---
title: "Idiomatic Expression Identification using — Zeng 2021"
description: "The paper tackles idiomatic expression identification in English: deciding whether a sentence contains a potentially idiomatic expression (PIE) used…"
authors: ["Ziheng Zeng", "Suma Bhat"]
year: 2021
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2021.tacl-1.92/"
doi: "10.1162/tacl_a_00442"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/zeng-2021-idiomatic-expression-identification-semantic-compatibility/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/zeng-2021-idiomatic-expression-identification-semantic-compatibility/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2021](https://md.research.elamur.ai/free/papers/2021/page/2/) / Idiomatic Expression Identification using Semantic Compatibility

# Idiomatic Expression Identification using Semantic Compatibility

**Authors:** [Ziheng Zeng](https://md.research.elamur.ai/free/authors/ziheng-zeng/), [Suma Bhat](https://md.research.elamur.ai/free/authors/suma-bhat/) · **Year:** 2021 · **Venue:** Transactions of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.tacl-1.92/) · [PDF](https://aclanthology.org/2021.tacl-1.92.pdf) · [DOI 10.1162/tacl\_a\_00442](https://doi.org/10.1162/tacl_a_00442) · [OpenAlex](https://openalex.org/W3206846084)

**Citations:** 36 citations (openalex-snapshot · 2026-10-14) · [39](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tackles idiomatic expression identification in English: deciding whether a sentence contains a potentially idiomatic expression (PIE) used figuratively and marking its exact span. Earlier work usually assumed the PIE and its position were known, or covered only certain constructions such as verb-noun combinations. The authors present DISC, a multi-stage neural model that receives only the raw sentence. It uses word and POS information and returns either the idiom span or the \<CLS>/\<SEP> pair, which means the phrase is literal. The core idea is semantic compatibility: a phrase that fits its context poorly is likely figurative. The model tests this by fusing static and contextualized token representations through two attention flow layers, borrowed from machine reading comprehension. Evaluation used three large benchmark datasets, a strict exact-span criterion, and type-aware splits so that test idioms never appear in training. DISC reached state-of-the-art results and handled unseen idioms especially well. It could therefore serve as an idiom-aware preprocessing step for machine translation, paraphrasing and sentiment analysis.

## Key points
- **Model and resource:** DISC (iDentifier of Idiomatic expressions via Semantic Compatibility) was published in TACL vol. 9, 2021. The code is at github.com/zzeng13/DISC.
- **Architecture:** The first attention flow layer works on static embeddings and POS tags to find candidate idiomatic phrases. The second layer compares these with contextualized representations to check semantic compatibility and predict the span.
- **Evaluation protocol:** A prediction counts only if every token of the idiom is recovered exactly. Type-aware splits test how well the model generalizes to unseen PIEs.
- **Results on unseen idioms:** The abstract reports gains of 1.4% to 30.8% over competitive baselines on the largest dataset. The contributions list gives 1.4% to 11.1% over the best baseline.
- **Error analysis:** The authors manually reviewed 300 errors from the MAGPIE type-aware split.
  - 42.3% were "missing" idioms, where the model returned nothing.
  - About 43.4% were partly correct or reasonable alternatives (partial 29.3%, alternative idiom 9.7%, other figurative phrase 4.3%).
  - Some of these errors stem from datasets labeling at most one PIE per sentence.

*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](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

39 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 32 not yet.

- [No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language Models](https://md.research.elamur.ai/free/papers/cheng-2024-no-context-needed-contextual-quandary/)
- [Modelling Idiomatic Expressions in Abstract Meaning Representation](https://md.research.elamur.ai/free/papers/gareeva-2026-modelling-idiomatic-expressions-abstract-meaning/)
- [ID10M-JAM: Stress-Testing Idiom Identification Under Challenging Context](https://md.research.elamur.ai/free/papers/hashiloni-2026-id10m-jam-stress-testing-idiom/)
