Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions

Hwang, Jena D.; Bhatia, Archna; Han, Na-Rae; O'Gorman, Tim; Srikumar, Vivek; Schneider, Nathan

doi:10.18653/v1/s17-1022

Cited by 11 publications

(10 citation statements)

References 34 publications

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“…Many of the component annotations have been applied to other languages: verbal multiword expressions (Savary et al, 2017;Ramisch et al, 2018) 2008; Qiu et al, 2011;Schneider et al, 2013;Martínez Alonso et al, 2015;Hellwig, 2017), and adposition supersenses (Hwang et al, 2017;Zhu et al, 2019). In this paper we focus on English, where comprehensive annotation is available.…”

Section: Lsr Tagging Frameworkmentioning

confidence: 99%

Lexical Semantic Recognition

Liu¹,

Hershcovich²,

Kranzlein³

et al. 2021

Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

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In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification/ classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.

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Section: Lsr Tagging Frameworkmentioning

confidence: 99%

Lexical Semantic Recognition

Liu¹,

Hershcovich²,

Kranzlein³

et al. 2021

Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

Self Cite

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show abstract

“…Many of the component annotations have been applied to other languages: verbal multiword expressions 2008; Qiu et al, 2011;Schneider et al, 2013;Martínez Alonso et al, 2015;Hellwig, 2017), and adposition supersenses (Hwang et al, 2017;Zhu et al, 2019). In this paper we focus on English, where comprehensive annotation is available.…”

Section: Lsr Tagging Frameworkmentioning

confidence: 99%

Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

2021

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In recent years, language models (LMs) have become almost synonymous with NLP. Pre-trained to "read" a large text corpus, such models are useful as both a representation layer as well as a source of world knowledge. But how well do they represent MWEs? This talk will discuss various problems in representing MWEs, and the extent to which LMs address them:• Do LMs capture the implicit relationship between constituents in compositional MWEs (from baby oil through parsley cake to cheeseburger stabbing)?

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“…SNACS categorizes the use of adpositions and case markers, including English possessives, into 50 coarse-grained supersense classes. Each adposition token is annotated as a construal construction with two of these supersenses (Hwang et al, 2017). A construal includes a SCENE ROLE and a FUNCTION, where the former expresses the adposition's meaning in context and the latter denotes its lexical meaning.…”

Section: Preposition Supersensesmentioning

confidence: 99%

PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

Kranzlein¹,

Manning²,

Peng³

et al. 2021

Preprint

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We present the Prepositions Annotated with Supersense 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.This work is licensed under a Creative Commons Attribution 4.0 International License. Licence details: http:// creativecommons.org/licenses/by/4.0/.1 PASTRIE is available at https://github.com/nert-nlp/pastrie 2 Automatic lemmas and part-of-speech tags are also included. 3 We may sometimes use the word "preposition" loosely to cover all of these categories. When specific analyses are being made, more precise terminology is used. 4 Information on how L1s were identified is included in §3.1. Following Rabinovich et al. ( 2018), we simply say "native speakers" or "L1" with the understanding that this is an imperfect assumption.

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Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions

Cited by 11 publications

References 34 publications

Lexical Semantic Recognition

Lexical Semantic Recognition

Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

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