Abstract
We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences. Using a set of linguistically principled transformation patterns, sentences are converted into a hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. As opposed to previously proposed sentence splitting approaches, which commonly do not take into account discourse-level aspects, our TS approach preserves the semantic relationship of the decomposed constituents in the output. A comparative analysis with the annotations contained in RST-DT shows that we capture the contextual hierarchy between the split sentences with a precision of 89% and reach an average precision of 69% for the classification of the rhetorical relations that hold between them. Moreover, an integration into state-of-the-art Open Information Extraction (IE) systems reveals that when applying our TS approach as a pre-processing step, the generated relational tuples are enriched with additional meta information, resulting in a novel lightweight semantic representation for the task of Open IE.- Anthology ID:
- 2022.codi-1.9
- Volume:
- Proceedings of the 3rd Workshop on Computational Approaches to Discourse
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea and Online
- Venue:
- CODI
- SIG:
- Publisher:
- International Conference on Computational Linguistics
- Note:
- Pages:
- 64–76
- Language:
- URL:
- https://aclanthology.org/2022.codi-1.9
- DOI:
- Cite (ACL):
- Christina Niklaus, André Freitas, and Siegfried Handschuh. 2022. Shallow Discourse Parsing for Open Information Extraction and Text Simplification. In Proceedings of the 3rd Workshop on Computational Approaches to Discourse, pages 64–76, Gyeongju, Republic of Korea and Online. International Conference on Computational Linguistics.
- Cite (Informal):
- Shallow Discourse Parsing for Open Information Extraction and Text Simplification (Niklaus et al., CODI 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.codi-1.9.pdf