The Extraction of Complex Information and Encoding Density (EXCITED)
Project B5 Completed
Project B5 addresses the problem of automatic relation extraction from densely encoded texts. Focusing on cross-sentence relation extraction, the project takes into account different linguistic encodings of a given relation. The research is corpus-based, compiling a collection of syntactically analysed mentions of selected relations exhibiting variation in encoding density.
The insights gained from analysis will be used to optimize automatic relation extraction taking into account encoding density and its relation to information density, and further shed light on whether the observed variation can be explained by uniform information density (UID).
Keywords: computational linguistics, relation extraction, distant supervision
Other Area-B Projects
- Information Density in English Scientific Writing: A Diachronic Perspective B1
- Cognitive Modelling of Information Density for Discourse Relations B2
- Information Theory and Ellipsis Redundancy B3
- Modeling and Measuring Information Density B4
- Neural Feature and Representation Learning for Information Density Based Translationese Classification B6
- Modelling Human Translation with a Noisy Channel B7