For recently years the research of adaptation of computer education has been an important topic. Although Adaptive Educational Hypermedia Systems (AEHS) are different disciplines with IMS Learning Design (IMS LD), they have the same goal is to create the best possible environment for a learner to perform his/her learning activities in. How IMS LD addresses many requirements for computer based adaptation and personalized e-Learning is one of the main concerns for researcher in this field.
This book emerged from a stream of research conducted in CASTLE Laboratory at
Princeton University during the period 2006 - 2011. Initially, the work was motivated
by the "exploration vs. exploitation" problem that arises in the design of algorithms
for approximate dynamic programming, where it may be necessary to visit a state to
learn the value of being in the state. However, we quickly became aware that this
basic question had many applications outside of dynamic programming.
The results of this research were made possible by the efforts and contributions of
We present a joint model for Chinese word segmentation and new word detection. We present high dimensional new features, including word-based features and enriched edge (label-transition) features, for the joint modeling. As we know, training a word segmentation system on large-scale datasets is already costly.
This paper presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration, and acquires knowledge iteratively from the Web. We study the active learning and the unsupervised learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. ...
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Google and YouTube use Python because it's highly adaptable, easy to maintain, and allows for rapid development. If you want to write high-quality, efficient code that's easily integrated with other languages and tools, this hands-on book will help you be productive with Python quickly -- whether you're new to programming or just new to Python. It's an easy-to-follow self-paced tutorial, based on author and Python expert Mark Lutz's popular training course.
dvances in technology are increasingly impacting the way in which curriculum is delivered and assessed. The emergence of the Internet has offered learners a new instructional delivery system that connects them with educational resources.
In this paper, we study the problem of using an annotated corpus in English for the same natural language processing task in another language. While various machine translation systems are available, automated translation is still far from perfect. To minimize the noise introduced by translations, we propose to use only key ‘reliable” parts from the translations and apply structural correspondence learning (SCL) to ﬁnd a low dimensional representation shared by the two languages.
We consider the problem of correcting errors made by English as a Second Language (ESL) writers and address two issues that are essential to making progress in ESL error correction - algorithm selection and model adaptation to the ﬁrst language of the ESL learner. A variety of learning algorithms have been applied to correct ESL mistakes, but often comparisons were made between incomparable data sets. We conduct an extensive, fair comparison of four popular learning methods for the task, reversing conclusions from earlier evaluations. ...
We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at query level than document level.
We present a pointwise approach to Japanese morphological analysis (MA) that ignores structure information during learning and tagging. Despite the lack of structure, it is able to outperform the current state-of-the-art structured approach for Japanese MA, and achieves accuracy similar to that of structured predictors using the same feature set. We also ﬁnd that the method is both robust to outof-domain data, and can be easily adapted through the use of a combination of partial annotation and active learning. ...
Accurate unsupervised learning of phonemes of a language directly from speech is demonstrated via an algorithm for joint unsupervised learning of the topology and parameters of a hidden Markov model (HMM); states and short state-sequences through this HMM correspond to the learnt sub-word units. The algorithm, originally proposed for unsupervised learning of allophonic variations within a given phoneme set, has been adapted to learn without any knowledge of the phonemes.
We describe the adaptation to French of a machine-learned sentence realization system called Amalgam that was originally developed to be as language independent as possible and was first implemented for German. We discuss the development of the French implementation with particular attention to the degree to which the original system could be reused, and we present the results of a human evaluation of the quality of sentence realization using the new French system.
The paper presents an application of Structural Correspondence Learning (SCL) (Blitzer et al., 2006) for domain adaptation of a stochastic attribute-value grammar (SAVG). So far, SCL has been applied successfully in NLP for Part-of-Speech tagging and Sentiment Analysis (Blitzer et al., 2006; Blitzer et al., 2007). An attempt was made in the CoNLL 2007 shared task to apply SCL to non-projective dependency parsing (Shimizu and Nakagawa, 2007), however, without any clear conclusions.
We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-speciﬁc entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall.
Adaptive Dialogue Systems are rapidly becoming part of our everyday lives. As they progress and adopt new technologies they become more intelligent and able to adapt better and faster to their environment. Research in this ﬁeld is currently focused on how to achieve adaptation, and particularly on applying Reinforcement Learning (RL) techniques, so a comparative study of the related methods, such as this, is necessary.
This paper presents a method for the automatic extraction of subgrammars to control and speeding-up natural language generation NLG. The method is based on explanation-based learning EBL. The main advantage for the proposed new method for NLG is that the complexity of the grammatical decision making process during NLG can be vastly reduced, because the EBL method supports the adaption of a NLG system to a particular use of a language.
Best practices in adaptive the assessment of adaptive behavior focuses on adaptive behavior assessment as an important component of data-based, decision-making/problem-solving models of school psychological services for students with disabilities and other learning and behavior problems. Specific assessment methodologies are described along with their respective benefits and limitations. Additionally, the chapter describes and classifies the types of adaptive behavior difficulties that are most frequently associated with specific disabilities (e.g.
We find that for operational forms of policy rules, ie rules that do not depend on contemporaneous values of endogenous aggregate variables, many interest-rate rules do not exhibit robust stability. We consider a variety of interest-rate rules, including instrument rules, optimal reaction functions under discretion or commitment, and rules that approximate optimal policy under commitment.