johnson-machine-2021

Introduction

This is a Distant Reader "study carrel", a set of structured data intended to help the student, researcher, or scholar use & understand a corpus.

This study carrel was created on 2021-02-23 by Eric Morgan <emorgan@nd.edu>. The carrel was created using the Distant Reader zip2carrel process, and the input was a Zip file locally cached with the name input-file.zip. Documents in the Zip file have been saved in a cache, and each of them have been transformed & saved as a set of plain text files. All of the analysis -- "reading" -- has been done against these plain text files. For example, a short narrative report has been created. This Web page is a more verbose version of that report.

All study carrels are self-contained -- no Internet connection is necessary to use them. Download this carrel for offline reading. The carrel is made up of many subdirectories and data files. The manifest describes each one in greater detail.

Size

There are 15 item(s) in this carrel, and this carrel is 75,921 words long. Each item in your study carrel is, on average, 5,061 words long. 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 charts illustrate the overall size of the carrel.

Readability

On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, the documents have an average readability score of 56. 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 charts illustrate the overall readability of the carrel.

Word Frequencies

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

learning, machine, data, https, research, iitb, library, org, ai, new, libraries, will, one, com, use, www, digital, also, ff, information, doi, process, work, using, see, may, disciplinary, model, intelligence, cross, many, example, time, text, different, like, systems, project, used, human, word, computational, algorithms, tools, knowledge, images, artificial, make, al, based

Using the three most frequent words, the three files containing all of those words the most are Cultures of Innovation: Machine Learning as a Library Service, Artificial Intelligence in the Humanities: Wolf in Disguise, or Digital Revolution?, and AI and Its Moral Concerns.

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

machine learning, disciplinary research, ff qbxq, et al, artificial intelligence, deep learning, new york, html https, digital humanities, place names, ai systems, training data, word embeddings, topic modeling, pdf https, place name, chicago place, natural language, adversarial networks, data sets, generative adversarial, facial recognition, learning techniques, ai system, new data, full text, code lib, neural network, language processing, org abs, word embedding, text analysis, open access, learning applications, learning algorithms, neural networks, cultural heritage, computational linguistics, com https, state university, markov chain, automated information, computer vision, archival materials, supervised learning, plain text, military robots, face recognition, notre dame, data science

And the three file that use all of the three most frequent phrases are Cultures of Innovation: Machine Learning as a Library Service Taking a Leap Forward: Machine Learning for New Limits, and AI and Its Moral Concerns.

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 the carrel include:

learning, machine, research, datum, system, libraries, word, university, scholar, reading, process, pmss, place, notre, nakazawa, msc, moral, model, microsoft, material, markov, literary, library, kentucky, information, image, ieee, human, gan, disciplinary, computational, cohen, chinese, chicago, balke, 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 the study carrel could be summed up in a single word, then that word might be learning, and Taking a Leap Forward: Machine Learning for New Limits is most about that word.

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

1. learning - AI and Its Moral Concerns
2. library - Cultures of Innovation: Machine Learning as a Library Service
3. word - Humanities and Social Science Reading through Machine Learning

If the 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 - AI and Its Moral Concerns
2. 2019, https, learning - Generative Machine Learning
3. learning, machine, data - Machine Learning + Data Creation in a Community Partnership for Archival Research
4. research, ml, disciplinary - Cross-Disciplinary ML Research is like Happy Marriages: Five Strengths and Two Examples
5. chicago, place, book - Towards a Chicago place name dataset: From back-of-the-book index to a labeled dataset

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, machine, learning, research, �, library, information, process, libraries, model, example, text, time, images, work, word, systems, results, project, tools, use, knowledge, training, way, place, people, algorithms, system, researchers, set, models, words, collections, materials, methods, problem, algorithm, language, dataset, scholars, image, applications, ways, problems, humanities, analysis, questions, examples, network, history

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

Proper Nouns

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

�, learning, machine, ai, libraries, disciplinary, cross, -, al, researchǔchapter, ml, library, chicago, university, ff, digital, et, intelligence, artificial, new, data, google, research, m, science, information, ieee, york, press, n.d, review, gan, journal, marc, microsoft, ing, generative, conference, adversarial, technology, may, international, figure, humanities, computer, march, ., markov, january, congress

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, they, our, i, its, your, them, us, one, my, itself, her, themselves, he, his, me, she, ourselves, yourself, ours, ibqm, `ikr?qh2f, #f[mb+f/`, ’s, ၯஒ,ࡢᄝࡢმ, zbmath,19, qbxq`;fryxryydfbrr3jr@yrn@ynj, mfvqm`2@, ibqmbx?ikh?/`4tm#v2, hxpj3brxrynd9, hvib+bfk, https://www.aclweb.org/anthology/p14-5010/, https://radimrehurek.com/gensim/, http://www.minedminds.org/, http://read.gov/resources/, him, hh@/b;bi, hbx2, hbbkf, fr?v@kyr8@r, ff/?h, de-, byry, bhf1pb/2m+2@amkk, b;m, `b, +ibfi

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?"

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

not, also, more, only, then, well, as, even, very, out, now, so, however, most, often, n’t, up, just, together, still, already, instead, here, rather, first, highly, always, especially, perhaps, much, far, too, really, morally, fully, better, back, similarly, increasingly, down, yet, previously, on, generally, easily, thus, sometimes, simply, long, likely

Next steps

There is much more to a study carrel than the things outlined above. Use this page's menubar to navigate and explore in more detail. There you will find additional features & functions including: ngrams, parts-of-speech, grammars, named entities, topic modeling, a simple search interface, etc.

Again, study carrels are self-contained. Download this carrel for offline viewing and use.

Thank you for using the Distant Reader.