Interactive Data Analytics for the Humanities

Abstract

In this vision paper, we argue that current solutions to data analytics are not suitable for complex tasks from the humanities, as they are agnostic of the user and focused on static, predefined tasks with large-scale benchmarks. Instead, we believe that the human must be put into the loop to address small data scenarios that require expert domain knowledge and fluid, incrementally defined tasks, which are common for many humanities use cases. Besides the main challenges, we discuss existing and urgently required solutions to interactive data acquisition, model development, model interpretation, and system support for interactive data analytics. In the envisioned interactive systems, human users not only provide annotations to a machine learner, but train a model by using the system and demonstrating the task. The learning system will actively query the user for feedback, refine its model in real-time, and is able to explain its decisions. Our vision links natural language processing research with recent advances in machine learning, computer vision, and data management systems, as realizing this vision relies on combining expertise from all of these scientific fields. 1 Challenges in Analyzing Humanities Data Automated data analytics, aka. data mining and machine learning, is a key technology for enriching and interpreting data, making informed decisions, and developing new data-driven scientific methods across many disciplines in industry and academia. Although the potential of interactive problem solving was recognized early on [16], this field has not progressed very far beyond the initial work. In particular, interactive machine learning and data analytics have only recently received increased attention [98]. Current data analytics solutions focus predominantly on well-defined tasks that can be solved by processing large, homogeneous datasets available in a structured form. Consider for example recommender systems, which suggest new products based on the product's properties, the products that the customer has previously bought, and the collective behavior of the customer database [43]. The state-of-the-art relies on huge amounts of data-over one billion pairs of users and news items passively gathered-to train a deep neural network [28]. This may explain, why data analytics is conceived in a rather impersonal way, with algorithms working autonomously on passively collected data, although practice is quite the opposite. Most of the influence practitioners have, comes through interacting with data, including crafting the data and examining results. In the late 1990s, digitized data became widely available in the humanities as well. Since then, there has been a clear demand for data analytics approaches to tap into these

Preference-based interactive multi-document summarisation

“…Interactive Natural Language Processing (NLP) approaches that put the human in the loop gained increasing research interests recently (Amershi et al 2014;Gurevych et al 2018;Kreutzer et al 2018a). The user-system interaction enables personalised and user-adapted results by incrementally refining the underlying model based on a user's behaviour and by optimising the learning through actively querying for feedback and judgements.…”

Computational Text Analysis within the Humanities

“…Within the spectrum of possibilities there is one that requires considerable time and patience, but avoids risks with regard to potentially missing out an important component within complex analytical scenarios: building up a full pipeline or network of related subtasks in a careful bottom-up manner (necessarily making some selective choice regarding target discipline, language, genre etc.). Such a systematic bottom-up resource building approach is effectively what a lot of computationally oriented projects in the Digital Humanities have been taking in the past 5-10 years (see e. g., Biemann et al 2014;Kuhn, Alexiadou, et al 2016;ter Braake et al 2016;Gurevych et al 2018), with varying dynamic flexibility in the interaction between the computational side and Humanities scholars. Over time, the inventory of readily available tools is growing, such that access to reliable computational analysis models in the course of a hermeneutic process will become less and less constrained.…”

Computational text analysis within the Humanities: How to combine working practices from the contributing fields?

“…Within the spectrum of possibilities there is one that requires considerable time and patience, but avoids risks with regard to potentially missing out an important component within complex analytical scenarios: building up a full pipeline or network of related subtasks in a careful bottom-up manner (necessarily making some selective choice regarding target discipline, language, genre etc.). Such a systematic bottom-up resource building approach is effectively what a lot of computationally oriented projects in the Digital Humanities have been taking in the past 5-10 years (see e.g., Biemann et al 2014;ter Braake et al 2016;Kuhn et al 2016;Gurevych et al 2018), with varying dynamic flexibility in the interaction between the computational side and Humanities scholars. Over time, the inventory of readily available tools is growing, such that access to reliable computational analysis models in the course of a hermeneutic process will become less and less constrained.…”