The A–Z of Correct English is a reference book which has been written for The student and The general reader. It aims to tackle The basic questions about spelling, punctuation, grammar and word usage that The student and The general reader are...
The GMAT always prefers the sentence that expresses ideas most clearly and succintly. Although style is not usually the only thing that makes an answer choice correct you can very often use style elements, such as brevity, redundancy, or altered intent, to eliminate wrong answer choices.
Manhattan GMAT Guide 8 Sentence correction covers key grammatical principles in depth teaches effective gmat problem solving strategies includes practice problems with detailed explanations updated for the official guide for GMAT.
• Network errors are in the form of corrupted data or lost data.
• Network errors occur naturally on all networks due to electrical noise and distortion
and must be detected and corrected by either hardware or software.
• Bit Error Rates (BERs) are calculated as the number of bits in error divided by the
number of bits transmitted. A BER of 1 in 100,000 might be shown as 1:105
as a BER of 10-5
• Errors often occur in bursts where many bits in a sequence will be in...
Let us first discuss some issues related, directly ,indirectly, to error detection and correction.
Types of ErrorsRedundancyDetection Versus CorrectionForward Error Correction Versus RetransmissionCoding
Networks must be able to transfer data from
one device to another with complete accuracy.
Data can be corrupted during transmission.
For reliable communication, errors must be
detected and corrected.Single bit errors are the least likely type of
errors in serial data transmission because
the noise must have a very short duration
which is very rare. However this kind of
errors can happen in parallel transmission.
The study is to observe teachers‟ ways dealing with spoken errors and compare whether their used ways correspond with theory of error correction techniques in methodology or not and suggest some implications and techniques for error correction.
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Autotune is a pretty popular audio software, which corrects the pitch of a singer’s vocals in real time. Melodyne has gone even further now. They found a way to separate the harmonics of each note in a chord, allowing individual notes to be manipulated at will. Really impressive stuff even if you don’t know anything about audio.
Spelling correction for keyword-search queries is challenging in restricted domains such as personal email (or desktop) search, due to the scarcity of query logs, and due to the specialized nature of the domain. For that task, this paper presents an algorithm that is based on statistics from the corpus data (rather than the query log). This algorithm, which employs a simple graph-based approach, can incorporate different types of data sources with different levels of reliability (e.g., email subject vs.
We present a novel approach to grammatical error correction based on Alternating Structure Optimization. As part of our work, we introduce the NUS Corpus of Learner English (NUCLE), a fully annotated one million words corpus of learner English available for research purposes. We conduct an extensive evaluation for article and preposition errors using various feature sets. Our experiments show that our approach outperforms two baselines trained on non-learner text and learner text, respectively. ...
Data can be corrupted during transmission. For reliable
communication, error must be detected and corrected
are implemented either at the data link layer or the
transport layer of the OSI model.Error detection uses the concept of redundancy, which
means adding extra bits for detecting errors at the
We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a meta-learning with several base classifiers trained on different corpora. This research focuses on a grammatical error correction task for article errors.
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. ...
Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks. However, these tasks have mostly involved very small label sets. When deployed on tasks with larger label sets, the requirements for computational resources mean that training becomes intractable. This paper describes a method for training CRFs on such tasks, using error correcting output codes (ECOC). A number of CRFs are independently trained on the separate binary labelling tasks of distinguishing between a subset of the labels and its complement. ...
This paper describes a spelling correction system that functions as part of an intelligent tutor that carries on a natural language dialogue with its users. The process that searches the lexicon is adaptive as is the system filter, to speed up the process. The basis of our approach is the interaction between the parser and the spelling corrector. Alternative correction targets are fed back to the parser, which does a series of syntactic and semantic checks, based on the dialogue context, the sentence context, and the phrase context. phrases that are used in the correction process. ...
It is important to correct the errors in the results of speech recognition to increase the performance of a speech translation system. This paper proposes a method for correcting errors using the statistical features of character co-occurrence, and evaluates the method. The proposed method comprises two successive correcting processes. The first process uses pairs of strings: the first string is an erroneous substring of the utterance predicted by speech recognition, the second string is the corresponding section of the actual utterance.
We present a novel OCR error correction method for languages without word delimiters that have a large character set, such as Japanese and Chinese. It consists of a statistical OCR model, an approximate word matching method using character shape similarity, and a word segmentation algorithm using a statistical language model. By using a statistical OCR model and character shape similarity, the proposed error corrector outperforms the previously published method. When the baseline character recognition accuracy is 90%, it achieves 97.4% character recognition accuracy. ...
Statistical methods require very large corpus with high quality. But building large and faultless annotated corpus is a very difficult job. This paper proposes an efficient m e t h o d to construct part-of-speech tagged corpus. A rulebased error correction m e t h o d is proposed to find and correct errors semi-automatically by user-defined rules. We also make use of user's correction log to reflect feedback. Experiments were carried out to show the efficiency of error correction process of this workbench. The result shows that about 63.2 % of tagging errors can be corrected. ...
Cointegration and error correction
Professor Roy Batchelor
City University Business School, London
& ESCP, Paris
r On the City University system, EVIEWS 3.1 is in
Start/ Programs/ Departmental Software/CUBS
r Analysing stationarity in a single variable using VIEW
r Analysing cointegration among a group of variables
r Estimating an ECM model
r Estimating a VAR-ECM model