The Proposition Bank: An Annotated Corpus of Semantic Roles

@article{Palmer2005ThePB,
  title={The Proposition Bank: An Annotated Corpus of Semantic Roles},
  author={Martha Palmer and Paul R. Kingsbury and Daniel Gildea},
  journal={Computational Linguistics},
  year={2005},
  volume={31},
  pages={71-106}
}
The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated. We… 

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References

SHOWING 1-10 OF 61 REFERENCES

Proposition Bank II: Delving Deeper

An overview of the second phase of PropBank Annotation, PropBank II, which is being applied to English and Chinese, and includes (Neodavidsonian) eventuality variables, nominal references, sense tagging, and connections to the Penn Discourse Treebank (PDTB), a project for annotating discourse connectives and their arguments.

Automatic Verb Classification Based on Statistical Distributions of Argument Structure

This work reports on supervised learning experiments to automatically classify three major types of English verbs, based on their argument structure-specifically, the thematic roles they assign to participants, using linguistically-motivated statistical indicators extracted from large annotated corpora to train the classifier.

Annotating the Propositions in the Penn Chinese Treebank

It is described how diathesis alternation patterns can be used to make coarse sense distinctions for Chinese verbs as a necessary step in annotating the predicate-structure of Chinese verbs.

The Necessity of Parsing for Predicate Argument Recognition

The effect of parser accuracy on these systems' performance is quantified, and the question of whether a flatter "chunked" representation of the input can be as effective for the purposes of semantic role identification is examined.

The Notion of Argument in Prepositional Phrase Attachment

This article refine the formulation of the problem of prepositional phrase (PP) attachment as a four-way disambiguation problem, and introduces a method to learn arguments and adjuncts based on a definition of arguments as a vector of features.

The Berkeley FrameNet Project

This report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work.

Thematic proto-roles and argument selection

As a novel attack on the perennially vexing questions of the theoretical status of thematic roles and the inventory of possible roles, this paper defends a strategy of basing accounts of roles on

Supertagging: An Approach to Almost Parsing

Novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques are proposed.

From Grammar to Lexicon: Unsupervised Learning of Lexical Syntax

  • M. Brent
  • Linguistics
    Comput. Linguistics
  • 1993
This paper describes an approach based on two principles, rely on local morpho-syntactic cues to structure rather than trying to parse entire sentences, and treat these cues as probabilistic rather than absolute indicators of syntactic structure.

Head-Driven Statistical Models for Natural Language Parsing

Three statistical models for natural language parsing are described, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree.
...