Therefore we propose a character-level dependency scheme to represent primary linguistic relationships within a Chinese sentence. The usefulness of character dependencies are verified through two specialized dependency parsing tasks.

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  • The object of T B L is to learn an ordered sequence of transformation rules. Such rules dictate when - based on the context - a word should have its tag changed. An example would be "replace tag vb with nn if the word immediately to the left has a tag dr." Here is how this rule is represented in the # - T B L rule/template formalism:

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  • Chapter 5 introduce z-Transform. In this chapter, you learned to: The z-transform, properties of the z-transform, causality and stability, inverse z-transform.

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  • Written documents created through dictation differ significantly from a true verbatim transcript of the recorded speech. This poses an obstacle in automatic dictation systems as speech recognition output needs to undergo a fair amount of editing in order to turn it into a document that complies with the customary standards. We present an approach that attempts to perform this edit from recognized words to final document automatically by learning the appropriate transformations from example documents. ...

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  • Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of decision trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to four phrase chunking tasks: Portuguese noun phrase chunking, English base noun phrase chunking, English text chunking and Hindi text chunking. In all four tasks, ETL shows better results than Decision Trees and also than TBL with hand-crafted templates.

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