Getting More Out Of Syntax with PropS
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Abstract (arXiv)
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic post-processing or to information loss. To directly address the needs of semantic applications, we present PropS -- an output representation designed to explicitly and uniformly express much of the proposition structure which is implied from syntax, and an associated tool for extracting it from dependency trees.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2016.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 0; 2024: 1.
8 papers.
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- 2021Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledgecited by 40
- 2021Zero-Shot Information Extraction as a Unified Text-to-Triple Translationcited by 22
- 2021Syntactic and Semantic-driven Learning for Open Information Extractioncited by 2
- 2022IELM: An Open Information Extraction Benchmark for Pre-Trained Language Modelscited by 1
- 2021Context-Preserving Text Simplificationcited by 0
- 2022Collaborative Knowledge Graph Fusion by Exploiting the Open Corpuscited by 0
- 2024Knowledge Bases in Support of Large Language Models for Processing Web Newscited by 0
- 2024Knowledge Bases in Support of Large Language Models for Processing Web News13 Nov 2024
- 2022IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models25 Oct 2022
- 2022Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus15 Jun 2022
- 2022DeepStruct: Pretraining of Language Models for Structure Prediction21 May 2022
- 2021Zero-Shot Information Extraction as a Unified Text-to-Triple Translation23 Sep 2021
- 2021Context-Preserving Text Simplification24 May 2021
- 2021Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge14 Apr 2021
- 2021Syntactic and Semantic-driven Learning for Open Information Extraction5 Mar 2021