Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. This report is a terse narrative report, and when processing is complete you will be linked to a more complete narrative report. Eric Lease Morgan Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 15 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 5061 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 57 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- 5 Learning 4 machine 3 research 3 datum 2 system 2 Libraries 1 word 1 scholar 1 process 1 pmss 1 place 1 moral 1 model 1 material 1 literary 1 library 1 learning 1 image 1 human 1 computational 1 algorithm 1 University 1 Reading 1 Notre 1 Nakazawa 1 Microsoft 1 Markov 1 Machine 1 MSC 1 Kentucky 1 Information 1 IEEE 1 GAN 1 Disciplinary 1 Cohen 1 Chinese 1 Chicago 1 Balke 1 Adversarial Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 361 datum 339 machine 292 learning 235 library 184 research 166 model 155 � 149 system 148 example 144 word 138 image 136 process 124 text 124 algorithm 119 way 117 information 114 tool 113 time 109 work 109 result 108 project 104 problem 97 set 97 dataset 91 collection 88 data 82 question 81 researcher 81 method 80 use 77 place 76 value 76 material 76 knowledge 75 computer 73 application 72 training 70 people 69 name 68 decision 67 network 64 task 64 scholar 63 article 61 case 59 archive 58 language 56 service 55 topic 55 level Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 637 � 184 Learning 175 Machine 162 AI 97 Libraries 83 Disciplinary 82 Cross 81 - 75 al 73 ResearchǔChapter 62 ML 60 Library 58 Chicago 57 University 56 ff 48 Digital 47 et 46 Intelligence 46 Artificial 43 New 41 Data 37 Google 35 Research 31 Science 31 M 28 Information 28 IEEE 27 York 27 Press 26 n.d 25 Review 24 GAN 22 Journal 21 ing 21 Microsoft 21 Generative 21 Conference 21 Adversarial 20 Technology 20 MARC 20 International 19 May 19 Humanities 19 Figure 19 Computer 18 March 18 . 17 Markov 17 January 17 Congress Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 405 we 362 it 260 you 193 they 158 i 56 them 46 us 29 one 18 itself 12 themselves 12 he 8 me 5 she 4 ourselves 3 yourself 3 her 2 ours 2 `ikr?qh2f 2 #f[mb+f/` 1 ’s 1 zbmath,19 1 hxpj3brxrynd9 1 hvib+bfk 1 http://www.minedminds.org/ 1 http://read.gov/resources/ 1 him 1 hh@/b;bi 1 hbx2 1 hbbkf 1 fr?v@kyr8@r 1 de- 1 byry 1 `b 1 #bfr3y8xrkkre Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 2032 be 400 have 299 use 237 do 160 learn 156 make 99 generate 88 include 88 find 86 see 83 give 82 base 81 create 78 provide 77 work 70 train 70 know 70 build 65 need 64 help 61 become 60 identify 58 take 56 develop 49 produce 47 exist 47 call 43 allow 39 go 39 add 38 look 37 discuss 35 come 35 automate 34 write 34 try 34 represent 34 ff 33 understand 33 compare 32 want 32 read 32 get 31 show 31 share 31 require 31 lead 31 describe 31 apply 30 think Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 357 not 208 more 167 new 158 such 149 also 148 other 130 well 104 many 97 different 95 only 88 digital 83 large 81 then 73 as 67 computational 64 good 61 deep 59 very 59 most 59 even 58 moral 57 human 54 out 54 important 54 first 52 possible 51 - 49 historical 48 literary 48 able 47 social 47 now 46 so 46 local 46 however 43 specific 43 often 43 high 43 ethical 40 long 39 � 39 same 39 much 37 up 37 likely 37 just 36 together 36 similar 34 cultural 34 available Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 19 good 15 most 9 least 7 near 5 great 2 ter 2 bad 1 true 1 sparse 1 simple 1 silly 1 safe 1 rich 1 raw 1 quick 1 new 1 late 1 large 1 high 1 broad 1 big 1 `2@i` 1 Most Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 44 most 7 well 3 least Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 130 doi.org 13 towardsdatascience.com 13 github.com 12 arxiv.org 11 www.wired.com 8 www.nytimes.com 7 critinq.wordpress.com 5 www.technologyreview.com 5 www.forbes.com 5 medium.com 4 www.infodocket.com 4 www.digitalhumanities.org 4 www.clevelandart.org 4 www.blog.google 4 plato.stanford.edu 4 passamaquoddypeople.com 4 mathscinet.ams.org 4 journal.code4lib.org 3 www.weforum.org 3 www.washingtonpost.com 3 www.theguardian.com 3 www.theatlantic.com 3 www.sowetanlive.co.za 3 www.oclc.org 3 www.microsoft.com 3 www.infotoday.com 3 www.chipublib.org 3 www.campaignlive.com 3 www.bbc.com 3 www.ala.org 3 www.aclweb.org 3 www.aclu.org 3 scalar.usc.edu 3 proceedings.neurips.cc 3 papers.nips.cc 3 mallet.cs.umass.edu 3 