---
title: "Automatic disambiguation of English puns — Miller 2015"
description: "Standard word sense disambiguation assumes every word carries one intended meaning. 49 references, cited by 30 papers here."
authors: ["Tristan Miller", "Iryna Gurevych"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/P15-1070/"
doi: "10.3115/v1/p15-1070"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/miller-2015-automatic-disambiguation-english-puns/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/miller-2015-automatic-disambiguation-english-puns/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2015](https://md.research.elamur.ai/free/en/papers/2015/page/3/) / Automatic disambiguation of English puns

# Automatic disambiguation of English puns

**Authors:** [Tristan Miller](https://md.research.elamur.ai/free/en/authors/tristan-miller/) and [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) · **Year:** 2015 · **Venue:** [Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2015](https://md.research.elamur.ai/free/en/venues/acl/2015/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P15-1070/) · [PDF](https://aclanthology.org/P15-1070.pdf) · [DOI 10.3115/v1/p15-1070](https://doi.org/10.3115/v1/p15-1070) · [OpenAlex](https://openalex.org/W2251092808)

**Citations:** 60 citations (OpenAlex, 2026-10-14) · [30](https://md.research.elamur.ai/free/en/papers/miller-2015-automatic-disambiguation-english-puns/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Standard word sense disambiguation assumes every word carries one intended meaning. Puns break that assumption because the writer wants two meanings active at once. Miller and Gurevych treat pun interpretation as a new task: given a word already known to be a pun, recover both of its senses. They limit the scope to homographic, monolexemic puns, which keeps the study about lexical semantics rather than phonology. They assembled a corpus of English puns whose senses were labeled by hand, and they adapted WSD components to the two-sense setting: metrics, baselines and knowledge-based Lesk-style algorithms. A key change is that the candidate pool includes every possible lemma and part of speech of the target, since a tagger would hide one of the readings. Directly ported Lesk variants did poorly. Adding pun-specific tie-breaking strategies brought recall up to the level of the supervised MFS baseline and pushed precision well above it. The work matters for sentiment analysis of advertising, digital humanities and assisted translation of wordplay.

## Key points
- The data set contains manually sense-annotated homographic puns. The per-POS analysis covers 1,607 targets: 999 with a single "pure" POS and 608 with mixed POS.
- Systems compared: SL, SEL, SLEL, and SEL extended with two backoffs, "POS" and "cluster". Baselines were random and MFS.
- SEL+POS reached the highest precision (21.21%) and raised coverage by 41% relative.
- SEL+cluster raised coverage by over 60% relative and gave the best recall (14.10%) and F1 (16.77%). Its recall was statistically indistinguishable from MFS (McNemar's test, 5% level).
- Verbs were hardest, with average candidate polysemy of 21.6. Mixed-POS targets had higher coverage but lower recall, even though their polysemy was lower than single-POS targets (13.2 vs 15.8).

## Interesting ideas
- The candidate sense pool is built from every lemma and POS the surface form could have, instead of trusting the tagger, because a pun like "moped" hides one reading behind the obvious analysis.
- When gloss overlap ties, the tie is broken with pun-specific signals: the two chosen senses should differ in part of speech or come from different sense clusters.
- Output is a set of two senses rather than one, so the word stays deliberately ambiguous instead of being forced into a single choice.
- Coverage, precision and recall are reported separately and broken down by POS, each next to a random baseline, with McNemar's test for significance. This shows where a system abstains versus where it is wrong.
- The task is deliberately narrowed to a controlled subset (homographic, monolexemic) to isolate one phenomenon before tackling harder cases like homophonic or imperfect puns.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): keep a lattice of alternative lemma/POS analyses for a token, so later stages can bring back a reading the tagger dropped when the frames don't fit.
- **I2** (frames): when detecting irony or wordplay, require the two competing readings to be genuinely distinct (different cluster or POS), so near-synonymous senses are not flagged as a double meaning.
- **I3** (world): let an entity or event in the text world carry two parallel readings, each with its own consequences, so answers can explain both sides of a joke.
- **I4** (learn): use per-category coverage/precision/recall with a random-baseline negative control and paired significance tests when evaluating repair rules or new parsers.
- **I5** (answer): build focused test suites that isolate a single reading difficulty before mixing it into full school-course or fable QA.

