Interpretable and Comparative Textual Dataset Exploration Using Near-Identity Mention Relations

Abstract

Dataset exploration is a set of techniques crucial in many research and data science projects. For textual datasets, commonly used techniques include topic modeling, document summarization, and methods related to dimension reduction. Despite their robustness, these techniques suffer from at least one of the following drawbacks: document summarization does not explicitly set documents in relation, the others yield summaries or topics that often are difficult to interpret and yield poor results for topics that consist of context-dependent terms. We propose a method for dataset exploration that employs cross-document near-identity resolution of mentions of semantic concepts, such as persons, other named entity types, events, actions. The method not only sets documents in relation and thus allows for comparative dataset exploration, but also yields well interpretable document representations. Additionally, due to the underlying approach for cross-document resolution of concept mentions, the method is able to set documents in relation as to their near-identity terms, e.g., synonyms that are not universally valid but only in the given dataset.