- [Semantics of Multiword Expressions in Transformer-Based Models: A Survey](https://md.research.elamur.ai/free/papers/miletic-2024-semantics-multiword-expressions-transformer-based/)
- [Are idioms surprising?](https://md.research.elamur.ai/free/papers/philipp-2023-are-idioms-surprising/)
- [MultiCoPIE: A Multilingual Corpus of Potentially Idiomatic Expressions for Cross-lingual PIE Disambiguation](https://md.research.elamur.ai/free/papers/sentsova-2025-multicopie-multilingual-corpus-potentially-idiomatic/)
- [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://md.research.elamur.ai/free/papers/zeng-2023-iekg-commonsense-knowledge-graph-idiomatic/)

## Cite

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

```bibtex
@article{zeng2021idiomatic,
  title = {{Idiomatic Expression Identification using Semantic Compatibility}},
  author = {Zeng, Ziheng and Bhat, Suma},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2021},
  doi = {10.1162/tacl\_a\_00442},
  url = {https://doi.org/10.1162/tacl_a_00442}
}
```

- [← previous Hurdles to Progress in Long-form Question Answering](https://md.research.elamur.ai/free/papers/krishna-2021-hurdles-progress-long-form-question/)
- [next → Improving Arabic Diacritization with Regularized Decoding and Adversarial Training](https://md.research.elamur.ai/free/papers/acl-2021-qin-improving-arabic-diacritization-regularized-decoding/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/papers/2021/page/2/)

## References

71 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Timothy Baldwin. 2005. Deep lexical acquisition of verb–particle constructions. Computer Speech & Language, 19(4):398–414. https://doi.org/10.1016/j.csl.2005.02.004 — [doi:10.1016/j.csl.2005.02.004](https://doi.org/10.1016/j.csl.2005.02.004) [↗](https://doi.org/10.1016/j.csl.2005.02.004)
2. Timothy Baldwin and Su Nam Kim. 2010. Multiword expressions. In Nitin Indurkhya and Fred J. Damerau, editors, Handbook of Natural Language Processing, Second Edition, pages 267–292. Chapman and Hall/CRC the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Paper… [find](https://scholar.google.com/scholar?q=Timothy+Baldwin+and+Su+Nam+Kim%2E+2010%2E+Multiword+expressions%2E+In+Nitin+Indurkhya+and+Fred+J%2E+Damerau%2C+editors%2C+Handbook+of+Natural+Language+Processing%2C+Second+Edition%2C+pages+267%E2%80%93292%2E+Chapman+and+Hall%2FC)
3. Rhys Biddle, Aditya Joshi, Shaowu Liu, Cecile Paris, and Guandong Xu. 2020. Leveraging sentiment distributions to distinguish figurative from literal health reports on Twitter. In Proceedings of The Web Conference 2020, pages 1217–1227. https://doi.org/10 .1145/3366423.3380198 [find](https://scholar.google.com/scholar?q=Rhys+Biddle%2C+Aditya+Joshi%2C+Shaowu+Liu%2C+Cecile+Paris%2C+and+Guandong+Xu%2E+2020%2E+Leveraging+sentiment+distributions+to+distinguish+figurative+from+literal+health+reports+on+Twitter%2E+In+Proceedings+of+The+W)
4. Philip Blunsom. 2007. Structured Classification for Multilingual Natural Language Processing. Ph.D. thesis, University of Melbourne. [find](https://scholar.google.com/scholar?q=Philip+Blunsom%2E+2007%2E+Structured+Classification+for+Multilingual+Natural+Language+Processing%2E+Ph%2ED%2E+thesis%2C+University+of+Melbourne%2E)
5. BNC Consortium. 2007. British national corpus, XML edition. Oxford Text Archive. [find](https://scholar.google.com/scholar?q=BNC+Consortium%2E+2007%2E+British+national+corpus%2C+XML+edition%2E+Oxford+Text+Archive%2E)
6. Samuel A. Bobrow and Susan M. Bell. 1973. On catching on to idiomatic expressions. Memory & Cognition, 1(3):343–346. https://doi.org/10.3758/BF03198118, PubMed: 24214567 — [doi:10.3758/bf03198118](https://doi.org/10.3758/bf03198118) [↗](https://doi.org/10.3758/BF03198118)
7. Mathieu Constant, Gülşen Eryiğit, Johanna Monti, Lonneke Van Der Plas, Carlos Ramisch, Michael Rosner, and Amalia Todirascu. 2017. Multiword expression processing: A survey. Computational Linguistics, 43(4):837–892. https://doi.org/10.1162/COLI a 00302 — [doi:10.1162/coli](https://doi.org/10.1162/coli) [↗](https://doi.org/10.1162/COLI)
8. Paul Cook, Afsaneh Fazly, and Suzanne Stevenson. 2008. The VNC-tokens dataset. In Proceedings of the LREC Workshop Towards a Shared Task for Multiword Expressions (MWE 2008), pages 19–22. [find](https://scholar.google.com/scholar?q=Paul+Cook%2C+Afsaneh+Fazly%2C+and+Suzanne+Stevenson%2E+2008%2E+The+VNC%2Dtokens+dataset%2E+In+Proceedings+of+the+LREC+Workshop+Towards+a+Shared+Task+for+Multiword+Expressions+%28MWE+2008%29%2C+pages+19%E2%80%9322%2E)
9. Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The WaCky wide web: A collection of very large linguistically processed web-crawled corpora. Language Resources and Evaluation, 43(3):209–226. https://doi.org/10.1007/s10579-009-9081-4 [↗](https://doi.org/10.1007/s10579-009-9081-4)
10. Silvio Cordeiro, Carlos Ramisch, Marco Idiart, and Aline Villavicencio. 2016. Predicting the compositionality of nominal compounds: Giving word embeddings a hard time. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1986–1997.… [find](https://scholar.google.com/scholar?q=Silvio+Cordeiro%2C+Carlos+Ramisch%2C+Marco+Idiart%2C+and+Aline+Villavicencio%2E+2016%2E+Predicting+the+compositionality+of+nominal+compounds%3A+Giving+word+embeddings+a+hard+time%2E+In+Proceedings+of+the+54th+Annua)
11. Beata Beigman Klebanov, Chee Wee (Ben) Leong, and Michael Flor. 2018. A corpus of non-native written English annotated for metaphor. In Proceedings of the 2018 Conference of the North American Chapter of Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of de… [find](https://scholar.google.com/scholar?q=Beata+Beigman+Klebanov%2C+Chee+Wee+%28Ben%29+Leong%2C+and+Michael+Flor%2E+2018%2E+A+corpus+of+non%2Dnative+written+English+annotated+for+metaphor%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Chapter)
12. Yue Dong, Zichao Li, Mehdi Rezagholizadeh, and Jackie Chi Kit Cheung. 2019. EditNTS: An neural programmer-interpreter model for sentence simplification through explicit editing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3393–3402, Florence, Ita… [find](https://scholar.google.com/scholar?q=Yue+Dong%2C+Zichao+Li%2C+Mehdi+Rezagholizadeh%2C+and+Jackie+Chi+Kit+Cheung%2E+2019%2E+EditNTS%3A+An+neural+programmer%2Dinterpreter+model+for+sentence+simplification+through+explicit+editing%2E+In+Proceedings+of+the+) [our card](https://md.research.elamur.ai/free/papers/dong-2019-editnts-neural-programmer-interpreter-model/)
13. Stefan Evert and Brigitte Krenn. 2001. Methods for the qualitative evaluation of lexical association measures. In Proceedings of the 39th annual meeting of the association for computational linguistics, pages 188–195. https://doi.org/10.3115/1073012.1073037 [↗](https://doi.org/10.3115/1073012.1073037)
14. Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2018. Examining the tip of the iceberg: A data set for idiom translation. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). [find](https://scholar.google.com/scholar?q=Marzieh+Fadaee%2C+Arianna+Bisazza%2C+and+Christof+Monz%2E+2018%2E+Examining+the+tip+of+the+iceberg%3A+A+data+set+for+idiom+translation%2E+In+Proceedings+of+the+Eleventh+International+Conference+on+Language+Resour)
15. Afsaneh Fazly, Paul Cook, and Suzanne Stevenson. 2009. Unsupervised type and token identification of idiomatic expressions. Computational Linguistics, 35(1):61–103. https://doi.org/10.1162/coli.08-010-R1-07 -048 [↗](https://doi.org/10.1162/coli.08-010-R1-07) [our card](https://md.research.elamur.ai/free/papers/fazly-2009-unsupervised-type-token-identification-idiomatic/)
16. Afsaneh Fazly and Suzanne Stevenson. 2006. Automatically constructing a lexicon of verb phrase idiomatic combinations. In 11th Conference of the European Chapter of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Afsaneh+Fazly+and+Suzanne+Stevenson%2E+2006%2E+Automatically+constructing+a+lexicon+of+verb+phrase+idiomatic+combinations%2E+In+11th+Conference+of+the+European+Chapter+of+the+Association+for+Computational+L) [our card](https://md.research.elamur.ai/free/papers/fazly-2006-automatically-constructing-lexicon-verb-phrase/)
17. Anna Feldman and Jing Peng. 2013. Automatic detection of idiomatic clauses. In International Conference on Intelligent Text Processing and Computational Linguistics, pages 435–446. Springer. https://doi.org/10.1007/978 -3-642-37247-6 35 — [doi:10.1007/978](https://doi.org/10.1007/978) [↗](https://doi.org/10.1007/978)
18. Katja Filippova, Enrique Alfonseca, Carlos A. Colmenares, Łukasz Kaiser, and Oriol Vinyals. 2015. Sentence compression by deletion with LSTMs. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 360–368. https://doi.org/10.18653/v1/D15-1042 [↗](https://doi.org/10.18653/v1/D15-1042)
19. Richard Fothergill and Timothy Baldwin. 2012. Combining resources for MWE-token classification. In Proceedings of the First Joint Conference on Lexical and Computational Semantics, *SEM 2012, June 7–8, 2012, Montréal, Canada, pages 100–104. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Richard+Fothergill+and+Timothy+Baldwin%2E+2012%2E+Combining+resources+for+MWE%2Dtoken+classification%2E+In+Proceedings+of+the+First+Joint+Conference+on+Lexical+and+Computational+Semantics%2C+%2ASEM+2012%2C+June+7%E2%80%938)
20. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 2013. PPDB: The paraphrase database. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 758–764. [find](https://scholar.google.com/scholar?q=Juri+Ganitkevitch%2C+Benjamin+Van+Durme%2C+and+Chris+Callison%2DBurch%2E+2013%2E+PPDB%3A+The+paraphrase+database%2E+In+Proceedings+of+the+2013+Conference+of+the+North+American+Chapter+of+the+Association+for+Computa) [our card](https://md.research.elamur.ai/free/papers/ganitkevitch-2013-ppdb-paraphrase-database/)
21. Hongyu Gong, Suma Bhat, and Pramod Viswanath. 2017. Geometry of compositionality. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31. [find](https://scholar.google.com/scholar?q=Hongyu+Gong%2C+Suma+Bhat%2C+and+Pramod+Viswanath%2E+2017%2E+Geometry+of+compositionality%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+Intelligence%2C+volume+31%2E)
22. Hongyu Gong, Kshitij Gupta, Akriti Jain, and Suma Bhat. 2020. IlliniMET: Illinois system for metaphor detection with contextual and linguistic information. In Proceedings of the Second Workshop on Figurative Language Processing, pages 146–153. https://doi.org/10.18653/v1/2020.figlang-1.21 [↗](https://doi.org/10.18653/v1/2020.figlang-1.21)
23. Spence Green, Marie-Catherine de Marneffe, and Christopher D. Manning. 2013. Parsing models for identifying multiword expressions. Computational Linguistics, 39(1):195–227. https://doi.org/10.1162/COLI a 00139 [↗](https://doi.org/10.1162/COLI)
24. Hessel Haagsma, Johan Bos, and Malvina Nissim. 2020. MAGPIE: A large corpus of potentially idiomatic expressions. In Proceedings of The 12th Language Resources and Evaluation Conference, pages 279–287. [find](https://scholar.google.com/scholar?q=Hessel+Haagsma%2C+Johan+Bos%2C+and+Malvina+Nissim%2E+2020%2E+MAGPIE%3A+A+large+corpus+of+potentially+idiomatic+expressions%2E+In+Proceedings+of+The+12th+Language+Resources+and+Evaluation+Conference%2C+pages+279%E2%80%93287) [our card](https://md.research.elamur.ai/free/papers/haagsma-2020-magpie-large-corpus-potentially-idiomatic/)
25. Chikara Hashimoto, Satoshi Sato, and Takehito Utsuro. 2006. Japanese idiom recognition: Drawing a line between literal and idiomatic meanings. In Proceedings of the COLING/ ACL 2006 Main Conference Poster Sessions, pages 353–360. https://doi.org/10 .3115/1273073.1273119 [find](https://scholar.google.com/scholar?q=Chikara+Hashimoto%2C+Satoshi+Sato%2C+and+Takehito+Utsuro%2E+2006%2E+Japanese+idiom+recognition%3A+Drawing+a+line+between+literal+and+idiomatic+meanings%2E+In+Proceedings+of+the+COLING%2F+ACL+2006+Main+Conference+Po)
26. Yuting Hu and Suzan Verberne. 2020. Named entity recognition for Chinese biomedical patents. In Proceedings of the 28th International Conference on Computational Linguistics, pages 627–637. [find](https://scholar.google.com/scholar?q=Yuting+Hu+and+Suzan+Verberne%2E+2020%2E+Named+entity+recognition+for+Chinese+biomedical+patents%2E+In+Proceedings+of+the+28th+International+Conference+on+Computational+Linguistics%2C+pages+627%E2%80%93637%2E)
27. Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. Clinical Orthopaedics and Related Research, abs/1508.01991. pressions with contextual embeddings. In Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons, pages 85–94. [find](https://scholar.google.com/scholar?q=Zhiheng+Huang%2C+Wei+Xu%2C+and+Kai+Yu%2E+2015%2E+Bidirectional+LSTM%2DCRF+models+for+sequence+tagging%2E+Clinical+Orthopaedics+and+Related+Research%2C+abs%2F1508%2E01991%2E+pressions+with+contextual+embeddings%2E+In+Procee) [our card](https://md.research.elamur.ai/free/papers/huang-2015-bidirectional-lstm-crf-models-sequence/)
28. Leon Jaeger. 1999. The Nature of Idioms: A Systematic Approach. Peter Lang Pub Incorporated. [find](https://scholar.google.com/scholar?q=Leon+Jaeger%2E+1999%2E+The+Nature+of+Idioms%3A+A+Systematic+Approach%2E+Peter+Lang+Pub+Incorporated%2E)
29. Changsheng Liu. 2019. Toward Robust and Efficient Interpretations of Idiomatic Expressions in Context. Ph.D. thesis, University of Pittsburgh. [find](https://scholar.google.com/scholar?q=Changsheng+Liu%2E+2019%2E+Toward+Robust+and+Efficient+Interpretations+of+Idiomatic+Expressions+in+Context%2E+Ph%2ED%2E+thesis%2C+University+of+Pittsburgh%2E)
