1. Thermometer: Temperature. 2. Anemometer: Wind vane 3. Odometer: Speed 4. Scale: Length 5. Balance: Mass 6. Sphygmomanometer: Blood Pressure 7. Rain Gauge: Rain 8. Hygrometer: Humidity 9. Ammeter: Current 10. Screw Gauge: Thickness 11. Seismograph: Earthquakes 12. Taseometer: Strains. Let us see some practice questions now.
In this type of questions, two words are given. These words are related to each other in some way. Another word is also given. The candidate is required to find out the relationship between the first two words and choose the word from the given alternatives, which bears the same relationship to the third word, as the first two bear.
Free Download 501 Word Analogy Questions
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Directions : In each of the following questions, there is a certain relationship between two given words on one side of:: and one word is given on another side of:: while another word is to be found from the given alternatives having the same relationship with this word as the words of the given pair bear. Choose the correct alternative.
Directions: Each of the following questions consists of two words that have a certain relationship to each other, followed by four lettered pairs of words. Select that lettered pair which has the same relationship as the original pair of words.
Regular polysemes are sets of ambiguous words that all share the same relationship between their meanings, such as CHICKEN and LOBSTER both referring to an animal or its meat. To probe how a distributional semantic model, here exemplified by bidirectional encoder representations from transformers (BERT), represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to "is the mapping between CHICKEN (as an animal) and CHICKEN (as a meat) similar to that which maps between LOBSTER (as an animal) to LOBSTER (as a meat)?" We did so using the LRcos model, which combines a logistic regression classifier of different categories (e.g., animal vs. meat) with a measure of cosine similarity. We found that (a) the model was sensitive to the shared structure within a given regular relationship; (b) the shared structure varies across different regular relationships (e.g., animal/meat vs. location/organization), potentially reflective of a "regularity continuum;" (c) some high-order latent structure is shared across different regular relationships, suggestive of a similar latent structure across different types of relationships; and (d) there is a lack of evidence for the aforementioned effects being explained by meaning overlap. Lastly, we found that both components of the LRcos model made important contributions to accurate responding and that a variation of this method could yield an accuracy boost of 10% in answering sense analogy questions. These findings enrich previous theoretical work on regular polysemy with a computationally explicit theory and methods, and provide evidence for an important organizational principle for the mental lexicon and the broader conceptual knowledge system.
Directions: The analogies below are word problems that consist of two word pairs. Look at the first pair and decide how the two words relate to each other. Then select one of the words below so the second pair of words has the same relationship.
It might not be the best indicator of how well word-vectors will work for your own project-specific goals. (That is, a model which does better on word-analogies might be worse for whatever other info-retrieval, or classification, or other goal you're really pursuing.) So if at all possible, create an automated evaluation that's tuned to your own needs.
Note that the absolute analogy scores can also be quite sensitive to how you trim the vocabulary before training, or how you treat analogy-questions with out-of-vocabulary words, or whether you trim results at the end to just higher-frequency words. Certain choices for each of these may boost the supposed "correctness" of the simple analogy questions, but not improve the overall model for more realistic applications.
So there's no absolute accuracy rate on these simplistic questions that should be the target. Only relative rates are somewhat indicative - helping to show when more data, or tweaked training parameters, seem to improve the vectors. But even vectors with small apparent accuracies on generic analogies might be useful elsewhere.
All that said, you can review a demo notebook like the gensim "Comparison of FastText and Word2Vec" to see what sorts of accuracies on the Google word2vec.c `questions-words.txt' analogy set (40-60%) are achieved under some simple defaults and relatively small training sets (100MB-1GB).
In 2005, major changes made to the question types in the reading and math sections of the SAT. Two types of comparison questions - quantitative comparison questions from math, and analogies from reading - were booted from the SAT.
Quatitative comparison questions used to be a considerable part of the math section (25 percent of questions). Instead of asking you to solve for a value, these questions asked you to determine which of two quantities was larger.
The SAT was hoping to disentangle itself from its long-standing reputation as a "tricky" standardized test. Quantitative reasoning questions came across more as riddles than math questions because they don't ask you to find a numerical solution or use a well-outlined mathematical skill set.
New algebra II-related topics were added to the math section to replace quantitative comparison questions. These included: exponential growth, manipulation of fractional and negative exponents, functional notation, absolute value, equations of lines, and data interpretation.
Quantitative comparison questions were replaced with questions that specifically targeted areas of math that students were familiar with from algebra and geometry classes and could be used to demonstrate a strong understanding of core math principles.
Not too much has changed from the previous version, but a few more math topics have been added. These include: basic trigonometry, more data interpretation, questions that invoke real life scenarios, and more in-depth questions involving algebra and solving equations.
This question asks you to use your mathematical skills to analyze a real data set. The ability to answer quesitons like this more accurately reflects career and college preparedness than performance on quantitative comparison questions, which were totally disconnected from real life scenarios.
These were the original stereotypical SAT questions. You were given a pair of words and asked to choose from five other pairs of words to find the relationship that most closely resembled that of the first pair.
Sentence completion questions were the closest analogue (sorry, the wordplay is getting out of hand) to analogy questions on the 2005-2015 version of the SAT. Though they were also on the SAT prior to 2005, after the removal of analogy questions they became the only real vocab-centric questions in the Critical Reading section.
These questions called for skills in identifying vocabulary in the context of a sentence. You were given a sentence with one or two blanks and asked to choose the best vocabulary word to fill in the blank(s).
On the current SAT, sentence completion questions are replaced with vocabulary in context questions. These questions are similar to reading questions found on the ACT. You are asked to pick out the closest synonym for a vocabulary word that appears in a passage.
The goal of these questions is to encourage students to understand the nuances in meaning of more common words rather than overwhelming them with a bunch of archaic vocabulary. The best way to study for these types of questions is to focus on honing your passage reading skills. The ability to read and interpret the meanings of passages correctly is key on the current version of the SAT because all questions in the reading section are passage-based.
Essentially, the SAT removed these old types of questions to reduce criticisms about inequality. The test continuously finds itself under fire for the direct proportionality of family income to test scores, and it has made numerous efforts over the years to reinvent itself to combat this problem. In a decade, we may see yet another re-imagining of the SAT to tackle these issues in a different way.
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If someone has been humiliated, they have been greatly embarrassed. If someone is terrified, they are extremely frightened. The answer is not choice b because an agitated person is not necessarily frightened. Choices c and d are incorrect because neither word expresses a state of being frightened.
A group of lions is called a pride. A group of fish swim in a shoal. Teacher (choice a) and student (choice b) refer to another meaning of the word school. The answer is not (choice c) because self-respect has no obvious relationship to this particular meaning of school.
The answer to this verbal analogy (from here) does not seem the best to me. I'd have chosen D. My reasoning is: When a pencil gets worn down (blunt), it's "out of lead," it won't write anymore, so it can't serve its purpose anymore, till it gets sharpened. Similarly, when a well runs out of water, it no longer serves its purpose, so it has to be filled.
As CrossRoads says, I presume the thinking behind the answer is that "sharpen" is something you do to a "pencil", so the correct answer would be words that share that relationship. "Knife/cut" doesn't work because you do not normally cut a knife, rather, you use a knife to cut. The "does the action" versus "receives the action" is backwards. Similarly with B. In E, if you think of "saw" as a noun, then it doesn't work because we have 2 nouns rather than a noun and a verb. If you think of "saw" as a verb, it doesn't work because the order doesn't match -- noun/verb versus verb/noun -- and because you can't saw things with an ax, that's not what an ax does. So either way, E doesn't work. 0852c4b9a8
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