Using Syntax to Resolve NPE in English
This paper describes a novel, syntax-based system for automatic detection and resolution of Noun Phrase Ellipsis (NPE) in English. The system takes in free input English text, detects the site of nominal elision, and if present, selects potential antecedent candidates. The rules are built using the syntactic information on ellipsis and its antecedent discussed in previous theoretical linguistics literature on NPE. Additionally, we prepare a curated dataset of 337 sentences from well-known, reliable sources, containing positive and negative samples of NPE. We split this dataset into two parts, and use one part to refine our rules and the other to test the performance of our final system. We get an F1-score of 76.47{\%} for detection and 70.27{\%} for NPE resolution on the testset. To the best of our knowledge, ours is the first system that detects and resolves NPE in English. The curated dataset used for this task, albeit small, covers a wide variety of NPE cases and will be made public for future work.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Projection-based Coreference Resolution Using Deep Syntax
The paper describes the system for coreference resolution in German and Russian, trained exclusively on coreference relations project ed through a parallel corpus from English. The resolver operates on the level of deep …
coreference-resolutionCoreference ResolutionSupervised Syntax-based Alignment between English Sentences and Abstract Meaning Representation Graphs
As alignment links are not given between English sentences and Abstract Meaning Representation (AMR) graphs in the AMR annotation, automatic alignment becomes indispensable for training an AMR parser. Previous studies fo…
Abstract Meaning RepresentationAMR ParsingGenerating Discourse Inferences from Unscoped Episodic Logical Formulas
Abstract Unscoped episodic logical form (ULF) is a semantic representation capturing the predicate-argument structure of English within the episodic logic formalism in relation to the syntactic structure, while leaving s…
Syntax-based data augmentation for Hungarian-English machine translation
We train Transformer-based neural machine translation models for Hungarian-English and English-Hungarian using the Hunglish2 corpus. Our best models achieve a BLEU score of 40.0 on HungarianEnglish and 33.4 on English-Hu…
Data AugmentationMachine TranslationTranslationPredicting Target Language CCG Supertags Improves Neural Machine Translation
Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly mo…
DecoderMachine TranslationNMTPrepositional Phrase Attachment+1