30. Hyeju Jang, Seungwhan Moon, Yohan Jo, and Carolyn Rosé. 2015. Metaphor detection in discourse. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 384–392, Prague, Czech Republic. Association for Computational Linguistics. https://doi.org/10 .186… [find](https://scholar.google.com/scholar?q=Hyeju+Jang%2C+Seungwhan+Moon%2C+Yohan+Jo%2C+and+Carolyn+Rose%CC%81%2E+2015%2E+Metaphor+detection+in+discourse%2E+In+Proceedings+of+the+16th+Annual+Meeting+of+the+Special+Interest+Group+on+Discourse+and+Dialogue%2C+pages)
31. Changsheng Liu and Rebecca Hwa. 2017. Representations of context in recognizing the figurative and literal usages of idioms. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31. [find](https://scholar.google.com/scholar?q=Changsheng+Liu+and+Rebecca+Hwa%2E+2017%2E+Representations+of+context+in+recognizing+the+figurative+and+literal+usages+of+idioms%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+Intelligence%2C+volume+31)
32. Graham Katz and Eugenie Giesbrecht. 2006. Automatic identification of non-compositional multi-word expressions using latent semantic analysis. In Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties, pages 12–19. https://doi.org/10.3115/1613692 .1613… [↗](https://doi.org/10.3115/1613692) [our card](https://md.research.elamur.ai/free/papers/katz-2006-automatic-identification-non-compositional-multi/)
33. Jerrold J. Katz and Jerry A. Fodor. 1963. The structure of a semantic theory. Language, 39(2):170–210. https://doi.org/10.2307/411200 [↗](https://doi.org/10.2307/411200)
34. Ioannis Korkontzelos and Suresh Manandhar. 2010. Can recognising multiword expressions improve shallow parsing? In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 636–644. [find](https://scholar.google.com/scholar?q=Ioannis+Korkontzelos+and+Suresh+Manandhar%2E+2010%2E+Can+recognising+multiword+expressions+improve+shallow+parsing%3F+In+Human+Language+Technologies%3A+The+2010+Annual+Conference+of+the+North+American+Chapter)
35. Ioannis Korkontzelos, Torsten Zesch, Fabio Massimo Zanzotto, and Chris Biemann. 2013. Semeval-2013 task 5: Evaluating phrasal semantics. In Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemE… [find](https://scholar.google.com/scholar?q=Ioannis+Korkontzelos%2C+Torsten+Zesch%2C+Fabio+Massimo+Zanzotto%2C+and+Chris+Biemann%2E+2013%2E+Semeval%2D2013+task+5%3A+Evaluating+phrasal+semantics%2E+In+Second+Joint+Conference+on+Lexical+and+Computational+Semanti)
36. Tarun Kumar and Yashvardhan Sharma. 2020. Character aware models with similarity learning for metaphor detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 116–125. https://doi.org/10.18653/v1/2020.figlang-1.18 [↗](https://doi.org/10.18653/v1/2020.figlang-1.18)
37. Murathan Kurfalı and Robert Östling. 2020. Disambiguation of potentially idiomatic ex-Changsheng Liu and Rebecca Hwa. 2019. A generalized idiom usage recognition model based on semantic compatibility. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 6738–6745. http… — [doi:10.1609/aaai.v33i01.33016738](https://doi.org/10.1609/aaai.v33i01.33016738) [find](https://scholar.google.com/scholar?q=Murathan+Kurfal%C4%B1+and+Robert+O%CC%88stling%2E+2020%2E+Disambiguation+of+potentially+idiomatic+ex%2DChangsheng+Liu+and+Rebecca+Hwa%2E+2019%2E+A+generalized+idiom+usage+recognition+model+based+on+semantic+compatibility)
38. Pengfei Liu, Kaiyu Qian, Xipeng Qiu, and Xuan-Jing Huang. 2017. Idiom-aware compositional distributed semantics. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1204–1213. [find](https://scholar.google.com/scholar?q=Pengfei+Liu%2C+Kaiyu+Qian%2C+Xipeng+Qiu%2C+and+Xuan%2DJing+Huang%2E+2017%2E+Idiom%2Daware+compositional+distributed+semantics%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Language+Processi)
39. Edward Loper and Steven Bird. 2002. NLTK: The natural language toolkit. In Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics. Philadelphia: Association for Computational Linguistics. https://doi.org/10.3115 /11… [find](https://scholar.google.com/scholar?q=Edward+Loper+and+Steven+Bird%2E+2002%2E+NLTK%3A+The+natural+language+toolkit%2E+In+Proceedings+of+the+ACL+Workshop+on+Effective+Tools+and+Methodologies+for+Teaching+Natural+Language+Processing+and+Computation)
40. Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. 2019. Encode, tag, realize: High-precision text editing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Proces… [find](https://scholar.google.com/scholar?q=Eric+Malmi%2C+Sebastian+Krause%2C+Sascha+Rothe%2C+Daniil+Mirylenka%2C+and+Aliaksei+Severyn%2E+2019%2E+Encode%2C+tag%2C+realize%3A+High%2Dprecision+text+editing%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+) [our card](https://md.research.elamur.ai/free/papers/malmi-2019-encode-tag-realize-high-precision/)