linkedgeodata.org 3 library.stanford.edu 3 geodeepdive.org 3 dhdebates.gc.cuny.edu 3 collectionsasdata.github.io 3 cloud.google.com 3 blogs.loc.gov 3 blog-ica.org 3 acuads.com.au 2 zbmath.org 2 xpmethod.plaintext.in 2 www.yewno.com 2 www.who.int 2 www.wandb.com Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 6 http://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 4 http://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/ 4 http://www.blog.google/products/assistant/interpreter-mode-brings-real-time-translation-your-phone/ 4 http://passamaquoddypeople.com/passamaquoddy-traditional-knowledge-labels 4 http://journal.code4lib.org/articles/13671 4 http://doi.org/10.1037/0033-295X.114.2.211 3 http://www.weforum.org/whitepapers/how-to-prevent-discriminatory-outcomes-in-machine-learning 3 http://www.washingtonpost.com/technology/2020/01/06/facebook-ban-deepfakes-sources-say-new-policy-may-not-cover-controversial-pelosi-video/ 3 http://www.theguardian.com/science/alexs-adventures-in-numberland/2015/jan/08/banking-forecasts-maths-weather-prediction-stochastic-processes 3 http://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-galler 3 http://www.sowetanlive.co.za/news/south-africa/2019-06-04-meet-libby-the-new-robot-library-assistant-at-the-university-of-pretorias-hatfield-campus/ 3 http://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html 3 http://www.microsoft.com/en-us/research/project/academic/articles/microsoft-academic-increases-power-semantic-search-adding-fields-study/ 3 http://www.infotoday.com/OnlineSearcher/Articles/Features/Artificial-Intelligence-Tools-for-Information-Discovery-124721.shtml 3 http://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/ 3 http://www.chipublib.org/one-book-one-chicago/ 3 http://www.campaignlive.com/article/recognizing-exclusion-key-inclusive-design-conversation-kat-holmes/1488872 3 http://www.ala.org/tools/programming/onebook 3 http://www.aclu.org/blog/privacy-technology/pitfalls-artificial-intelligence-decisionmaking-highlighted-idaho-aclu-case 3 http://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063 3 http://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf 3 http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf 3 http://medium.com/a-wikipedia-librarian/youre-a-researcher-without-a-library-what-do-you-do-6811a30373cd 3 http://linkedgeodata.org/About 3 http://library.stanford.edu/blogs/digital-library-blog/2017/11/artificial-intelligence-and-library-future-revisited 3 http://github.com/ericleasemorgan/bringing-algorithms 3 http://github.com/ageitgey/face_recognition 3 http://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/620caf9f-08a8-485e-a496-51400296ebcd#ch19 3 http://critinq.wordpress.com/2019/03/31/computational-literary-studies-a-critical-inquiry-online-forum/ 3 http://collectionsasdata.github.io/part2whole/ 3 http://blogs.loc.gov/thesignal/2020/02/machine-learning-libraries-summit-event-summary-now-live/ 3 http://blog-ica.org/2019/10/02/machine-learning-archives-and-special-collections-a-high-level-view/ 3 http://acuads.com.au/conference/article/challenges-and-opportunities-of-cross-disciplinary-design-education-and-research/ 2 http://xpmethod.plaintext.in/torn-apart/volume/2/ 2 http://www.wired.com/story/facebooks-ai-says-field-hit-wall/ 2 http://www.wired.com/story/ai-biased-how-scientists-trying-fix/ 2 http://www.wired.com/2017/04/courts-using-ai-sentence-criminals-must-stop-now/ 2 http://www.wired.com/2016/07/artificial-intelligence-setting-internet-huge-clash-europe/ 2 http://www.wired.com/2014/10/future-of-artificial-intelligence/ 2 http://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov 2 http://www.wandb.com/articles/object-detection-with-retinanet 2 http://www.theverge.com/2018/5/8/17332070/google-assistant-makes-phone-call-demo-duplex-io-2018 2 http://www.technologyreview.com/2020/04/01/974997 2 http://www.technologyreview.com/2019/04/08/103223 2 http://www.scientificamerican.com/article/how-the-computer-beat-the-go-master/ 2 http://www.sciencedirect.com/science/article/pii/S2589750019301232 2 http://www.sas.com/en_us/insights/analytics/machine-learning.html 2 http://www.prepare-enrich.com/pe_main_site_content/pdf/research/national_survey.pdf 2 http://www.people.virginia.edu/~jmu2m/Kings.5-00/primitives.html 2 http://www.openstreetmap.org/ Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 5 machine learning algorithms 3 - making process 3 machine learning solution 3 machines do not 