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

## Institutions

- [Technische Universität Darmstadt](https://md.research.elamur.ai/free/en/universities/technische-universitat-darmstadt/) — University, Darmstadt, Germany · 114 works · 36,326 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works · [📖 Lexical semantics & word embeddings](https://md.research.elamur.ai/free/en/topics/lexical-semantics/) · 65 works

## Cite

Cite as: Tristan Miller and Iryna Gurevych. 2015. Automatic disambiguation of English puns. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).

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

```bibtex
@inproceedings{miller2015automatic,
  title = {{Automatic disambiguation of English puns}},
  author = {Tristan Miller and Iryna Gurevych},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-1070},
  url = {https://doi.org/10.3115/v1/p15-1070}
}
```

- [← previous A Conventional Orthography for Algerian Arabic](https://md.research.elamur.ai/free/en/papers/saadane-2015-conventional-orthography-algerian-arabic/)
- [next → Frame-Semantic Role Labeling with Heterogeneous Annotations](https://md.research.elamur.ai/free/en/papers/kshirsagar-2015-frame-semantic-role-labeling-heterogeneous/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/page/3/)

## References

49 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Salvatore Attardo. 1994. Linguistic Theories of Humor. Mouton de Gruyter. [find](https://scholar.google.com/scholar?q=Salvatore+Attardo%2E+1994%2E+Linguistic+Theories+of+Humor%2E+Mouton+de+Gruyter%2E)
2. Pierpaolo Basile, Annalina Caputo, and Giovanni Semeraro. 2014. [An enhanced Lesk word sense disambiguation algorithm through a distributional semantic model](https://md.research.elamur.ai/free/en/papers/basile-2014-enhanced-lesk-word-sense-disambiguation/). In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pages 1591–1600.
3. Tristan A. Bekinschtein, Matthew H. Davis, Jennifer M. Rodd, and Adrian M. Owen. 2011. Why clowns taste funny: The relationship between humor and semantic ambiguity. The Journal of Neuroscience, 31(26):9665–9671, June. [find](https://scholar.google.com/scholar?q=Tristan+A%2E+Bekinschtein%2C+Matthew+H%2E+Davis%2C+Jennifer+M%2E+Rodd%2C+and+Adrian+M%2E+Owen%2E+2011%2E+Why+clowns+taste+funny%3A+The+relationship+between+humor+and+semantic+ambiguity%2E+The+Journal+of+Neuroscience%2C+31%2826)
4. Nancy D. Bell, Scott Crossley, and Christian F. Hempelmann. 2011. Wordplay in church marquees. Humor: International Journal of Humor Research, 24(2):187– 202, April. [find](https://scholar.google.com/scholar?q=Nancy+D%2E+Bell%2C+Scott+Crossley%2C+and+Christian+F%2E+Hempelmann%2E+2011%2E+Wordplay+in+church+marquees%2E+Humor%3A+International+Journal+of+Humor+Research%2C+24%282%29%3A187%E2%80%93+202%2C+April%2E)
5. Chiara Bucaria. 2004. Lexical and syntactic ambiguity as a source of humor: The case of newspaper headlines. Humor: International Journal of Humor Research, 17(3):279–309. [find](https://scholar.google.com/scholar?q=Chiara+Bucaria%2E+2004%2E+Lexical+and+syntactic+ambiguity+as+a+source+of+humor%3A+The+case+of+newspaper+headlines%2E+Humor%3A+International+Journal+of+Humor+Research%2C+17%283%29%3A279%E2%80%93309%2E)
6. Paul Buitelaar. 2000. Reducing lexical semantic complexity with systematic polysemous classes and underspecification. In Proceedings of the 2000 NAACL-ANLP Workshop on Syntactic and Semantic Complexity in Natural Language Processing Systems, volume 1, pages 14–19. [find](https://scholar.google.com/scholar?q=Paul+Buitelaar%2E+2000%2E+Reducing+lexical+semantic+complexity+with+systematic+polysemous+classes+and+underspecification%2E+In+Proceedings+of+the+2000+NAACL%2DANLP+Workshop+on+Syntactic+and+Semantic+Complexit)