41. Rui Mao, Chenghua Lin, and Frank Guerin. 2019. End-to-end sequential metaphor identification inspired by linguistic theories. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3888–3898. https://doi.org/10 .18653/v1/P19-1378 [find](https://scholar.google.com/scholar?q=Rui+Mao%2C+Chenghua+Lin%2C+and+Frank+Guerin%2E+2019%2E+End%2Dto%2Dend+sequential+metaphor+identification+inspired+by+linguistic+theories%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Computati)
42. Diana McCarthy, Sriram Venkatapathy, and Aravind Joshi. 2007. Detecting compositionality of verb-object combinations using selectional preferences. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL… [find](https://scholar.google.com/scholar?q=Diana+McCarthy%2C+Sriram+Venkatapathy%2C+and+Aravind+Joshi%2E+2007%2E+Detecting+compositionality+of+verb%2Dobject+combinations+using+selectional+preferences%2E+In+Proceedings+of+the+2007+Joint+Conference+on+Empir)
43. Rosamund Moon et al.. 1998. Fixed Expressions and Idioms in English: A Corpus-Based Approach. Oxford University Press. [find](https://scholar.google.com/scholar?q=Rosamund+Moon+et+al%2E%2E+1998%2E+Fixed+Expressions+and+Idioms+in+English%3A+A+Corpus%2DBased+Approach%2E+Oxford+University+Press%2E)
44. Alexis Nasr, Carlos Ramisch, José Deulofeu, and André Valli. 2015. Joint dependency parsing and multiword expression tokenization. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (V… [find](https://scholar.google.com/scholar?q=Alexis+Nasr%2C+Carlos+Ramisch%2C+Jose%CC%81+Deulofeu%2C+and+Andre%CC%81+Valli%2E+2015%2E+Joint+dependency+parsing+and+multiword+expression+tokenization%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Co)
45. Joakim Nivre and Jens Nilsson. 2004. Multiword units in syntactic parsing. Proceedings of Methodologies and Evaluation of Multiword Units in Real-World Applications (MEMURA). [find](https://scholar.google.com/scholar?q=Joakim+Nivre+and+Jens+Nilsson%2E+2004%2E+Multiword+units+in+syntactic+parsing%2E+Proceedings+of+Methodologies+and+Evaluation+of+Multiword+Units+in+Real%2DWorld+Applications+%28MEMURA%29%2E)
46. Darren Pearce. 2001. Synonymy in collocation extraction. In Proceedings of the Workshop on WordNet and Other Lexical Resources, Second Meeting of the North American Chapter of the Association for Computational Linguistics, pages 41–46. [find](https://scholar.google.com/scholar?q=Darren+Pearce%2E+2001%2E+Synonymy+in+collocation+extraction%2E+In+Proceedings+of+the+Workshop+on+WordNet+and+Other+Lexical+Resources%2C+Second+Meeting+of+the+North+American+Chapter+of+the+Association+for+Comp)
47. Jing Peng and Anna Feldman. 2016. Automatic idiom recognition with word embeddings. In Information Management and Big Data - Second Annual International Symposium, SIMBig 2015, Cusco, Peru, September 2–4, 2015, and Third Annual International Symposium, SIMBig 2016, Cusco, Peru, September 1–3, 2016,… — [doi:10.1007/978-3-319-55209-5_2](https://doi.org/10.1007/978-3-319-55209-5_2) [find](https://scholar.google.com/scholar?q=Jing+Peng+and+Anna+Feldman%2E+2016%2E+Automatic+idiom+recognition+with+word+embeddings%2E+In+Information+Management+and+Big+Data+%2D+Second+Annual+International+Symposium%2C+SIMBig+2015%2C+Cusco%2C+Peru%2C+September+)
48. Jing Peng, Anna Feldman, and Ekaterina Vylomova. 2014. Classifying idiomatic and literal expressions using topic models and intensity of emotions. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2019–2027. Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Jing+Peng%2C+Anna+Feldman%2C+and+Ekaterina+Vylomova%2E+2014%2E+Classifying+idiomatic+and+literal+expressions+using+topic+models+and+intensity+of+emotions%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Me)
49. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVE: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. https://doi.org/10 .3115/v1/D14-1162 [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+D%2E+Manning%2E+2014%2E+GloVE%3A+Global+vectors+for+word+representation%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Proc)