2 - based methods 2 - based symbolic 2 - generated content 2 data are good 2 libraries do not 2 libraries have not 2 machine does not 2 machine learning algorithm 2 machine learning application 2 machine learning model 2 words are most 1 - added service 1 - based approach 1 - based categorization 1 - based decoloniza- 1 - based environment 1 - based generator 1 - based interventions 1 - based linguistic 1 - based local 1 - based memory 1 - based model 1 - based natural 1 - based periodicals 1 - based reading 1 - based software 1 - based studies 1 - based substrate 1 - based systems 1 - based techniques 1 - based workbench 1 - building enterprise 1 - called digital 1 - called neuromorphic 1 - generated abstractions 1 - generated la- 1 - generated metadata 1 - generated output 1 - going research 1 - knowing oracle 1 - known annual 1 - known authors 1 - known biases 1 - learning algorithm 1 - learning applications 1 - learning arms Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 1 ai is not yet 1 ai was not only 1 algorithms are not smart 1 libraries are not as 1 model is not as 1 process was not ideal 1 processes are not as 1 results were not just Sizes of items; "Measures in words, how big is each item?" ---------------------------------------------------------- 7542 12-cohen-machine 7293 07-kim-ai 7195 03-plumb-humanities 7014 05-wiegand-cultures 6148 08-altman-building 5935 02-harper-generative 5793 10-morgan-bringing 5069 01-hintze-artificial 4796 09-lesk-fragility 4339 14-hansen-can 3910 11-prudhomme-taking 3623 06-jiang-cross 3101 04-janco-machine 3073 13-lucic-towards 1090 00-johnson-preface Readability of items; "How difficult is each item to read?" ----------------------------------------------------------- 74.0 10-morgan-bringing 63.0 08-altman-building 63.0 09-lesk-fragility 63.0 13-lucic-towards 63.0 14-hansen-can 59.0 02-harper-generative 56.0 01-hintze-artificial 55.0 06-jiang-cross 55.0 07-kim-ai 54.0 04-janco-machine 54.0 12-cohen-machine 51.0 11-prudhomme-taking 49.0 05-wiegand-cultures 47.0 00-johnson-preface 45.0 03-plumb-humanities Item summaries; "In a narrative form, how can each item be abstracted?" ----------------------------------------------------------------------- 00-johnson-preface The plan called for a survey and a series of workshops hosted across the country to explore, originally, "the national need for library based topic modeling tools in support of cross-disciplinary libraries ran concurrently with our grant — Cordell 2020 and Padilla 2019, which were commissioned by major players in the field, the Library of Congress and OCLC, respectively — and vi Machine Learning, Libraries, and Cross-Disciplinary Research We would like to thank the IMLS for providing essential funding support for the grant and the Thank you to the members of the Notre Dame IMLS grant team who, at of course, thanks to the 95 participants in our 2019 IMLS Grant Workshops (too many to enumerate here) and to the essay authors for sharing their expertise and perspectives in growing our collective knowledge of machine learning and its use in research, scholarship, and cultural heritage organizations. https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html 01-hintze-artificial Artificial Intelligence, with its ability to machine learn coupled to an almost human-like understanding, sounds like the ideal tool to the humanities. But are these technologies imbued with intuition or understanding, and do they learn like humans? In the 80s and 90s, as home computers were becoming more common, Hollywood was sensationalizing the idea of smart or human-like Artificial Intelligent machines (AI) through movies Machine learning allows us to learn from these data sets in ways that exceed human capabilities, while an artificial brain will eventually allow us to objectively describe a subjective experience (through quantifying neural activations or positively and negatively associated memories). The following paragraphs will explore current Machine Learning and Artificial Intelligence learning, to the point where our whole identity as human could be generously defined as the Just because humans and machine learning are both black Currently, machines do not learn but must be trained, typically with human-labeled data. 02-harper-generative Reddit have each issued their own bans on the category of machine-generated or -altered content that is commonly termed "deep fakes" (Cohen 2020; Romm, Harwell, and