7. Jonathan D. Culler, editor. 1988. On Puns: The Foundation of Letters. Basil Blackwell, Oxford. [find](https://scholar.google.com/scholar?q=Jonathan+D%2E+Culler%2C+editor%2E+1988%2E+On+Puns%3A+The+Foundation+of+Letters%2E+Basil+Blackwell%2C+Oxford%2E)
8. Dirk Delabastita, editor. 1997. Traductio: Essays on Punning and Translation. St. Jerome, Manchester. [find](https://scholar.google.com/scholar?q=Dirk+Delabastita%2C+editor%2E+1997%2E+Traductio%3A+Essays+on+Punning+and+Translation%2E+St%2E+Jerome%2C+Manchester%2E)
9. Christiane Fellbaum, editor. 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA. [find](https://scholar.google.com/scholar?q=Christiane+Fellbaum%2C+editor%2E+1998%2E+WordNet%3A+An+Electronic+Lexical+Database%2E+MIT+Press%2C+Cambridge%2C+MA%2E)
10. William Gale, Kenneth Ward Church, and David Yarowsky. 1992. Estimating upper and lower bounds on the performance of word-sense disambiguation programs. In Proceedings of the 30th Annual Meeting of the Association of Computational Linguistics (ACL 1992), pages 249–256. [find](https://scholar.google.com/scholar?q=William+Gale%2C+Kenneth+Ward+Church%2C+and+David+Yarowsky%2E+1992%2E+Estimating+upper+and+lower+bounds+on+the+performance+of+word%2Dsense+disambiguation+programs%2E+In+Proceedings+of+the+30th+Annual+Meeting+of+th)
11. Christian F. Hempelmann. 2003. Paronomasic Puns: Target Recoverability Towards Automatic Generation. Ph.D. thesis, Purdue University. [find](https://scholar.google.com/scholar?q=Christian+F%2E+Hempelmann%2E+2003%2E+Paronomasic+Puns%3A+Target+Recoverability+Towards+Automatic+Generation%2E+Ph%2ED%2E+thesis%2C+Purdue+University%2E)
12. Bryan Anthony Hong and Ethel Ong. 2009. Automatically extracting word relationships as templates for pun generation. In Proceedings of the 1st Workshop on Computational Approaches to Linguistic Creativity (CALC 2009), pages 24–31, June. [find](https://scholar.google.com/scholar?q=Bryan+Anthony+Hong+and+Ethel+Ong%2E+2009%2E+Automatically+extracting+word+relationships+as+templates+for+pun+generation%2E+In+Proceedings+of+the+1st+Workshop+on+Computational+Approaches+to+Linguistic+Creati)
13. David Jurgens and Ioannis Klapaftis. 2013. SemEval-2013 Task 13: Word sense induction for graded and non-graded senses. In Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval 2013), pages 290–299, June. [find](https://scholar.google.com/scholar?q=David+Jurgens+and+Ioannis+Klapaftis%2E+2013%2E+SemEval%2D2013+Task+13%3A+Word+sense+induction+for+graded+and+non%2Dgraded+senses%2E+In+Proceedings+of+the+7th+International+Workshop+on+Semantic+Evaluation+%28SemEval)
14. David Jurgens. 2014. An analysis of ambiguity in word sense annotations. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), pages 3006–3012, May. [find](https://scholar.google.com/scholar?q=David+Jurgens%2E+2014%2E+An+analysis+of+ambiguity+in+word+sense+annotations%2E+In+Proceedings+of+the+9th+International+Conference+on+Language+Resources+and+Evaluation+%28LREC+2014%29%2C+pages+3006%E2%80%933012%2C+May%2E)