50. Siva Reddy, Diana McCarthy, and Suresh Manandhar. 2011. An empirical study on compositionality in compound nouns. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 210–218. Asian Federation of Natural Language Processing. [find](https://scholar.google.com/scholar?q=Siva+Reddy%2C+Diana+McCarthy%2C+and+Suresh+Manandhar%2E+2011%2E+An+empirical+study+on+compositionality+in+compound+nouns%2E+In+Proceedings+of+5th+International+Joint+Conference+on+Natural+Language+Processing%2C+p) [our card](https://md.research.elamur.ai/free/papers/reddy-2011-empirical-study-compositionality-compound-nouns/)
51. Ivan A. Sag, Timothy Baldwin, Francis Bond, Ann Copestake, and Dan Flickinger. 2002. Multiword expressions: A pain in the neck for NLP. In International Conference on Intelligent Text Processing and Computational Linguistics, pages 1–15. Springer. https://doi.org/10.1007/3-540-45715-1_1 [↗](https://doi.org/10.1007/3-540-45715-1_1) [our card](https://md.research.elamur.ai/free/papers/idioms-2002-sag-multiword-expressions-pain-neck-nlp/)
52. Giancarlo Salton, Robert Ross, and John Kelleher. 2014. An empirical study of the impact of idioms on phrase based statistical machine translation of English to Brazilian-Portuguese. In Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra), pages 36–41. Association for… [find](https://scholar.google.com/scholar?q=Giancarlo+Salton%2C+Robert+Ross%2C+and+John+Kelleher%2E+2014%2E+An+empirical+study+of+the+impact+of+idioms+on+phrase+based+statistical+machine+translation+of+English+to+Brazilian%2DPortuguese%2E+In+Proceedings+of)
53. Giancarlo Salton, Robert Ross, and John Kelleher. 2016. Idiom token classification using sentential distributed semantics. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 194–204. https://doi.org/10.18653/v1/P16-1019 [↗](https://doi.org/10.18653/v1/P16-1019) [our card](https://md.research.elamur.ai/free/papers/salton-2016-idiom-token-classification-sentential-distributed/)
54. Agata Savary, Carlos Ramisch, Silvio Ricardo Cordeiro, Federico Sangati, Veronika Vincze, Behrang Qasemi Zadeh, Marie Candito, Fabienne Cap, Voula Giouli, Ivelina Stoyanova, and Antoine Doucet. 2017. The PARSEME shared task on automatic identification of verbal multiword expressions. In The 13th Wor… [find](https://scholar.google.com/scholar?q=Agata+Savary%2C+Carlos+Ramisch%2C+Silvio+Ricardo+Cordeiro%2C+Federico+Sangati%2C+Veronika+Vincze%2C+Behrang+Qasemi+Zadeh%2C+Marie+Candito%2C+Fabienne+Cap%2C+Voula+Giouli%2C+Ivelina+Stoyanova%2C+and+Antoine+Doucet%2E+2017%2E+) [our card](https://md.research.elamur.ai/free/papers/savary-2017-parseme-shared-task-automatic-identification/)
55. Nathan Schneider and Noah A. Smith. 2015. A corpus and model integrating multiword expressions and supersenses. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1537–1547, Denver, Colorado. Associ… [find](https://scholar.google.com/scholar?q=Nathan+Schneider+and+Noah+A%2E+Smith%2E+2015%2E+A+corpus+and+model+integrating+multiword+expressions+and+supersenses%2E+In+Proceedings+of+the+2015+Conference+of+the+North+American+Chapter+of+the+Association+f) [our card](https://md.research.elamur.ai/free/papers/missing-2015-schneider-corpus-model-integrating-multiword-expressions/)
56. Patrick Schone and Dan Jurafsky. 2001. Is knowledge-free induction of multiword unit dictionary headwords a solved problem? In Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Patrick+Schone+and+Dan+Jurafsky%2E+2001%2E+Is+knowledge%2Dfree+induction+of+multiword+unit+dictionary+headwords+a+solved+problem%3F+In+Proceedings+of+the+2001+Conference+on+Empirical+Methods+in+Natural+Langua)
57. Mike Schuster and Kaisuke Nakajima. 2012. Japanese and Korean voice search. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5149–5152. IEEE. https://doi.org/10.1109/ICASSP.2012.6289079 — [doi:10.1109/icassp.2012.6289079](https://doi.org/10.1109/icassp.2012.6289079) [↗](https://doi.org/10.1109/ICASSP.2012.6289079)
58. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017. Bidirectional attention flow for machine comprehension. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings. [find](https://scholar.google.com/scholar?q=Minjoon+Seo%2C+Aniruddha+Kembhavi%2C+Ali+Farhadi%2C+and+Hannaneh+Hajishirzi%2E+2017%2E+Bidirectional+attention+flow+for+machine+comprehension%2E+In+5th+International+Conference+on+Learning+Representations%2C+ICLR+2) [our card](https://md.research.elamur.ai/free/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
59. Ekaterina Shutova, Lin Sun, and Anna Korhonen. 2010. Metaphor identification using verb and noun clustering. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pages 1002–1010. [find](https://scholar.google.com/scholar?q=Ekaterina+Shutova%2C+Lin+Sun%2C+and+Anna+Korhonen%2E+2010%2E+Metaphor+identification+using+verb+and+noun+clustering%2E+In+Proceedings+of+the+23rd+International+Conference+on+Computational+Linguistics+%28Coling+20)