Stanley-Becker TV because of their dystopian implications, deep fakes are just one application of generative machine learning. Figure 2.2: Images generated with a simple statistical model appear as noise as the model is insufficient to capture the structure of the real data (Markov chains trained using wine bottles and 1In many examples, I have used the Google QuickDraw Dataset to highlight features of generative machine learning. (?iiTb,ff;Bi?m#X+QKf;QQ;H2+''2�iBp2H�#f[mB+F/''�r@/�i�b2i) shows the generator learning how to produce better sketches over time. built a GAN that generates high-quality photo-realistic images of people (Karras, Laine, and Aila Beyond medicine and autonomous vehicles, generative data augmentation will progressively impact other imaging-heavy fields (Shorten and Khoshgoftaar 2019) like GANs in Action: Deep Learning with Generative Adversarial Networks. 03-plumb-humanities Respondents such as Mark Algee-Hewitt pointed out that literary scholars employ computational statistical models in order to reveal something about texts that human readers Machine learning, and word embedding algorithms in particular, may have a unique ability to shift this conversation into new territory, where scholars Acknowledging this helps contextualize machine learning algorithms for text analysis tasks in the humanities, but also highlights data curation challenges This naturally raises questions about how machine learning algorithms like word embeddings are implemented for text analysis, and how they Based on the potential for word embeddings to model semantic spaces for different corpora and compare the distribution of terms, the next step was to build a corpus of non-canonical Designing humanities research with novel word embedding models stands to widen the territory where machine learning engineers look for conceptual concepts Systematic data curation, combined with word embedding algorithms, represent a new interpretive system for literary scholars. 04-janco-machine Tools like RunwayML, the Teachable Machine, and Google AutoML allow researchers to train project-specific Since 2014, dramatic innovations in machine learning have occurred, providing new capabilities in computer vision, natural language processing, and other areas of applied artificial intelligence. deliberately and identify how machine learning methods can benefit a scholar''s research? for identifying basic tasks that can be completed by computers in ways that advance humanities research (2000). When working with texts or images, machine learning models are presently capable of making simple annotations and associations. Google''s Teachable Machine offers an intuitive web application that humanities faculty and students can use to train classification models for images, sounds, and poses. Machine learning models offer a variety of ways to identify similarity and difference with research materials. goals of academic researchers in the humanities with the technical possibilities of machine learning. "Scholarly Primitives: What Methods Do Humanities Researchers Have 05-wiegand-cultures traditional role, librarians in the 20th century added a new function—discovery—teaching people to find and use the library''s collected scholarship. learning in the library as the next step beyond collecting, with librarians instructing on information infrastructure with the goal of empowering library users to find, evaluate, and use scholarly go far beyond local library collections to a global perspective and normative practice of participation at scale in innovative emerging technologies such as Machine Learning. start by using Machine Learning tools to automate alerts of new content in a narrow area of interest and help researchers at all levels find and focus on problem-solving. A library that adapted Machine Learning as an innovation technology would improve its practices; add new services; choose, use, and license collections differently; utilize all spaces for learning; and role model innovative leadership. opening local collections to discovery and use in order to create new knowledge through digitization and semantic linking, with cross-disciplinary technologies to augment traditional research 06-jiang-cross Cross-disciplinary research matters, because (1) it provides an understanding of complex problems that require a multifaceted approach to solve; (2) it combines disciplinary breadth with the ability to collaborate One