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Survey: Multiword Expression Processing: A Survey

Summary

Multiword expressions (MWEs), such as idioms, light-verb constructions, phrasal verbs, compounds and multiword named entities, behave partly like single words even though they span several tokens. This breaks the usual split between lexicon and grammar and causes problems for NLP systems. The authors are members of the PARSEME network. They argue that earlier reviews either focused on linguistics or covered only narrow slices of the field, so no overall picture existed. To fill that gap, they propose a conceptual framework that separates MWE processing into two subtasks: discovery, which builds lexicons of expressions from corpora, and identification, which marks MWE occurrences in running text. The framework then examines how these subtasks interact with parsing and machine translation. A central idea is "orchestration", meaning when MWE processing happens relative to the downstream task, and how that timing shapes what a system can handle. The survey links key MWE properties (discontiguity, variability, non-compositionality) to concrete challenges. It also gives practitioners a shared vocabulary and a research agenda.

Key points

  • The survey adopts the Baldwin and Kim (2010) definition of MWEs: expressions that split into multiple lexemes and show lexical, syntactic, semantic, pragmatic and/or statistical idiomaticity.
  • Prior work is positioned along three dimensions: the task at hand, the orchestration scenario, and the MWE category. The authors find coverage across this space uneven and comparative evaluations rare.
  • The open issues they identify are:
    • evaluation, including inconsistent annotations and a lack of extrinsic evaluation for discovery;
    • the need for large-scale comparative studies;
    • extending the analysis to further use cases, such as information extraction and sentiment analysis;
    • limited dictionary coverage, which automatic discovery could improve.
  • The appendix lists resources and tools:
    • corpora and lexicons: the datasets released for the PARSEME shared task, along with Streusle, Wiki50, TED-MWE, BabelNet and JRC-Names;
    • treebanks: IMST, Prague Dependency Treebank, French Treebank, Szeged;
    • processing tools: mwetoolkit, jMWE, AMALGrAM;
    • MWE-aware MT systems: ITS-2, Apertium, OpenLogos.
  • The work was supported by PARSEME (COST Action IC1207) and published in Computational Linguistics in 2017 (doi:10.1162/COLI_a_00302).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

40 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 34 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00302

@article{constant2017survey,
  title = {{Survey: Multiword Expression Processing: A Survey}},
  author = {Constant, Mathieu and Eryiǧit, Gülşen and Monti, Johanna and Plas, Lonneke van der and Ramisch, Carlos and Rosner, Michael and Todirascu, Amalia},
  journal = {Computational Linguistics},
  year = {2017},
  doi = {10.1162/coli\_a\_00302},
  url = {https://doi.org/10.1162/coli_a_00302}
}

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