Text-based NP Enrichment
- URL: http://arxiv.org/abs/2109.12085v1
- Date: Fri, 24 Sep 2021 17:23:25 GMT
- Title: Text-based NP Enrichment
- Authors: Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty
- Abstract summary: We establish the task of text-based NP enrichment (TNE), that is, enriching each NP with all the preposition-mediated relations that hold between this and the other NPs in the text.
Humans recover such relations seamlessly, while current state-of-the-art models struggle with them due to the implicit nature of the problem.
We build the first large-scale dataset for the problem, provide the formal framing and scope of annotation, analyze the data, and report the result of fine-tuned neural language models on the task.
- Score: 51.403543011094975
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding the relations between entities denoted by NPs in text is a
critical part of human-like natural language understanding. However, only a
fraction of such relations is covered by NLP tasks and models nowadays. In this
work, we establish the task of text-based NP enrichment (TNE), that is,
enriching each NP with all the preposition-mediated relations that hold between
this and the other NPs in the text. The relations are represented as triplets,
each denoting two NPs linked via a preposition. Humans recover such relations
seamlessly, while current state-of-the-art models struggle with them due to the
implicit nature of the problem. We build the first large-scale dataset for the
problem, provide the formal framing and scope of annotation, analyze the data,
and report the result of fine-tuned neural language models on the task,
demonstrating the challenge it poses to current technology. We created a
webpage with the data, data-exploration UI, code, models, and demo to foster
further research into this challenging text understanding problem at
yanaiela.github.io/TNE/.
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