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Smatch: an Evaluation Metric for Semantic Feature Structures

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TLDR
This paper presents smatch, a metric that calculates the degree of overlap between two semantic feature structures, and gives an efficient algorithm to compute the metric and shows the results of an inter-annotator agreement study.
Abstract
The evaluation of whole-sentence semantic structures plays an important role in semantic parsing and large-scale semantic structure annotation. However, there is no widely-used metric to evaluate wholesentence semantic structures. In this paper, we present smatch, a metric that calculates the degree of overlap between two semantic feature structures. We give an efficient algorithm to compute the metric and show the results of an inter-annotator agreement study.

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References
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Proceedings ArticleDOI

Bleu: a Method for Automatic Evaluation of Machine Translation

TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
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A Study of Translation Edit Rate with Targeted Human Annotation

TL;DR: A new, intuitive measure for evaluating machine translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments is defined.
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Events in the Semantics of English: A Study in Subatomic Semantics

TL;DR: Focusing on the structure of meaning in English sentences at a "subatomic" level - that is, a level below the one most theories accept as basic or "atomic" - Parsons asserts that the semantics of simple English sentences require logical forms somewhat more complex than is normally assumed in natural language semantics.
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Learning to map sentences to logical form: structured classification with probabilistic categorial grammars

TL;DR: A learning algorithm is described that takes as input a training set of sentences labeled with expressions in the lambda calculus and induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence.
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From treebank to propbank

TL;DR: This paper describes the approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank and introduces metaframes as a technique for handling similar frames among near− synonymous verbs.
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