Jakob Prange


2021

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Subcategorizing Adverbials in Universal Conceptual Cognitive AnnotationUniversal Conceptual Cognitive Annotation
Zhuxin Wang | Jakob Prange | Nathan Schneider
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

Universal Conceptual Cognitive Annotation (UCCA) is a semantic annotation scheme that organizes texts into coarse predicate-argument structure, offering broad coverage of semantic phenomena. At the same time, there is still need for a finer-grained treatment of many of the categories. The Adverbial category is of special interest, as it covers a wide range of fundamentally different meanings such as negation, causation, aspect, and event quantification. In this paper we introduce a refinement annotation scheme for UCCA’s Adverbial category, showing that UCCA Adverbials can indeed be subcategorized into at least 7 semantic types, and doing so can help clarify and disambiguate the otherwise coarse-grained labels. We provide a preliminary set of annotation guidelines, as well as pilot annotation experiments with high inter-annotator agreement, confirming the validity of the scheme.

2020

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Comparison by Conversion : Reverse-Engineering UCCA from Syntax and Lexical SemanticsUCCA from Syntax and Lexical Semantics
Daniel Hershcovich | Nathan Schneider | Dotan Dvir | Jakob Prange | Miryam de Lhoneux | Omri Abend
Proceedings of the 28th International Conference on Computational Linguistics

Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods : (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser qualityindicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.

2019

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Semantically Constrained Multilayer Annotation : The Case of Coreference
Jakob Prange | Nathan Schneider | Omri Abend
Proceedings of the First International Workshop on Designing Meaning Representations

We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions. We argue that this allows coreference annotators to sidestep some of the challenges faced in other schemes, which do not enforce consistency with predicate-argument structure and vary widely in what kinds of mentions they annotate and how. The proposed approach is examined with a pilot annotation study and compared with annotations from other schemes.

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Made for Each Other : Broad-Coverage Semantic Structures Meet Preposition Supersenses
Jakob Prange | Nathan Schneider | Omri Abend
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Universal Conceptual Cognitive Annotation (UCCA ; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles. We argue that lexicon-free annotation of the semantic roles marked by prepositions, as formulated by Schneider et al. (2018), is complementary and suitable for integration within UCCA. We show empirically for English that the schemes, though annotated independently, are compatible and can be combined in a single semantic graph. A comparison of several approaches to parsing the integrated representation lays the groundwork for future research on this task.