Word Lists Download __EXCLUSIVE_

Our word lists are designed to help English language learners at any level focus on the most important words to learn in their area of study. Based on our extensive corpora (= collections of written and spoken texts) and aligned to the Common European Framework of Reference for Languages (CEFR), the word lists have been carefully researched and developed together with vocabulary experts, so that you know you can rely on them in your learning or teaching.

So by pairing the "R" sound with the "O" sound like in the word "Rope", this makes the word extra difficult for a child who has a problem saying the "R" sound because the "O" that follows the "R" will naturally make them want to round there lips.

Word Lists Download

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I'm am looking for specific suggestions or references to an algorithm and/or data structures for encoding a list of words into what would effectively would turn out to be a spell checking dictionary. The objectives of this scheme would result in a very high compression ratio of the raw word list into the encoded form. The only output requirement I have on the encoded dictionary is that any proposed target word can be tested for existence against the original word list in a relatively efficient manner. For example, the application might want to check 10,000 words against a 100,000 word dictionary. It is not a requirement for the encoded dictionary form to be able to be [easily] converted back into the original word list form - a binary yes/no result is all that is needed for each word tested against the resulting dictionary.

I am assuming the encoding scheme, to improve compression ratio, would take advantage of known structures in a given language such as singular and plural forms, possessive forms, contractions, etc. I am specifically interested in encoding mainly English words, but to be clear, the scheme must be able to encode any and all ASCII text "words".

For pure compression, the Maximum Compression site offers some results for a 4 MB english wordlist, best program compresses this to around 400 KB. Some other compression resources for text/word compression are the Hutter Prize page and the Large Text Compression Benchmark.

Vocabulary Wheel - 8 Words: This 2-page print-out makes a wheel about vocabulary words; the student writes 8 new words together with definitions. It consists of a base page together with a wheel that spins around. After putting the wheel together, the student follows the instructions on the front wheel and fills out the 8 sections of the wheel with words and definitions. When you spin the wheel, the words and definitions appear, one at a time.

Vocabulary Wheel : This 2-page print-out makes a wheel about vocabulary words; the student writes 12 new words together with definitions. It consists of a base page together with a wheel that spins around. After putting the wheel together, the student follows the instructions on the front wheel and fills out the 12 sections of the wheel with words and definitions. When you spin the wheel, the words and definitions appear, one at a time.

This experiment tested hypotheses linking the right cerebral regulation of hostility and affective verbal learning. First, patterns of recall for positive, negative, and neutral affective list learning among high- and low-hostile individuals were examined. It was expected that low-hostiles would recall more items from the positive list and that high-hostiles would recall more words from the negative affective list. Also, independent of groups, it was expected that there would be a primacy effect for negative words and a recency effect for positive words. Exploratory analyses examined the relation between hostility and primacy and recency effects on the positive and negative word lists. High- and low-hostile participants (n = 65) completed the positive list learning task, the negative list learning task, or the neutral list learning task. Data analyses revealed no significant difference between the high- and low-hostile groups on the different affective lists. However, results of the present investigation reliably demonstrated the predicted primacy and recency effects. There was a primacy effect for the negative affective list and a recency effect for the positive affective list. These findings are consistent with previous research investigating the acquisition pattern of affective verbal learning.

Randomly-generated passphrases offer a major security upgrade over user-chosen passwords. Estimating the difficulty of guessing or cracking a human-chosen password is very difficult. It was the primary topic of my own PhD thesis and remains an active area of research. (One of many difficulties when people choose passwords themselves is that people aren't very good at making random, unpredictable choices.)

Measuring the security of a randomly-generated passphrase is easy. The most common approach to randomly-generated passphrases (immortalized by XKCD) is to simply choose several words from a list of words, at random. The more words you choose, or the longer the list, the harder it is to crack. Looking at it mathematically, for k words chosen from a list of length n, there are nk possible passphrases of this type. It will take an adversary about nk/2 guesses on average to crack this passphrase. This leaves a big question, though: where do we get a list of words suitable for passphrases, and how do we choose the length of that list?

