Polysemy in Controlled Natural Language Texts
This addresses the limitation of computational semantics and logic-based CNLs in handling polysemy for content words, enabling more natural interpretation in multi-domain contexts, though it appears incremental as it builds on existing CNL frameworks.
The paper tackles the problem of word sense disambiguation for content words in controlled natural languages (CNL), which are typically limited to functional words, by integrating micro-ontologies and multi-word units to incorporate polysemous background knowledge. The result is an extension of Attempto Controlled English (ACE) into a more natural CNL named PAO, demonstrated to cover narrative multi-domain texts.
Computational semantics and logic-based controlled natural languages (CNL) do not address systematically the word sense disambiguation problem of content words, i.e., they tend to interpret only some functional words that are crucial for construction of discourse representation structures. We show that micro-ontologies and multi-word units allow integration of the rich and polysemous multi-domain background knowledge into CNL thus providing interpretation for the content words. The proposed approach is demonstrated by extending the Attempto Controlled English (ACE) with polysemous and procedural constructs resulting in a more natural CNL named PAO covering narrative multi-domain texts.