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Getting More Out Of Syntax with PropS

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arxiv 1603.01648 v1 pith:2WO27KOW submitted 2016-03-04 cs.CL

classification cs.CL
keywords dependencysemanticstructuresyntaxtreesapplicationsmuchoften
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Semantic Hypergraphs

    cs.IR 2019-08 conditional novelty 6.0 of 10

    A recursive typed hypergraph representation of natural language, parsed with a random forest and search tree, is enough to rank first on the WiRe57 open information extraction benchmark.

  2. Knowledge Bases in Support of Large Language Models for Processing Web News

    cs.CL 2024-11 conditional novelty 5.0 of 10

    BERTGraph, which adds rule-extracted relational graphs to BERT via a graph convolutional network, improves news classification accuracy over fine-tuned BERT on N24News and Snopes datasets.

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