Modern knowledge bases have matured to the extent of being
capable of complex reasoning at scale. Unfortunately, wide deployment
of this technology is still hindered by the fact that specifying the req-
uisite knowledge requires skills that most domain experts do not have,
and skilled knowledge engineers are in short supply. A way around this
problem could be to acquire knowledge from text. However, the current
knowledge acquisition technologies for information extraction are not up
to the task because logic reasoning systems are extremely sensitive to er-
rors in the acquired knowledge, and existing techniques lack the required
accuracy by too large of a margin. Because of the enormous complexity of
the problem, controlled natural languages (CNLs) were proposed in the
past, but even they lack high enough accuracy. Instead of tackling the
general problem of text understanding, our interest is in a related, but
different, area of knowledge authoring—a technology designed to enable
domain experts to manually create formalized knowledge using CNL.
Our approach adopts and formalizes the FrameNet methodology for rep-
resenting the meaning, enables incrementally-learnable and explainable
semantic parsing, and harnesses rich knowledge graphs like BabelNet in
the quest to obtain unique, disambiguated meaning of CNL sentences.
Our experiments show that this approach is 95.6% accurate in standard-
izing the semantic relations extracted from CNL sentences—far superior
to alternative systems.
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Logical Story Representations via FrameNet + Semantic Parsing
We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events. These schematic representations of the frame events, which we call Episodic Logic (EL) schemas, abstract constants to variables, preserving their types and relationships to other individuals in the same text. Due to the temporal semantics of the chosen logical formalism, all identified schemas in a text are also assigned temporally bound "episodes" and related to one another in time. The semantic role information from the FrameNet frames is also incorporated into the schema's type constraints. We describe an implementation of this method using a neural FrameNet parser, and discuss the approach's possible applications to question answering and open-domain event schema learning.
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- Award ID(s):
- 1940981
- NSF-PAR ID:
- 10359409
- Date Published:
- Journal Name:
- Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022)
- Page Range / eLocation ID:
- 19 to 23
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Modern knowledge bases have matured to the extent of being capable of complex reasoning at scale. Unfortunately, wide deployment of this technology is still hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and skilled knowledge engineers are in short supply. A way around this problem could be to acquire knowledge from text. However, the current knowledge acquisition technologies for information extraction are not up to the task because logic reasoning systems are extremely sensitive to errors in the acquired knowledge, and existing techniques lack the required accuracy by too large of a margin. Because of the enormous complexity of the problem, controlled natural languages (CNLs) were proposed in the past, but even they lack high enough accuracy. Instead of tackling the general problem of text understanding, our interest is in a related, but different, area of knowledge authoring—a technology designed to enable domain experts to manually create formalized knowledge using CNL. Our approach adopts and formalizes the FrameNet methodology for representing the meaning, enables incrementally-learnable and explainable semantic parsing, and harnesses rich knowledge graphs like BabelNet in the quest to obtain unique, disambiguated meaning of CNL sentences. Our experiments show that this approach is 95.6% accurate in standardizing the semantic relations extracted from CNL sentences—far superior to alternative systems.more » « less
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In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help to reason over the states of involved entities in a procedural text. We consider a deep semantic parser (TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework. Second, we integrate semantic parsing information into state-of-the-art neural models for procedural reasoning. Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.more » « less
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We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process. In lieu of a pre-made corpus of stories, our dataset is a continuous feed of “situation samples” from a pre-trained language model, which are then parsed into FrameNet frames, mapped into simple behavioral schemas, and combined and generalized into complex, hierarchical schemas for a variety of everyday scenarios. We show that careful sampling from the language model can help emphasize stereotypical properties of situations and de-emphasize irrelevant details, and that the resulting schemas specify situations more comprehensively than those learned by other systems.more » « less
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null (Ed.)“Episodic Logic: Unscoped Logical Form” (EL-ULF) is a semantic representation capturing predicate-argument structure as well as more challenging aspects of language within the Episodic Logic formalism. We present the first learned approach for parsing sentences into ULFs, using a growing set of annotated examples. The results provide a strong baseline for future improvement. Our method learns a sequence-to-sequence model for predicting the transition action sequence within a modified cache transition system. We evaluate the efficacy of type grammar-based constraints, a word-to-symbol lexicon, and transition system state features in this task. Our system is availableat https://github.com/genelkim/ ulf-transition-parser. We also present the first official annotated ULF dataset at https://www.cs.rochester.edu/u/ gkim21/ulf/resources/.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2022.distcurate-1.3