of the most popular cross-disciplinary research topics/programs is Machine Learning + top strengths of conducting cross-disciplinary ML research and give two examples based on my marriages, just like collaborators expect to have successful project outcomes (Robinson and Blanton 1993; Pettigrew 2000; Xu et al. The history professor Liang Cai and I have collaborated on an international research project titled "Digital Empires: Structured Biographical and Social Network Analysis of Early Chinese We have enjoyed our collaboration and the power of cross-disciplinary research. Specifically, I presented the top strengths of producing successful cross-disciplinary ML research: (1) Partners are satisfied with communication. "The Challenges of Cross lj Disciplinary Research." Social Research Collaboration." Social Studies of Science 33, no. "Building Cross-Disciplinary Research Collaborations." 07-kim-ai does not provide an easy answer to the question of how one should program moral decisionmaking into intelligent machines. Described below are some of the significant ethical challenges that autonomous AI systems such as military robots present. 11Note that this moral decision-making process can be modeled with a rule-based symbolic AI approach, a machine 13(Kahn 2012) also argues that the resulting increase in the number of wars by the use of military robots will be morally 15This black-box nature of AI systems powered by machine learning has raised great concern among many AI researchers in recent years. agency in the AI -powered automated information environment presents an ethical challenge In this chapter, I discussed four significant ethical challenges that automating decisions and actions with AI presents: (a) moral desensitization; (b) unintended outcomes; (c) surrender of are at an early stage in developing AI applications and applying machine learning and deep learning techniques to improve library services, systems, and operations. 08-altman-building As you begin ingesting and preparing data, you''ll want to explore possible machine learning algorithms to perform on your dataset. Start by determining what general type of learning algorithm you need, and proceed from there to research and select one that While the final output of a machine learning workflow is some sort of intelligent model, The pipeline for a machine learning project generally comprises five stages: data acquisition, data preparation, model training and testing, evaluation and analysis, and application of results. good idea to save a copy in the rawest possible form and treat that copy as immutable, at least during the initial phase of testing different algorithms or configurations. algorithm uses the training data to "learn" a set of rules that it can subsequently apply to new, Immutable data storage can benefit the batch-processing ML pipeline, especially during the initial research and development phase. 09-lesk-fragility Machine learning systems have a set of data for training. of the real problem (if you train a machine translation program solely on engineering documents, there may be a lot of training data, including many noisy points, and the program may decide on Many popular magazines have discussed this problem; Forbes, for example, had an explanation of how the choice of datasets can produce a biased result without any deliberate attempt to used to suggest malicious creation of training data or examples of data designed to deceive machine learning systems. blood pressure, and lower blood pressure decreases the risk of heart attacks." Then I have to explain that the paper evaluates 32 possibilities (prior/current ownership ⇥ cats/dogs ⇥ 4 medical compare the performance of machine learning systems for medical diagnosis with actual doctors If a program is constantly learning from new data, there is no list of previously fixed failures to 10-morgan-bringing advent of computers, the idea of sharing cataloging data as MARC (machine readable cataloging) the full text of its collections to enhance bibliographic description and resulting public service. ability to save, organize, and retrieve data; on the whole, the library profession does not understand the concept of a "data structure." For example, tab-delimited files, CSV (comma-separated the use of data structures, computers store and retrieve information. Libraries use computers to store, organize, preserve, and disseminate the gray literature of our time, and we