MONK

MONK is a digital environment designed to help humanities scholars discover and analyze patterns in the texts they study. It supports both micro analyses of the verbal texture of an individual text and macro analyses that let you locate texts in the context of a large document space consisting of hundreds or thousands of other texts. Shuttling between the “micro” and the “macro” is a distinctive feature of the MONK environment, where you may read as closely as you wish but can also practice many forms of what Franco Moretti has provocatively called “distant reading.”

MONK stands for Metadata Offer New Knowledge, and metadata (data about data) are at the heart of its very radical “divide and conquer” strategy. For every document in a MONK environment there are explicitly recorded metadata at the top level (bibliographical data), at the bottom level of individual word occurrence (lexical, morphological, and syntactic data), and at the mid-level of discursive organization (chapters, scenes, stanzas, etc).

This triple-decker structure of metadata helps to organize the MONK inventory of words in a collection. Visualization tools will play a critical role in helping scholars both formulate the questions they want to ask and interpret the result sets their questions produce.

Halfway through the project and in the midst of making some 150 milions words from four centuries of English searchable in new ways, we are discovering that the opportunities for visualization are dazzling, and that the challenges of wrangling diverse documents into comparable shape are daunting.  Standard NLP tools assume that written English follows the conventions of the Wall Street Journal or Reuters. They fail badly when trying to cope with texts outside that narrow domain.  But we can report good progress in constructing tools that are equally comfortable with a stanza from the Faerie Queene and a page from Moby Dick, Wuthering Heights, or Uncle Tom's Cabin.  The interface design for MONK has been largely in the hands of the Canadian members of the team. They have shown a consistent knack for combining the stylish with the functional.

It is clear that strategies of information retrieval and text mining have considerable promise for Literary Studies and other disciplines in the humanities. But there remains both a technical and a culture gap. How to bridge it and how to find the right words that will help in bridging it remains a challenge.