Tag: natural language processing
Bancroft to Explore Text Analysis as Aid in Analyzing, Processing, and Providing Access to Text-based Archival Collections
Mary W. Elings, Head of Digital Collections, The Bancroft Library
The Bancroft Library recently began testing a theory discussed at the Radcliffe Workshop on Technology & Archival Processing held at Harvard’s Radcliffe College in early April 2014. The theory suggested that archives can use text analysis tools and topic modelling — a type of statistical model for discovering the abstract “topics” that occur in a collection of documents — to analyze text-based archival collections in order to aid in analyzing, processing and describing collections, as well as improving access.
Helping us to test this theory, the Bancroft welcomed summer intern Janine Heiser from the UC Berkeley School of Information. Over the summer, supported by an ISchool Summer Non-profit Internship Grant, Ms. Heiser worked with digitized analog archival materials to test this theory, answer specific research questions, and define use cases that will help us determine if text analysis and topic modelling are viable technologies to aid us in our archival work. Based on her work over the summer, the Bancroft has recently awarded Ms. Heiser an Archival Technologies Fellowship for 2015 so that she can continue the work she began in the summer and further develop and test her work.
During her summer internship, Ms. Heiser created a web-based application, called “ArchExtract” that extracts topics and named entities (people, places, subjects, dates, etc.) from a given collection. This application implements and extends various natural language processing software tools such as MALLET and the Stanford Core NLP toolkit. To test and refine this web application, Ms. Heiser used collections with an existing catalog record and/or finding aid, namely the John Muir Correspondence collection, which was digitized in 2009.
For a given collection, an archivist can compare the topics and named entities that ArchExtract outputs to the topics found in the extant descriptive information, looking at the similarities and differences between the two in order to verify ArchExtract’s accuracy. After evaluating the accuracy, the ArchExtract application can be improved and/or refined.
Ms. Heiser also worked with collections that either have minimal description or no extant description in order to further explore this theory as we test the tool further. Working with Bancroft archivists, Ms. Heiser will determine if the web application is successful, where it falls short, and what the next steps might be in exploring this and other text analysis tools to aid in processing collections.
The hope is that automated text analysis will be a way for libraries and archives to use this technology to readily identify the major topics found in a collection, and potentially identify named entities found in the text, and their frequency, thus giving archivists a good understanding of the scope and content of a collection before it is processed. This could help in identifying processing priorities, funding opportunities, and ultimately helping users identify what is found in the collection.
Ms. Heiser is a second year masters’ student at the UC Berkeley School of Information where she is learning the theory and practice of storing, retrieving and analyzing digital information in a variety of contexts and is currently taking coursework in natural language processing with Marti Hearst. Prior to the ISchool, Ms. Heiser worked at several companies where she helped develop database systems and software for political parties, non-profits organizations, and an online music distributor. In her free time, she likes to go running and hiking around the bay area. Ms. Heiser was also one of our participants in the #HackFSM hackathon! She was awarded an ISchool Summer Non-profit Internship Grant to support her work at Bancroft this summer and has been awarded an Archival Technologies Fellowship at Bancroft for 2015.