This annoys me more than it should. With English being a child of German obviously this kind of thing turns up all the time where the vocab is the same (or very similar, maybe a slight spelling difference) word, same meaning.
Removing the ends of words like that is called stemming and there are a couple of packages in R that will do that for you, if you'd like. One is the hunspell package from rOpenSci, and another option is the SnowballC package which implements Porter algorithm stemming. You would implement that like so:
Download One Word Substitution Pdf For Ssc
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One word substitution is the use of one word in place of a wordy phrase in order to make the sentence structure clearer. The meaning, with the replacement of the phrase remains identical while the sentence becomes shorter.
Apart from the above-given questions, one-word substitute can be asked in the form of spotting the error, word replacements as well as in reading comprehension passages. Candidates can go through the exercise on One Word Substitution Questions and Answers for practice and revision.
One Word Substitution: One Word Substitution is an essential topic of vocabulary. As the name suggests, questions based on this concept ask you to replace a given sentence with an appropriate word. One word substitution is an important concept in the English language as it makes communication much more concise, lucid, and easy. One-Word Substitution refers to those types of questions where a sentence or a phrase is simply replaced by a word that describes the whole sentence. One-word substitution makes the sentence structure more precise. One-word substitution questions frequently occur in many national-level exams such as SSC CGL, SSC CSHL, UPSC, Defence Exams, and other competitive exams. The best way to learn the concept of One Word Substitution is to learn by framing sentences or visualising the words through an interesting story.
When I need to redo something I've already done, usually I just hitthe up-arrow key if the shell command was recent (like one or twocommands ago), or type Control-R and followed by some keyword on thecommand line. Sure, this approach often requires multiple Control-Rkeystrokes until I would get to the right command, but this featurehas served me well for thousands of years.
Note that a % word designator works only when used in one of !%, !:% or !?str?:%, and only when used after a !? expansion (possibly in an earlier command). Anything else results in an error, although the error may not be the most obvious one.
People usually look at things before they name them. For instance, before they say "a hammer," they look at the hammer for a second. But what about when they see a hammer and unintentionally call it "an axe"? Zenzi M. Griffin, Georgia Institute of Technology, assumed people made the mistake when they didn't look at the hammer long enough, which could reflect rushed word preparation, forgetting to check the name they had mentally prepared against the object, or paying too much attention to other objects.
But Griffin discovered that people who say "axe" when they mean "hammer" look at the hammer just as long as they do when they say "hammer." However, they look at the hammer longer after they call it "an axe," apparently as they prepare to correct their mistake. In her study, "The Eyes Are Right When the Mouth Is Wrong," Griffin concluded that, as with a gesture, a person's gaze may accurately reflect what he intends, even if his words do not. The study will be published in the December issue of Psychological Science, a journal of the American Psychological Society.
These results have three implications: 1) word-substitution mistakes are more indicative of problems in planning speech than problems in thought or attention; 2) speech errors are not caused by a person rushing through word preparation or omitting a sub-process; and 3) looking at an object is not a guarantee that the person will say it correctly.
The following is a list of commonly deployed phrasal verbs that find one use or another in academic texts. These (and others) can be acceptably used in academic texts. Along with these examples, however, are a number of one-word substitutions to illustrate that in each case the phrasal verb can be easily replaced.Table of contentsSeparableInseparableOther interesting articles
This flexibility means that although these substitutions work for the examples given, and although the examples are common uses of phrasal verbs, a suggested replacement will not cover every possible use of its phrasal verb.
What is a One-Word Substitution? Word Substitution simply means using a specific word to replace a wordy phrase or sentence and making it shorter, more concise and clearer to understand. This way, the word becomes identical to the sentence thus providing the same meaning as the wordy sentence. The best way to master this concept is to learn their meanings by putting them in a particular sentence or visualising them through an interesting story.
One-word substitution questions frequently occur in many competitive exams such as SBI PO, UPSC, CAPF, CDS, RRB, SSC etc. Read this blog to find 100+ one-word substitutions with examples as well as practice questions and quizzes for competitive exams.
Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. Separately, certified word substitution robustness methods have been developed to decrease the impact of spurious features and synonym substitutions on model predictions. While their end goals are different, they both aim to encourage models to make the same prediction for certain changes in the input. In this paper, we investigate the utility of certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. We observe that certified robustness methods improve fairness, and using both robustness and bias mitigation methods in training results in an improvement in both fronts
The often observed unavailability of large amounts of training data typically required by deep learning models to perform well in the context of NLP tasks has given rise to the exploration of data augmentation techniques. Originally, such techniques mainly focused on rule-based methods (e.g. random insertion/deletion of words) or synonym replacement with the help of lexicons. More recently, model-based techniques which involve the use of non-contextual (e.g. Word2Vec, GloVe) or contextual (e.g. BERT) embeddings seem to be gaining ground as a more effective way of word replacement. For BERT, in particular, which has been employed successfully in various NLP tasks, data augmentation is typically performed by applying a masking approach where an arbitrary number of word positions is selected to replace words with others of the same meaning. Considering that the words selected for substitution are bound to affect the final outcome, this work examines different ways of selecting the words to be replaced by emphasizing different parts of a sentence, namely specific parts of speech or words that carry more sentiment information. Our goal is to study the effect of selecting the words to be substituted during data augmentation on the final performance of a classification model. Evaluation experiments performed for binary classification tasks on two benchmark datasets indicate improvements in the effectiveness against state-of-the-art baselines.
One Word Substitution is a key component of vocabulary. It requires the replacement of a sentence with an appropriate single word that captures its full meaning. This method of communication is much more succinct and easy to understand, making it widely used in numerous competitive exams. To learn this concept effectively, go through the following article where some examples and sample questions are provided for practice.
These types of errors are called paraphasias, and they are common in many types of aphasia. A paraphasia is the production of an unintended sound within a word, or of a whole word or phrase. It can be the substitution of one sound for another sound, using the wrong word, or transposing sounds within a long word.
Depending on the type and severity of aphasia, people with aphasia might or might not be aware of paraphasias when they use them. Even when someone is aware they have said a word incorrectly, it can be difficult to correct. Speech pathologists can help with strategies and cues to work on paraphasias.
The community did not discuss the name before the Final Review, because at the time of making this proposal, and throughout Definition, I knew nothing about a "final review" at the end of the Definition stage, since this was never told to us at the time of proposing, and the word "review" doesn't even appear anywhere in the A51 Help Center or FAQ, and I had not read every single Discussion Zone post (I had the above two things to worry about, and they were not easy problems to solve!). The most I could base things off was the most recent case (O.R.), and in that case the name did change from "Operations Research and Analytics" to "Operations Research", well after Definition phase, and after the Meta Discussion slightly after ~45% commitment (I understand the discussion might have played no role in the final decision, but the name change happening after the discussion and half-way through Commitment phase suggested that we have until much longer than Definition phase before a final decision).
But MMM is quite a mouthful, and now we know it's too long for the design, and it also excludes ab initio modeling of atoms, sub-atomic matter (coupled cluster is used in sub-atomic science too, in fact it was first invented there). MMM is also quite a drastic change from what we have right now, but what if we were to just change one single word: Materials to Matter. This solves the problem of "excluding" people that identify more with molecular-modeling than materials-modeling, in addition to ab intio modelers using the same techniques for other types of matter. The length is the same, and most of the SE network won't even notice the change. e24fc04721
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