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# Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches

**Authors:** [Tianze Shi](https://md.mova.elamur.ai/free/en/docs/papers/authors/tianze-shi/), [Lillian Lee](https://md.mova.elamur.ai/free/en/docs/papers/authors/lillian-lee/) · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020) **Link:** [aclanthology.org/2020.acl-main.775](https://aclanthology.org/2020.acl-main.775/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**2** citations](https://openalex.org/works?filter=cites:W3034903023) openalex · 2026-10-18[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/acl-2020-shi-extracting-headless-mwes-dependency-parse/) of the 140673 papers whose bibliographies we read[Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/2020.acl-main/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.acl-main.775/)
- [PDF](https://aclanthology.org/2020.acl-main.775.pdf)
- [DOI 10.18653/v1/2020.acl-main.775](https://doi.org/10.18653/v1/2020.acl-main.775)
- [OpenAlex](https://openalex.org/W3034903023)

**Authors (2)**

1. [Tianze Shi](https://openalex.org/A5112027715) h 14 · 752 citations · 34 works
2. [Lillian Lee](https://openalex.org/A5076876084) [ORCID](https://orcid.org/0000-0003-4770-1712) · h 48 · 38156 citations · 189 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

A headless multi-word expression, such as "Wells Fargo" or "day after day", has no internal head, yet dependency schemes force a head on it. The paper compares two ways of finding these flat structures: a parser that adds arcs, and a tagger that marks the tokens with BIO labels. It then proposes a joint decoder that combines the parser score and the tagger score in one search. The data is an MWE-aware English dependency corpus plus six treebanks in five other languages with frequent flat structures. Tagging is more accurate than parsing for flat MWEs: 0.59 points of F1 ahead with contextual embeddings and 1.42 points with a bi-LSTM. The joint decoder adds 0.69 and 1.64 points, and most of the gain comes from sharing features between the parser and the tagger.

## Ideas

- I1. Flat-span tagger in `en/src/ptag.rs` style for names and dates that UD writes as `flat`: BIO tags from the perceptron, then convert the spans to `flat` arcs in `en/src/convert.rs`. Measure: flat-arc F1 on EWT test against the parser's own flat arcs, two identical runs.
- I2. A flat-span bonus inside the beam of `en/src/parse.rs`, the Mova version of the joint decoder: add the tagger score to the transition score when a span is flat. Measure: LAS and flat F1 on EWT test, with and without the bonus.
- I3. Check the UD dialect in `en/src/convert.rs`: count the headless spans that the converter gives an internal head, and measure the change on EWT test.

## For Mova

The chapter is [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) for flat names, and [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/) for the MWE view. The code is `en/src/parse.rs`, `en/src/ptag.rs` and `en/src/convert.rs`. The paper's corpus is not named in the source record; check its licence before any training use. Its numbers are for a neural parser, so Mova's numbers must come from EWT test.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.acl-main.775](https://doi.org/10.18653/v1/2020.acl-main.775)

```
@inproceedings{shi2020extracting,
  title = {{Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches}},
  author = {Shi, Tianze and Lee, Lillian},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.775},
  url = {https://doi.org/10.18653/v1/2020.acl-main.775}
}
```

- [← previous Semi-supervised Contextual Historical Text Normalization](https://md.mova.elamur.ai/free/en/docs/papers/acl-2020-makarov-semi-supervised-contextual-historical-text/)
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