Understanding Genre in a Collection of a Million Volumes
01 Jan 2014-
About: The article was published on 2014-01-01 and is currently open access. It has received 13 citations till now.
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TL;DR: A view of digital humanities and English linguistics from within both disciplines can be found in this article, where a corpus linguist based in a digital humanities center is presented, arguing that much of what we do is digital humanities, even if we do not always recognize it as such.
Abstract: Where there is any perception of digital humanities (DH) within the field of English linguistics, it may be seen as a technical practice preoccupied with digitizing texts and producing digital editions. The one occurrence of digital humanities in JEngL’s archival content is a passing reference to digital editions and the role of editors—in an interview rather than a research article (Grant 2014). Within DH, there is a more vigorous conversation about what DH fundamentally is, alongside creative methodological questions about what it can be. That is because DH—from within—is largely viewed as a methodological challenge, driven by meaningful, even urgent research questions originating not only in the humanities but also in the social sciences which can be most effectively addressed via the development of new digital methods and tools. If DH is a methodological practice, it is in the sense of methods and epistemology: asking and debating how it is that we can know what we need to know, and testing the efficacy of selected digital methods in the service of specific research questions. As a corpus linguist based in a DH center, I present here a view of DH and English linguistics from within both disciplines. My discussion begins with a focus on corpus linguistics, but also includes English linguistics more generally, and linguistics as a whole. I argue that we as linguists should care about DH, not only because much of what we do is DH (even if we do not always recognize it as such), but also because collaborations between English linguistics and DH will be fruitful for all of us. Research questions in DH are wide-ranging; recent major DH projects that encompass humanities and social sciences include:
1 citations
01 Jan 2017
TL;DR: A new method for dimensionality reduction, “stable random projection,” (hereafter “SRP”) distinctly suited for large textual corpora like those used in the digital humanities, that is computationally efficient and easily parallelizable; scales to the largest digital libraries; and creates a standard Dimensionality reduction space for all texts so that corpora and models can be easily exchanged.
Abstract: This paper describes a new method for dimensionality reduction, “stable random projection,” (hereafter “SRP”) distinctly suited for large textual corpora like those used in the digital humanities. The method is computationally efficient and easily parallelizable; scales to the largest digital libraries; and creates a standard dimensionality reduction space for all texts so that corpora and models can be easily exchanged. The resulting space makes a wide variety of applications suitable to bag-of-words data, such as nearest neighbor searches, classification, and semantic querying possible with data sets an order of magnitude smaller in size than traditional feature counts. SRP is a minimal, universal dimensionality reduction with two distinctive features: 1. It makes no distinction between inand out-ofdomain vocabularies. In particular, unlike standard dimensionality reduction it creates a single space that can hold documents of any language. 2. It is trivially parallelizable, both on a local machine and through web-based architectures because it relies only on code that can be easily transferred across servers, rather than requiring large matrices or model parameters. These two features allow dimensionality reduction to be conceived of as a piece of infrastructure for digital humanities work, rather than just an ad-hoc convention used in a particular project. This method is particularly useful for provisioners and users of text data on extremely large and/or multilingual corpora. This creates a number of new applications for dimensionality reduction, both in scale and in type. SRP features could usefully be distributed by libraries as a (much smaller and easier to work with) supplement to feature counts. After a description of the method, some novel uses for dimensionality reduction on such libraries are shown using a sharable dataset of approximately 4,500,000 books projected into SRP-space from the Hathi Trust.
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TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.
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