Semantic Proto-Roles

Reisinger, Drew; Rudinger, Rachel; Ferraro, Francis; Harman, Craig; Rawlins, Kyle; Durme, Benjamin Van

doi:10.1162/tacl_a_00152

Cited by 63 publications

(76 citation statements)

References 24 publications

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“…Our work instead grounds into a fixed set of attributes but leverages language on a broader scale to learn about more verbs in more diverse set of frames. In this, our work can be seen as exploring predicate lexical semantics in the vein of semantic proto-roles (Dowty, 1991;Kako, 2006;Reisinger et al, 2015), but instead affording pairwise, physical relations between roles .…”

Section: Related Workmentioning

confidence: 99%

Verb Physics: Relative Physical Knowledge of Actions and Objects

Forbes

Choi

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., "My house is bigger than me." However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, "Tyler entered his house" implies that his house is bigger than Tyler.In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.

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Section: Related Workmentioning

confidence: 99%

Verb Physics: Relative Physical Knowledge of Actions and Objects

Forbes

Choi

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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“…This choice of axes is aimed at allowing our framework to capture not only the standard EPISODIC-HABITUAL-GENERIC distinction, but also phenomena that do not fit neatly into this distinction, such as taxonomic reference, abstract reference, and weak definites. The idea here is similar to prior decompositional semantics work on semantic protoroles (Reisinger et al, 2015;White et al, 2016White et al, , 2017, which associates categories like AGENT or PATIENT with sets of more basic properties, such as volitionality, causation, change-of-state, etc., and is similarly inspired by classic theoretical work (Dowty, 1991).…”

Section: Annotation Frameworkmentioning

confidence: 93%

“…To remedy this, we propose a novel framework for capturing linguistic expressions of generalization. Taking inspiration from decompositional semantics (Reisinger et al, 2015;White et al, 2016), we suggest that linguistic expressions of generalization should be captured in a continuous multilabel system, rather than a multi-class system. We do this by decomposing categories such as EPISODIC, HABITUAL, and GENERIC into simple referential properties of predicates and their arguments.…”

Section: Introductionmentioning

confidence: 99%

Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

Govindarajan

Durme

White

2019

Transactions of the Association for Computational Linguistics

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We present a novel semantic framework for modeling linguistic expressions of generalization-generic, habitual, and episodic statements-as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information-including handengineered features and static (GloVe) and contextual (ELMo) word embeddings-for predicting expressions of generalization.

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“…Various features in addition to the embeddings of verbs and prepositions were also tested. The features we experimented with include semantic protorole property scores (Reisinger et al 2015) of the target PP (normalized mean across 5 annotators), mutual information (MI) (Aldezabal et al 2002), word embeddings of the nominal head token of the NP under the PP in question, existence of a direct object, and various interaction terms between the features (e.g., additive, subtractive, inner/outer products).…”

Section: Modelmentioning

confidence: 99%

Predicting the Argumenthood of English Prepositional Phrases

Kim

Rawlins

Durme

et al. 2019

AAAI

Self Cite

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Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such as semantic role labeling (SRL) and prepositional phrase (PP) attachment disambiguation. In theoretical linguistics, many diagnostic tests for argumenthood exist but they often yield conflicting and potentially gradient results. This is especially the case for syntactically oblique items such as PPs. We propose two PP argumenthood prediction tasks branching from these two motivations: (1) binary argumentadjunct classification of PPs in VerbNet, and (2) gradient argumenthood prediction using human judgments as gold standard, and report results from prediction models that use pretrained word embeddings and other linguistically informed features. Our best results on each task are (1) acc. = 0.955, F1 = 0.954 (ELMo+BiLSTM) and (2) Pearson's r = 0.624 (word2vec+MLP). Furthermore, we demonstrate the utility of argumenthood prediction in improving sentence representations via performance gains on SRL when a sentence encoder is pretrained with our tasks.

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Semantic Proto-Roles

Cited by 63 publications

References 24 publications

Verb Physics: Relative Physical Knowledge of Actions and Objects

Verb Physics: Relative Physical Knowledge of Actions and Objects

Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

Predicting the Argumenthood of English Prepositional Phrases

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