15. Nora Kaplan and Teresa Lucas. 2001. Comprensión del humorismo en inglés: Estudio de las estrategias de inferencia utilizadas por estudiantes avanzados de inglés como lengua extranjera en la interpretación de los retruécanos en historietas cómicas en lengua inglesa. Anales de la Universidad Metropolitana, 1(2):245–258. [find](https://scholar.google.com/scholar?q=Nora+Kaplan+and+Teresa+Lucas%2E+2001%2E+Comprensio%CC%81n+del+humorismo+en+ingle%CC%81s%3A+Estudio+de+las+estrategias+de+inferencia+utilizadas+por+estudiantes+avanzados+de+ingle%CC%81s+como+lengua+extranjera+en+la+interpr)
16. Stefan Daniel Keller. 2009. The Development of Shakespeare’s Rhetoric: A Study of Nine Plays, volume 136 of Swiss Studies in English. Narr, Tübingen. [find](https://scholar.google.com/scholar?q=Stefan+Daniel+Keller%2E+2009%2E+The+Development+of+Shakespeare%E2%80%99s+Rhetoric%3A+A+Study+of+Nine+Plays%2C+volume+136+of+Swiss+Studies+in+English%2E+Narr%2C+Tu%CC%88bingen%2E)
17. Adam Kilgarriff and Joseph Rosenzweig. 2000. Framework and results for English SENSEVAL. Computers and the Humanities, 34:15–48. [find](https://scholar.google.com/scholar?q=Adam+Kilgarriff+and+Joseph+Rosenzweig%2E+2000%2E+Framework+and+results+for+English+SENSEVAL%2E+Computers+and+the+Humanities%2C+34%3A15%E2%80%9348%2E)
18. Klaus Krippendorff. 1980. Content Analysis: An Introduction to its Methodology. Sage, Beverly Hills, CA. [find](https://scholar.google.com/scholar?q=Klaus+Krippendorff%2E+1980%2E+Content+Analysis%3A+An+Introduction+to+its+Methodology%2E+Sage%2C+Beverly+Hills%2C+CA%2E)
19. Luuk Lagerwerf. 2002. Deliberate ambiguity in slogans: Recognition and appreciation. Document Design, 3(3):245–260. [find](https://scholar.google.com/scholar?q=Luuk+Lagerwerf%2E+2002%2E+Deliberate+ambiguity+in+slogans%3A+Recognition+and+appreciation%2E+Document+Design%2C+3%283%29%3A245%E2%80%93260%2E)
20. Michael Lesk. 1986. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from a ice cream cone. In Proceedings of the 5th Annual International Conference of Systems Documentation (SIGDOC 1986), pages 24–26. [find](https://scholar.google.com/scholar?q=Michael+Lesk%2E+1986%2E+Automatic+sense+disambiguation+using+machine+readable+dictionaries%3A+How+to+tell+a+pine+cone+from+a+ice+cream+cone%2E+In+Proceedings+of+the+5th+Annual+International+Conference+of+Syst)
21. Greg Lessard, Michael Levison, and Chris Venour. 2002. Cleverness versus funniness. In Proceedings of the 20th Twente Workshop on Language Technology, pages 137–145. [find](https://scholar.google.com/scholar?q=Greg+Lessard%2C+Michael+Levison%2C+and+Chris+Venour%2E+2002%2E+Cleverness+versus+funniness%2E+In+Proceedings+of+the+20th+Twente+Workshop+on+Language+Technology%2C+pages+137%E2%80%93145%2E)
22. Dekang Lin. 1998. [Automatic retrieval and clustering of similar words](https://md.research.elamur.ai/free/en/papers/lin-1998-automatic-retrieval-clustering-similar-words/). In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics (ACL 1998) and the 17th International Conference on Computational Linguistics (COLING 1998), volume 2, pages 768–774.
23. Teresa Lucas. 2004. Deciphering the Meaning of Puns in Learning English as a Second Language: A Study of Triadic Interaction. Ph.D. thesis, Florida State University. [find](https://scholar.google.com/scholar?q=Teresa+Lucas%2E+2004%2E+Deciphering+the+Meaning+of+Puns+in+Learning+English+as+a+Second+Language%3A+A+Study+of+Triadic+Interaction%2E+Ph%2ED%2E+thesis%2C+Florida+State+University%2E)
24. Peter J. Ludlow. 1996. Semantic Ambiguity and Underspecification (review). Computational Linguistics, 3(23):476–482. [find](https://scholar.google.com/scholar?q=Peter+J%2E+Ludlow%2E+1996%2E+Semantic+Ambiguity+and+Underspecification+%28review%29%2E+Computational+Linguistics%2C+3%2823%29%3A476%E2%80%93482%2E)