Several word lists have been published for different purposes; thus far, there has been little scientific evaluation of their usability. The most popular is Arnold Reinhold's Diceware list, first published in 1995. This list contains 7,776 words, equal to the number of possible ordered rolls of five six-sided dice (7776=65), making it suitable for using standard dice as a source of randomness. While the Diceware list has been used for over twenty years, we believe there are several avenues to improve the usability and are introducing three new lists for use with a set of five dice (as part of its Summer Security Reboot Campaign, EFF is providing a dice set to donors).

The Diceware list can provide strong security, but offers some challenges to usability. In particular, some of the words on the list can be hard to memorize, hard to spell, or easy to confuse with another word.

Note that several of these problems are exacerbated for users with a soft keyboard or other typing systems that relies on word recognition. Using only valid dictionary words makes this setup much easier.

Our first new list matches the original Diceware list in size (7,776 words (65)), offering equivalent security for each word you choose. However, we have fixed the above problems, resulting in a list that is hopefully easy to type and remember.

We based our list off of data collected by Ghent University's Center for Reading Research. The Ghent team has long studied word recognition; you can participate yourself in their online quiz to measure your English vocabulary. This list gives us a good idea of which words are most likely to be familiar to English speakers and eliminates most of the unusual words in the original Diceware list. This data also includes "concreteness" ratings for each words, from very concrete words (such as screwdriver) to very abstract words (such as love).

We took all words between 3 and 9 characters from the list, prioritizing the most recognized words and then the most concrete words. We manually checked and attempted to remove as many profane, insulting, sensitive, or emotionally-charged words as possible, and also filtered based on several public lists of vulgar English words (for example this one published by Luis von Ahn). We further removed words which are difficult to spell as well as homophones (which might be confused during recall). We also ensured that no word is an exact prefix of any other word.

The result is our own list of 7,776 words [.txt] suitable for use in dice-generated passphrases. The words in our list are longer (7.0 characters) on average, than Reinhold's Diceware list (4.3 characters). This is a result of banning words under 3 characters as well as prioritizing familiar words over short but unusual words.

Note that the security of a passphrase generated using either list is identical; the differences are in usability, including memorability, not in security. For most uses, we recommend a generating a six-word passphrase with this list, for a strength of 77 bits of entropy. ("Bits of entropy" is a common measure for the strength of a password or passphrase. Adding one bit of entropy doubles the number of guesses required, which makes it twice as difficult to brute force.) Each additional word will strengthen the passphrase by about 12.9 bits.

We are also introducing new lists containing only 1,296 words (64), suitable for use with four six-sided dice. By reducing the number of words in the list, we were able to use words with a maximum of five characters. This can lead to more efficient typing for the same security if it requires fewer characters to enter N short words than N-1 long words.

The first short list [.txt] is designed to include the 1,296 most memorable and distinct words. Our hope is that this approach might offer a usability improvement for longer passphrases. Further study is need to determine conclusively which list will yield passphrases that are easier to remember.

Different lists might be preferable in different situations, and that's perfectly fine. For example, you might consider using one of the short lists when you are prioritizing ease of remembering, or when you know that the highest level of passphrase strength is not necessary. This might cover a website login that offers additional protections, like two-factor authentication, and that rate-limits guesses to protect against brute force.

If you are typing the passphrase frequently (as opposed to using a passphrase database), you might prioritize reducing the length of the words. Our long list has an average length of 7.0 characters per word, and 12.9 bits of entropy per word, yielding an efficiency of 1.8 bits of entropy per character. Our short list has an average length of 4.5 characters per word, and 10.3 bits of entropy per word, yielding 2.3 bits of entropy per character. Our typo-tolerant list is much less efficient at only 1.4 bits of entropy per character. However, using a future autocomplete software feature, only three characters would need to be typed per word, in which case this would be the most efficient list to use at 3.1 bits of entropy per character typed. 17dc91bb1f