We address the problem of selecting nondomain-speciﬁc language model training data to build auxiliary language models for use in tasks such as machine translation. Our approach is based on comparing the cross-entropy, according to domainspeciﬁc and non-domain-specifc language models, for each sentence of the text source used to produce the latter language model. We show that this produces better language models, trained on less data, than both random data selection and two other previously proposed methods. ...
If you were to ask a random sampling of people what data analysis is, most
would say that it is the process of calculating and summarizing data to get
an answer to a question. In one sense, they are correct. However, the
actions they are describing represent only a small part of the process
known as data analysis
Technical Analysts often find a system or technical method that seems
extremely profitable and convenient to follow - one that they think has been
overlooked by the professionals. Sometimes they are right, but most often that
method doesn't work in practical trading or for a longer time.
Technical analysis uses price and related data to decide when to buy and sell.
The methods used can be interpretive as chart patterns and astrology, or as
specific as mathematical formulas and spectral analysis. All factors that
influence the markets are assumed to be netted out as the current price....
This study examines the technical and scale efficiencies for a sample of irrigated and rainfed rice farmers in Anambra State, using data envelopment analysis (DEA). Two(2) local government areas were purposively selected; three communities were randomly selected giving a total of six(6) communities.
•No restrictions on which operation can be used on the list
•No restrictions on where data can be inserted/deleted.
Unordered list(random list): Data are not in particular order.
Ordered list: data are arranged according to a key.
Nowadays, there are large amounts of data available to train statistical machine translation systems. However, it is not clear whether all the training data actually help or not. A system trained on a subset of such huge bilingual corpora might outperform the use of all the bilingual data. This paper studies such issues by analysing two training data selection techniques: one based on approximating the probability of an indomain corpus; and another based on infrequent n-gram occurrence.
Frequency distribution models tuned to words and other linguistic events can predict the number of distinct types and their frequency distribution in samples of arbitrary sizes. We conduct, for the ﬁrst time, a rigorous evaluation of these models based on cross-validation and separation of training and test data. Our experiments reveal that the prediction accuracy of the models is marred by serious overﬁtting problems, due to violations of the random sampling assumption in corpus data. We then propose a simple pre-processing method to alleviate such non-randomness problems. ...
In this paper, we explore the power of randomized algorithm to address the challenge of working with very large amounts of data. We apply these algorithms to generate noun similarity lists from 70 million pages. We reduce the running time from quadratic to practically linear in the number of elements to be computed.
This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random ﬁelds (CRFs). The models are encoded as deterministic weighted ﬁnite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the beneﬁt of automatically selecting a relatively small feature set in just a couple of passes over the training data. ...
Lecture "Advanced Econometrics (Part II) - Chapter 10: Models for panel data" presentation of content: General framework for panel data, pooled regression, fixed effects, random effects model, choosing between fixed and random effects models, finding big.
This paper presents a semi-supervised training method for linear-chain conditional random ﬁelds that makes use of labeled features rather than labeled instances. This is accomplished by using generalized expectation criteria to express a preference for parameter settings in which the model’s distribution on unlabeled data matches a target distribution. We induce target conditional probability distributions of labels given features from both annotated feature occurrences in context and adhoc feature majority label assignment. ...
Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artiﬁcially short sentences, we present the ﬁrst general, featurerich discriminative parser, based on a conditional random ﬁeld model, which has been successfully scaled to the full WSJ parsing data.
This paper presents an efﬁcient inference algorithm of conditional random ﬁelds (CRFs) for large-scale data. Our key idea is to decompose the output label state into an active set and an inactive set in which most unsupported transitions become a constant. Our method uniﬁes two previous methods for efﬁcient inference of CRFs, and also derives a simple but robust special case that performs faster than exact inference when the active sets are sufﬁciently small. We demonstrate that our method achieves dramatic speedup on six standard natural language processing problems. ...
In this paper we present a novel approach for inducing word alignments from sentence aligned data. We use a Conditional Random Field (CRF), a discriminative model, which is estimated on a small supervised training set. The CRF is conditioned on both the source and target texts, and thus allows for the use of arbitrary and overlapping features over these data. Moreover, the CRF has efﬁcient training and decoding processes which both ﬁnd globally optimal solutions.
We present a new semi-supervised training procedure for conditional random ﬁelds (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled training data. Our approach is based on extending the minimum entropy regularization framework to the structured prediction case, yielding a training objective that combines unlabeled conditional entropy with labeled conditional likelihood.
Conditional random ﬁelds (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and namedentity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on unlabeled data, we require unsupervised estimation methods for log-linear models; few exist. We describe a novel approach, contrastive estimation. We show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efﬁcient. ...
Chapter 12 is devoted to access control, the duties of the data link layer that are related to the use of the physical layer. The main contents of this chapter include all of the following: Random access, controlled access, channelization.
(BQ) Part 1 book "Data structures and problem solving using C++" has contents: Arrays, pointers & structures; objects & classes; templates, design patterns, algorithm analysis, recursion, randomization, utilities, simulation, graphs & paths,... and other contents.
Econometricians, as well as other scientists, are engaged in learning from their
experience and data - a fundamental objective of science. Knowledge so obtained
may be desired for its own sake, for example to satisfy our curiosity about aspects
of economic behavior and/or for use in solving practical problems, for example
to improve economic policymaking. In the process of learning from experience
and data, description and generalization both play important roles.
This compendium aims at providing a comprehensive overview of the main topics that appear
in any well-structured course sequence in statistics for business and economics at the
undergraduate and MBA levels. The idea is to supplement either formal or informal statistic
textbooks such as, e.g., “Basic Statistical Ideas for Managers” by D.K. Hildebrand and R.L.
Ott and “The Practice of Business Statistics: Using Data for Decisions” by D.S. Moore,
G.P. McCabe, W.M. Duckworth and S.L. Sclove, with a summary of theory as well as with
a couple of extra examples.