25. Michael Matuschek, Tristan Miller, and Iryna Gurevych. 2014. A language-independent sense clustering approach for enhanced WSD. In Proceedings of the 12th Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2014), pages 11–21, October. [find](https://scholar.google.com/scholar?q=Michael+Matuschek%2C+Tristan+Miller%2C+and+Iryna+Gurevych%2E+2014%2E+A+language%2Dindependent+sense+clustering+approach+for+enhanced+WSD%2E+In+Proceedings+of+the+12th+Konferenz+zur+Verarbeitung+natu%CC%88rlicher+Sprac)
26. Christian M. Meyer, Margot Mieskes, Christian Stab, and Iryna Gurevych. 2014. DKPro Agreement: An open-source Java library for measuring inter-rater agreement. In Proceedings of the 25th International Conference on Computational Linguistics (System Demonstrations) (COLING 2014), pages 105–109, August. [find](https://scholar.google.com/scholar?q=Christian+M%2E+Meyer%2C+Margot+Mieskes%2C+Christian+Stab%2C+and+Iryna+Gurevych%2E+2014%2E+DKPro+Agreement%3A+An+open%2Dsource+Java+library+for+measuring+inter%2Drater+agreement%2E+In+Proceedings+of+the+25th+International)
27. Rada Mihalcea and Carlo Strapparava. 2005. [Making computers laugh: Investigations in automatic humor recognition](https://md.research.elamur.ai/free/en/papers/mihalcea-2005-making-computers-laugh-investigations-automatic/). In Proceedings of the 11th Human Language Technology Conference and the 10th Conference on Empirical Methods in Natural Language Processing (HLT-EMNLP 2005), pages 531–538, October.
28. Rada Mihalcea and Carlo Strapparava. 2006. Learning to laugh (automatically): Computational models for humor recognition. Computational Intelligence, 22(2):126–142. [find](https://scholar.google.com/scholar?q=Rada+Mihalcea+and+Carlo+Strapparava%2E+2006%2E+Learning+to+laugh+%28automatically%29%3A+Computational+models+for+humor+recognition%2E+Computational+Intelligence%2C+22%282%29%3A126%E2%80%93142%2E)
29. Rada Mihalcea, Carlo Strapparava, and Stephen Pulman. 2010. Computational models for incongruity detection in humour. In Proceedings of the 11th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2010), volume 6008 of Lecture Notes in Computer Science, pages 364–374. Springer, March. [find](https://scholar.google.com/scholar?q=Rada+Mihalcea%2C+Carlo+Strapparava%2C+and+Stephen+Pulman%2E+2010%2E+Computational+models+for+incongruity+detection+in+humour%2E+In+Proceedings+of+the+11th+International+Conference+on+Computational+Linguistics+a)
30. Tristan Miller and Mladen Turković. 2015. Towards the automatic detection and identification of English puns. European Journal of Humour Research. To appear. [find](https://scholar.google.com/scholar?q=Tristan+Miller+and+Mladen+Turkovic%CC%81%2E+2015%2E+Towards+the+automatic+detection+and+identification+of+English+puns%2E+European+Journal+of+Humour+Research%2E+To+appear%2E)
31. Tristan Miller, Chris Biemann, Torsten Zesch, and Iryna Gurevych. 2012. Using distributional similarity for lexical expansion in knowledge-based word sense disambiguation. In Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), pages 1781–1796, December. [find](https://scholar.google.com/scholar?q=Tristan+Miller%2C+Chris+Biemann%2C+Torsten+Zesch%2C+and+Iryna+Gurevych%2E+2012%2E+Using+distributional+similarity+for+lexical+expansion+in+knowledge%2Dbased+word+sense+disambiguation%2E+In+Proceedings+of+the+24th+I)
