This Table is Different: A WordNet-Based Approach to Identifying References to Document Entities

Abstract

Writing intended to inform frequently contains references to document entities (DEs), a mixed class that includes orthographically structured items (e.g., illustrations, sections,lists) and discourse entities (arguments, suggestions,points). Such references are vital to the interpretation of documents, but they often eschew identifiers such as "Figure 1" for inexplicit phrases like "in this figure" or"from these premises". We examine inexplicit references to DEs, termed DE references, and recast the problem of their automatic detection into the determination of relevant word senses. We then show the feasibility of machine learning for the detection of DE relevant word senses, using a corpus of human-labeled synsets from WordNet. We test cross-domain performance by gathering lemmas and synsets from three corpora: website privacy policies, Wikipedia articles, and Wikibooks textbooks. Identifying DE references will enable language technologies to use the information encoded by them, permitting the automatic generation of finely-tuned descriptions of DEs and the presentation of richly-structured information to readers