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Creators/Authors contains: "Wein, Shira"

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  1. ; (, Proceedings of the 4th International Workshop on Designing Meaning Representations)
    Abstract Meaning Representation (AMR) is a popular semantic annotation schema that presents sentence meaning as a graph while abstracting away from syntax. It was originally designed for English, but has since been extended to a variety of non-English versions. These cross-lingual adaptations, to varying degrees, incorporate language-specific features necessary to effectively capture the semantics of the language being annotated. Uniform Meaning Representation (UMR) on the other hand, the multilingual extension of AMR, was designed specifically for uniform cross-lingual application. In this work, we discuss these two approaches to extending AMR beyond English. We describe both approaches, compare the information they capture for a case language (Spanish), and outline implications for future work. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ; ; ; et al (, The 4th International Workshop on Designing Meaning Representations)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; ; ; et al (, Proceedings of the 4th International Workshop on Designing Meaning Representations)
    Rooted in AMR, Uniform Meaning Representation (UMR) is a graph-based formalism with nodes as concepts and edges as relations between them. When used to represent natural language semantics, UMR maps words in a sentence to concepts in the UMR graph. Multiword expressions (MWEs) pose a particular challenge to UMR annotation because they deviate from the default one-to-one mapping between words and concepts. There are different types of MWEs which require different kinds of annotation that must be specified in guidelines. This paper discusses the specific treatment for each type of MWE in UMR. 
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    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, SIGCSE)
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  5. ; ; (, Proceedings of the 14th Linguistic Annotation Workshop)
    Prepositional supersense annotation is time-consuming and requires expert training. Here, we present two sensible methods for obtaining prepositional supersense annotations indirectly by eliciting surface substitution and similarity judgments. Four pilot studies suggest that both methods have potential for producing prepositional supersense annotations that are comparable in quality to expert annotations. 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; (, Proceedings of the 14th Linguistic Annotation Workshop)
    We present the Prepositions Annotated with Supsersense Tags in Reddit International English (“PASTRIE”) corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish. The annotations are comprehensive, covering all preposition types and tokens in the sample. Along with the corpus, we provide analysis of distributional patterns across the included L1s and a discussion of the influence of L1s on L2 preposition choice. 
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    Accepted Manuscript Full Text Available