60. Ekaterina Shutova, Simone Teufel, and Anna Korhonen. 2013. Statistical metaphor processing. Computational Linguistics, 39(2):301–353. https://doi.org/10.1162/COLI a 00124 [↗](https://doi.org/10.1162/COLI)
61. Caroline Sporleder and Linlin Li. 2009. Unsupervised recognition of literal and non-literal use of idiomatic expressions. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pages 754–762. https://doi.org/10.3115/1609067 .1609151 [↗](https://doi.org/10.3115/1609067) [our card](https://md.research.elamur.ai/free/papers/sporleder-2009-unsupervised-recognition-literal-non-literal/)
62. Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015. Highway networks. CoRR, abs/1505.00387. [find](https://scholar.google.com/scholar?q=Rupesh+Kumar+Srivastava%2C+Klaus+Greff%2C+and+Ju%CC%88rgen+Schmidhuber%2E+2015%2E+Highway+networks%2E+CoRR%2C+abs%2F1505%2E00387%2E) [our card](https://md.research.elamur.ai/free/papers/srivastava-2015-highway-networks/)
63. G. J. Steen, A. G. Dorst, J. B. Herrmann, A. A. Kaal, T. Krennmayr, and T. Pasma. 2010. A method for linguistic metaphor identification. From MIP to MIPVU. Converging Evidence in Language and Communication Research, number 14, John Benjamins. https://doi.org/10.1075/celcr.14 — [doi:10.1075/celcr.14](https://doi.org/10.1075/celcr.14) [↗](https://doi.org/10.1075/celcr.14)
64. Mark Stevenson and Yorick Wilks. 2001. The interaction of knowledge sources in word sense disambiguation. Computational Linguistics, 27(3):321–349. https://doi.org/10.1162/089120101317066104 [↗](https://doi.org/10.1162/089120101317066104)
65. Chuandong Su, Fumiyo Fukumoto, Xiaoxi Huang, Jiyi Li, Rongbo Wang, and Zhiqun Chen. 2020. DeepMet: A reading comprehension paradigm for token-level metaphor detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 30–39. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Chuandong+Su%2C+Fumiyo+Fukumoto%2C+Xiaoxi+Huang%2C+Jiyi+Li%2C+Rongbo+Wang%2C+and+Zhiqun+Chen%2E+2020%2E+DeepMet%3A+A+reading+comprehension+paradigm+for+token%2Dlevel+metaphor+detection%2E+In+Proceedings+of+the+Second+Wor)
66. Patrizia Tabossi, Rachele Fanari, and Kinou Wolf. 2008. Processing idiomatic expressions: Effects of semantic compositionality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(2):313. https://doi.org/10.1037/0278-7393.34.2.313, PubMed: 18315408 — [doi:10.1037/0278-7393.34.2.313](https://doi.org/10.1037/0278-7393.34.2.313) [↗](https://doi.org/10.1037/0278-7393.34.2.313)
67. Patrizia Tabossi, Rachele Fanari, and Kinou Wolf. 2009. Why are idioms recognized fast? Memory & Cognition, 37(4):529–540. https://doi.org/10.3758/MC.37.4.529, PubMed: 19460959 — [doi:10.3758/mc.37.4.529](https://doi.org/10.3758/mc.37.4.529) [↗](https://doi.org/10.3758/MC.37.4.529)
68. Shiva Taslimipoor, Omid Rohanian, Ruslan Mitkov, and Afsaneh Fazly. 2018. Identification of multiword expressions: A fresh look at modelling and evaluation. In Multiword Expressions at Length and in Depth: Extended Papers from the MWE 2017 Workshop, volume 2, page 299. Language Science Press. [find](https://scholar.google.com/scholar?q=Shiva+Taslimipoor%2C+Omid+Rohanian%2C+Ruslan+Mitkov%2C+and+Afsaneh+Fazly%2E+2018%2E+Identification+of+multiword+expressions%3A+A+fresh+look+at+modelling+and+evaluation%2E+In+Multiword+Expressions+at+Length+and+in+D)
69. Dag Westerståhl. 2002. On the compositionality of idioms. Proceedings of LLC8. CSLI Publications. [find](https://scholar.google.com/scholar?q=Dag+Westersta%CC%8Ahl%2E+2002%2E+On+the+compositionality+of+idioms%2E+Proceedings+of+LLC8%2E+CSLI+Publications%2E)
70. Yorick Wilks. 1975. A preferential, pattern-seeking, semantics for natural language inference. Artificial Intelligence, 6(1):53–74. https://doi.org/10.1016/0004-3702 (75)90016-8 — [doi:10.1016/0004-3702](https://doi.org/10.1016/0004-3702) [↗](https://doi.org/10.1016/0004-3702)
71. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lho… [find](https://scholar.google.com/scholar?q=Thomas+Wolf%2C+Lysandre+Debut%2C+Victor+Sanh%2C+Julien+Chaumond%2C+Clement+Delangue%2C+Anthony+Moi%2C+Pierric+Cistac%2C+Tim+Rault%2C+Re%CC%81mi+Louf%2C+Morgan+Funtowicz%2C+Joe+Davison%2C+Sam+Shleifer%2C+Patrick+von+Platen%2C+Clara+) [our card](https://md.research.elamur.ai/free/papers/wolf-2020-transformers-state-art-natural-language/)