32. Tristan Miller, Nicolai Erbs, Hans-Peter Zorn, Torsten Zesch, and Iryna Gurevych. 2013. DKPro WSD: A generalized UIMA-based framework for word sense disambiguation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (System Demonstrations) (ACL 2013), pages 37–42, August. [find](https://scholar.google.com/scholar?q=Tristan+Miller%2C+Nicolai+Erbs%2C+Hans%2DPeter+Zorn%2C+Torsten+Zesch%2C+and+Iryna+Gurevych%2E+2013%2E+DKPro+WSD%3A+A+generalized+UIMA%2Dbased+framework+for+word+sense+disambiguation%2E+In+Proceedings+of+the+51st+Annual+M)
33. Michel Monnot. 1982. Puns in advertising: Ambiguity as verbal aggression. Maledicta, 6:7–20. [find](https://scholar.google.com/scholar?q=Michel+Monnot%2E+1982%2E+Puns+in+advertising%3A+Ambiguity+as+verbal+aggression%2E+Maledicta%2C+6%3A7%E2%80%9320%2E)
34. Roberto Navigli, Kenneth C. Litkowski, and Orin Hargraves. 2007. [SemEval-2007 Task 07: Coarse-grained English All-words Task](https://md.research.elamur.ai/free/en/papers/navigli-2007-semeval-2007-task-07-coarse/). In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 30–35, June.
35. Martha Palmer, Christiane Fellbaum, Scott Cotton, Lauren Delfs, and Hoa Trang Dang. 2001. English tasks: All-words and verb lexical sample. In Proceedings of Senseval-2: 2nd International Workshop on Evaluating Word Sense Disambiguation Systems, pages 21–24, July. [find](https://scholar.google.com/scholar?q=Martha+Palmer%2C+Christiane+Fellbaum%2C+Scott+Cotton%2C+Lauren+Delfs%2C+and+Hoa+Trang+Dang%2E+2001%2E+English+tasks%3A+All%2Dwords+and+verb+lexical+sample%2E+In+Proceedings+of+Senseval%2D2%3A+2nd+International+Workshop+on+)
36. Martha Palmer, Hwee Tou Ng, and Hoa Trang Dang. 2006. Evaluation of WSD systems. In Eneko Agirre and Philip Edmonds, editors, Word Sense Disambiguation: Algorithms and Applications, volume 33 of Text, Speech, and Language Technology. Springer. [find](https://scholar.google.com/scholar?q=Martha+Palmer%2C+Hwee+Tou+Ng%2C+and+Hoa+Trang+Dang%2E+2006%2E+Evaluation+of+WSD+systems%2E+In+Eneko+Agirre+and+Philip+Edmonds%2C+editors%2C+Word+Sense+Disambiguation%3A+Algorithms+and+Applications%2C+volume+33+of+Text%2C)
37. Rebecca J. Passonneau, Nizar Habash, and Owen Rambow. 2006. Inter-annotator agreement on a multilingual semantic annotation task. In Proceedings of the 5th International Conference on Language Resources and Evaluations (LREC 2006), pages 1951–1956. [find](https://scholar.google.com/scholar?q=Rebecca+J%2E+Passonneau%2C+Nizar+Habash%2C+and+Owen+Rambow%2E+2006%2E+Inter%2Dannotator+agreement+on+a+multilingual+semantic+annotation+task%2E+In+Proceedings+of+the+5th+International+Conference+on+Language+Resourc)
38. Rebecca J. Passonneau. 2006. [Measuring agreement on set-valued items (MASI) for semantic and pragmatic annotation](https://md.research.elamur.ai/free/en/papers/passonneau-2006-measuring-agreement-set-valued-items/). In Proceedings of the 5th International Conference on Language Resources and Evaluations (LREC 2006), pages 831–836.
39. Simone Paolo Ponzetto and Roberto Navigli. 2010. Knowledge-rich word sense disambiguation rivaling supervised systems. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), pages 1522–1531. [find](https://scholar.google.com/scholar?q=Simone+Paolo+Ponzetto+and+Roberto+Navigli%2E+2010%2E+Knowledge%2Drich+word+sense+disambiguation+rivaling+supervised+systems%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+Association+for+Computational+Li)
40. Vitor Raskin. 1985. Semantic Mechanisms of Humor. D. Reidel, Dordrecht, the Netherlands. [find](https://scholar.google.com/scholar?q=Vitor+Raskin%2E+1985%2E+Semantic+Mechanisms+of+Humor%2E+D%2E+Reidel%2C+Dordrecht%2C+the+Netherlands%2E)
