Basic Reports

Size & Scope

First, the simple things. Your study carrel was created through the submission of a [SINGLE URL|FILE OF URLS|FILE FROM YOUR COMPUTER|ZIP FILE]. This ultimately resulted in a collection of 12 item(s). The original versions of these items have been saved in a cache, and each of them have been transformed & saved as a set of plain text files. All of the following analysis has been done against these plain text files.

Your study carrel is 62,470 words long. [0] Each item in your study carrel is, on average, 5,205 words long. [1] If you dig deeper, then you might want to save yourself some time by reading a shorter item. On the other hand, if your desire is for more detail, then you might consider reading a longer item. The following illustrate the overall size of your study carrel.

Readability

On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, your documents have an average readability score of 55. [2] Consequently, if you want to read something more simplistic, then consider a document with a higher score. If you want something more specialized, then consider something with a lower score. The following illustrate the overall readability of your study carrel.

Word Frequencies

By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of your carrel's "aboutness". Excluding "stop words", some of the more frequent words include: [3]

data, learning, machine, https, ai, library, will, new, one, research, use, libraries, information, also, process, digital, may, using, model, org, work, intelligence, footnoteref, example, many, systems, com, see, time, project, like, images, human, different, used, well, people, www, algorithms, training, system, make, place, al, tools, results, et, text, collections, deep

Using the three most frequent words, the three files containing all of those words the most are altman, wiegand, and morgan.

The most frequent two-word phrases (bigrams) include:

machine learning, et al, artificial intelligence, deep learning, daniel johnson, new york, see https, ai systems, training data, place name, place names, adversarial networks, generative adversarial, ai system, chicago place, data sets, topic modeling, digital humanities, cultural heritage, com login, learning techniques, new data, direct true, full text, facial recognition, neural networks, learning algorithms, supervised learning, disciplinary research, true db, url https, site ehost, plain text, learning applications, archival materials, generative machine, automated information, markov chain, military robots, neural network, data structure, open access, information environment, natural language, computer vision, face recognition, mark dehmlow, chicago reading, code lib, ethical sensitivity

And the three file that use all of the three most frequent phrases are harper wiegand, and prudhomme.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:

Keywords

Sets of keywords -- statistically significant words -- can be enumerated by comparing the relative frequency of words with the number of times the words appear in an entire corpus. Some of the most statistically significant keywords in your study carrel include:

machine, learning, datum, system, library, image, university, tönnies, research, process, problem, pmss, place, new, networks, moral, microsoft, material, markov, marc, kentucky, information, human, generative, gan, example, eastern, disciplinary, chinese, chicago, balke, archive, algorithm, adversarial

And now word clouds really begin to shine:

Topic Modeling

Topic modeling is another popular approach to connoting the aboutness of a corpus. If your study carrel could be summed up in a single word, then that word might be learning, and altman is most about that word.

If your study carrel could be summed up in three words ("topics") then those words and their significantly associated titles include:

1. learning - kim
2. learning - harper
3. data - altman

If your study carrel could be summed up in five topics, and each topic were each denoted with three words, then those topics and their most significantly associated files would be:

1. learning, machine, data - cohen-nakazawa
2. data, learning, machine - altman
3. ai, machine, learning - kim
4. library, learning, machine - wiegand
5. chicago, data, place - lucic-shanahan

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

Noun & Verbs

Through an analysis of your study carrel's parts-of-speech, you are able to answer question beyonds aboutness. For example, a list of the most frequent nouns helps you answer what questions; "What is discussed in this collection?":

data, learning, machine, research, libraries, library, information, process, model, example, time, images, use, systems, project, people, system, results, training, text, place, tools, work, way, algorithms, collections, researchers, problem, set, algorithm, dataset, materials, image, knowledge, problems, examples, number, applications, level, services, articles, recognition, network, gans, decision, classification, archives, techniques, input, file

An enumeration of the verbs helps you learn what actions take place in a text or what the things in the text do. Very frequently, the most common lemmatized verbs are "be", "have", and "do"; the more interesting verbs usually occur further down the list of frequencies:

is, be, are, have, was, were, do, has, using, see, learning, used, make, use, given, based, been, help, create, had, does, find, learn, generated, need, did, work, trained, provide, build, generate, being, making, working, know, including, identify, get, called, become, known, include, ’s, produce, found, add, want, understand, think, made

Proper Nouns

An extraction of proper nouns helps you determine the names of people and places in your study carrel.

ai, learning, machine, al, ml, chicago, library, intelligence, artificial, et, university, new, digital, google, data, daniel, johnson, information, ieee, research, york, marc, gan, adversarial, science, n.d, microsoft, generative, review, networks, press, journal, technology, may, markov, international, conference, reading, kentucky, ., march, january, computer, december, congress, Řehůřek, proceedings, msc, mark, libraries

An analysis of personal pronouns enables you to answer at least two questions: 1) "What, if any, is the overall gender of my study carrel?", and 2) "To what degree are the texts in my study carrel self-centered versus inclusive?"

we, it, you, their, our, they, your, i, its, them, us, my, one, itself, themselves, her, me, his, he, yourself, she, ourselves, ours, ’s, ml+history, https://www.kaggle.com/c/deepfake-detection-challenge, https://devblogs.nvidia.com/explaining-deep-learning-self-driving-car/., him, alphago

Below are words cloud of your study carrel's proper & personal pronouns.

Adjectives & Verbs

Learning about a corpus's adjectives and adverbs helps you answer how questions: "How are things described and how are things done?" An analysis of adjectives and adverbs also points to a corpus's overall sentiment. "In general, is my study carrel positive or negative?"

such, new, other, many, different, more, digital, moral, human, deep, possible, large, good, local, important, -, ethical, able, social, specific, same, available, historical, real, own, neural, intelligent, high, full, better, common, library, final, traditional, public, first, computational, multiple, likely, cultural, artificial, unique, similar, particular, technical, simple, open, generative, disciplinary, second

not, also, more, then, well, only, as, even, very, out, so, together, just, now, however, most, instead, here, up, often, still, n’t, already, first, rather, especially, perhaps, much, highly, really, far, back, always, too, morally, previously, sometimes, on, increasingly, down, fully, finally, automatically, yet, similarly, never, generally, enough, easily, better

Next steps

Here is a prioritized list of next steps to thus get more out of your study carrel:

1. browse, sort, and search the content of the menu items named Ngrams, POS, Grammars, and Others found at the top of this page to acquire more detail about your carrel
2. read standard-output.txt, as it will both summarize and elaborate upon this narrative report
3. read MANIFEST.htm to both inventory your study carrel as well as learn how to answer more specific questions of it