call these systems "institutional repositories." In all Using such a process, there are really only four different types of machine learning: classification, clustering, regression, and dimension reduction. Given a set of previously classified menus, one could create a model There are many possible ways to enhance library collections and services through the use of machine learning. of plain text files and an integer, Topic Modeling Tool will create a weighted list of latent themes 11-prudhomme-taking Combining automatic processes to assist in supporting inventory management with a focus on descriptive metadata, a machine learning solution could help alleviate time-consuming and relatively expensive metadata tagging tasks, Deep learning neural networks are more effective in feature detection as they are able to solve complex problems such as image classification with greater accuracy when trained with large datasets. For images, how can archives build a data-labeling pipeline into their digital curation workflow that enables machine learning of collections? machine learning is only good so long as value is added, archives and libraries will need to think As deep learning applications will only be as effective as the data, archives and libraries should expand their Along with greater computing capabilities, artificial intelligence could be an opportunity for libraries and archives to boost the discovery of their digital collections by pushing text and image 12-cohen-machine archivally focused project that emerged from a partnership between the Pine Mountain Settlement School (PMSS)1 in Harlan County, Kentucky, and scholars and students at Berea College. a latent social network of historical families represented by the images held in one local archive, curricula for use in Kentucky public schools with PMSS archival materials. That decision led a team of Berea College undergraduate and faculty researchers to scrape the data from the PMSS archive site and supplement the images and transcriptions it contains with available textual metadata drawn from the site.9 Alongside the WordPress facial recognition software to identify the persons in historic photographs in the PMSS archives. We demonstrated to the local members at Pine Mountain how our use case and its constraints for digital archives fit with the current standards for the fair use of copyrighted materials 13-lucic-towards Reading Chicago Reading1 is a grant-supported digital humanities project that takes as its object the "One Book One Chicago" (OBOC) program2 of the Chicago Public Library. A related question is the focus of this paper: by associating place names with sentiment scores in Chicago-themed OBOC The HathiTrust research portal permits the extraction of non-consumptive features of the works included in the digital library, even those that are still under copyright. The place names extracted from our three Chicago-setting OBOC books allowed us to focus Our interest in creating a dataset of Chicago place names extracted from literature led us to Kaser''s book contains several indexes that can serve as sources of labeled data or instances in which Chicago locations are mentioned. the index as a source of already-labeled data for Chicago place names. associated sentiment scores for Chicago place names in the three OBOC selections centered on 14-hansen-can I would use the Mathematical Subject Classification (MSC) values assigned to the publications in MathSciNet1 to create a temporal citation network which would allow me to visualize Machine-learning-based categorization needs data to classify, which in our case automated categorization of mathematics, we were dilettantes in the world of machine learning. what happens when smarter and more capable minds tackle the problem of classifying mathematics and other highly technical subjects using advanced machine learning techniques. 9Mathematical Subject Classification (MSC) values in MathSciNet and zbMath are a particularly interesting categorization set to work with as they are assigned and reviewed by a subject area expert editor and an active researcher in the 16See ?iiTb,ff�+�/2KB+XKB+''QbQ7iX+QKf. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 One really interesting part of the machine learning method used by Microsoft was that it did not rely only on information from the article being replace the work of humans categorizing mathematics articles indexed in a database, which for