41. Walter Redfern. 1984. Puns. Basil Blackwell, Oxford. [find](https://scholar.google.com/scholar?q=Walter+Redfern%2E+1984%2E+Puns%2E+Basil+Blackwell%2C+Oxford%2E)
42. Thorsten Schröter. 2005. Shun the Pun, Rescue the Rhyme? The Dubbing and Subtitling of Language-Play in Film. Ph.D. thesis, Karlstad University. [find](https://scholar.google.com/scholar?q=Thorsten+Schro%CC%88ter%2E+2005%2E+Shun+the+Pun%2C+Rescue+the+Rhyme%3F+The+Dubbing+and+Subtitling+of+Language%2DPlay+in+Film%2E+Ph%2ED%2E+thesis%2C+Karlstad+University%2E)
43. Benjamin Snyder and Martha Palmer. 2004. The English all-words task. In Proceedings of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3), pages 41–43, July. [find](https://scholar.google.com/scholar?q=Benjamin+Snyder+and+Martha+Palmer%2E+2004%2E+The+English+all%2Dwords+task%2E+In+Proceedings+of+the+3rd+International+Workshop+on+the+Evaluation+of+Systems+for+the+Semantic+Analysis+of+Text+%28Senseval%2D3%29%2C+pages)
44. Julia M. Taylor and Lawrence J. Mazlack. 2004. Computationally recognizing wordplay in jokes. In Proceedings of the 26th Annual Conference of the Cognitive Science Society (CogSci 2004), pages 1315– 1320, August. [find](https://scholar.google.com/scholar?q=Julia+M%2E+Taylor+and+Lawrence+J%2E+Mazlack%2E+2004%2E+Computationally+recognizing+wordplay+in+jokes%2E+In+Proceedings+of+the+26th+Annual+Conference+of+the+Cognitive+Science+Society+%28CogSci+2004%29%2C+pages+1315%E2%80%93+1)
45. Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. 2003. [Feature-rich part-of-speech tagging with a cyclic dependency network](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/). In Proceedings of the 3rd Conference of the North American Chapter of the Association for Computational Linguistics and the 9th Human Language Technologies Conference (HLT-NAACL 2003), pages 252–259.
46. Alessandro Valitutti, Carlo Strapparava, and Oliviero Stock. 2008. Textual affect sensing for computational advertising. In Proceedings of the AAAI Spring Symposium on Creative Intelligent Systems, pages 117–122, March. [find](https://scholar.google.com/scholar?q=Alessandro+Valitutti%2C+Carlo+Strapparava%2C+and+Oliviero+Stock%2E+2008%2E+Textual+affect+sensing+for+computational+advertising%2E+In+Proceedings+of+the+AAAI+Spring+Symposium+on+Creative+Intelligent+Systems%2C+pa)
47. Chris Venour. 1999. The computational generation of a class of puns. Master’s thesis, Queen’s University, Kingston, ON. [find](https://scholar.google.com/scholar?q=Chris+Venour%2E+1999%2E+The+computational+generation+of+a+class+of+puns%2E+Master%E2%80%99s+thesis%2C+Queen%E2%80%99s+University%2C+Kingston%2C+ON%2E)
48. Toshihiko Yokogawa. 2002. Japanese pun analyzer using articulation similarities. In Proceedings of the 11th IEEE International Conference on Fuzzy Systems (FUZZ 2002), volume 2, pages 1114–1119, May. [find](https://scholar.google.com/scholar?q=Toshihiko+Yokogawa%2E+2002%2E+Japanese+pun+analyzer+using+articulation+similarities%2E+In+Proceedings+of+the+11th+IEEE+International+Conference+on+Fuzzy+Systems+%28FUZZ+2002%29%2C+volume+2%2C+pages+1114%E2%80%931119%2C+May%2E)
49. Arnold M. Zwicky and Elizabeth D. Zwicky. 1986. Imperfect puns, markedness, and phonological similarity: With fronds like these, who needs anemones? Folia Linguistica, 20(3–4):493–503. [find](https://scholar.google.com/scholar?q=Arnold+M%2E+Zwicky+and+Elizabeth+D%2E+Zwicky%2E+1986%2E+Imperfect+puns%2C+markedness%2C+and+phonological+similarity%3A+With+fronds+like+these%2C+who+needs+anemones%3F+Folia+Linguistica%2C+20%283%E2%80%934%29%3A493%E2%80%93503%2E)

*Updated 2 November 2026*