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|title=What if the whole is greater than the sum of the parts? Modelling Complex (Multiword) Expressions (invited paper)
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==What if the whole is greater than the sum of the parts? Modelling Complex (Multiword) Expressions (invited paper)==
https://ceur-ws.org/Vol-2944/invited1.pdf
What if the whole is greater than the sum of the
parts? Modelling Complex (Multiword) Expressions
Aline Villavicencio
Department of Computer Science, University of Sheffield
Regent Court (DCS)
211 Portobello
Sheffield, S1 4DP, UK
Abstract
Multiword Expressions (MWEs) such as idioms (make ends meet), light verb constructions (give a sigh),
verb particle constructions (shake up) and noun compounds (loan shark), are an integral part of the
mental lexicon of native speakers often used to express complex ideas in a simple and conventionalised
way accepted by a given linguistic community. As they may display a wealth of idiosyncrasies, from
lexical, syntactic and semantic to statistical, they have represented a real challenge for natural language
processing. However, their accurate integration has the potential for improving the precision, natural-
ness and fluency of downstream tasks like text simplification. In this paper I discuss some advances in
the identification and modelling of MWEs, concentrating on techniques for identifying their degree of
idiomaticity and approximating their meaning. One of the challenges is that their interpretation often
needs more knowledge than can be gathered from their individual components and their combinations
to differentiate combinations whose meaning can be (partly) inferred from their parts (as apple juice:
juice made of apples) from those that cannot (as dark horse: an unknown candidate who unexpectedly
succeeds). In particular, the paper presents results obtained with the use of contextualised word repre-
sentation models, which have been successfully used for capturing different word usages, and therefore
could provide an attractive alternative for representing idiomaticity in language.
Keywords
Multiword Expressions, Idiomaticity, Lexical Simplification, Contextualised Word Embeddings
1. Introduction
Human language is a powerful means for communicating ideas, and concepts, requests and
desires, for transmitting folklore, tales, history and scientific knowledge, on both an individual
and a global scale. It is expressive allowing complex ideas to be transmitted both formally
and informally, in scientific and in colloquial settings; it is creative and dynamic incorporating
new concepts, words and usages; flexible and ambiguous allowing for irony, jokes, idioms and
metaphors (e.g. a blanket of snow, French kiss, rocket science). However, such a large, rich and
complex system may also challenge humans and create barriers for a clear understanding of the
message to be communicated. For instance, a lack of vocabulary or of a particular word usage
Proceedings of the First Workshop on Current Trends in Text Simplification (CTTS 2021), co-located with SEPLN 2021.
September 21st, 2021 (Online). Saggion, H., Štajner, S. and Ferrés, D. (Eds).
" a.villavicencio@sheffield.ac.uk (A. Villavicencio)
~ https://sites.google.com/view/alinev (A. Villavicencio)
0000-0002-3731-9168 (A. Villavicencio)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
1
may hinder the full understanding of a message. This is a common situation for children learning
a language [1], for adult native speakers learning a new domain, and often faced by second
language learners [2]. Health factors have also been linked with an impact in language abilities,
including in clinical conditions like aphasia [3] and dyslexia [4] and autism [5]. Socio-economic
conditions and lack of access to schooling may also result in low language abilities and even
illiteracy [6], which, in many contexts, may affect a large part of the population, as, for instance,
in Brazil, where according to social indicators, in 2012 6% of the Brazilian population aged 15 or
over were fully illiterate, among 27% who were functionally illiterate.1 Therefore, making texts
accessible for different target groups can be viewed as a crucial task not only for leading to a
better understanding, but also for ensuring a better quality of life.
In fact, there have been considerable research efforts into how to simplify texts [7, 8, 9, 10],
analysing their readability levels [11, 12, 13], creating resources [14, 15, 16], proposing new
techniques [17, 14] and analysing the suitability of evaluation frameworks [11, 15, 18]. These
have been applied for languages that in addition to English, include Spanish [19, 20], Portuguese
[6, 12, 21, 22], Italian [23] and Basque [24].
One approach to text simplification targets lexical substitutions [7], where words identified
as difficult are replaced by easier synonyms, according to a given simplicity measure, such as
word frequency, polysemy and length. However, in addition to considering words individually,
lexical substitution needs to take into account the occurrence of Multiword Expressions (MWEs)
in sentences, including compound nouns (like loan shark 2 ) and idioms (like let the cat out of the
bag 3 ) since their meanings may not be related to the meanings of their individual components,
and replacing one of their components in isolation may result in a nonsensical simplification
or even in loss of the original meaning (e.g. loan fish instead of loan shark). In particular,
MWEs are also challenging since they display many idiosyncrasies, from lexical to semantic
and statistical [25], and these may have an effect on the readability of a sentence, and are
particularly problematic for non-native speakers of a language since these idiosyncrasies are
often unpredictable, and may lead to markedness (e.g. strong coffee vs. ?powerful coffee).
Indeed, the positive impact of explicitly handling MWEs has been discussed for a number
of tasks and applications like machine translation [26], information retrieval [27] and parsing
[28], as discussed by Constant et al [29]. Although text simplification has received considerable
attention, studies focusing on MWEs are still scarce and there is a need for further investigation
into potential processing overheads involved in MWEs (if any) for different groups, and how
they can best be handled for text simplification. In what follows, I discuss some of the challenges
of MWEs and some techniques for detecting their idiomaticity.
2. Multiword Expressions and Idiomaticity
MWEs have been defined as word sequences, not necessarily adjacent, that are recurrent, act as
a single unit at some level of linguistic analysis [30], and whose interpretation crosses word
boundaries [31]. MWEs include, besides compound nouns (cheese knife, software engineering)
1
http://www.ipm.org.br/
2
Meaning a person who lends money at very high interest rates.
3
Meaning to reveal a secret.
2
and idioms (make ends meet, kick the bucket), verb-particle constructions (break down, carry on),
determinerless PPs (in hospital), collocations (strong coffee, heave baggage), support verbs (give
sigh, take shower), among others. The importance of MWEs for a simplification system can be
gauged from estimates about their frequency in language: for Biber et al. [32] they correspond
to between 30% and 45% of spoken English and 21% of academic prose, while for Jackendoff [33]
they have the same order of magnitude as the number of single words in a speaker’s mental
lexicon. They have also been found to have faster processing times compared to non-MWEs
(compositional novel sequences) [34, 35].
Their computational treatment involves steps like discovering their occurrence in sentences,
determining how idiomatic they are (as a type in general and as an MWE token in a particular
sentence), and approximating their meaning. For MWE discovery, methods for determining
if a given sequence of words forms an MWE or not have often been based on the statistical
markedness of their recurrence, using association and entropic measures calculated from corpus
counts [36], and on morphosyntactic patterns commonly associated to MWEs [37, 38]. These
have been applied to a variety of MWE types and languages and have been extensively discussed
[39, 36, 40].
For determining the meaning of a combination of words, one common strategy is to derive
it from the meanings of the parts. However, for idiomatic cases this approach will lead to
an unrelated meaning being derived. This difference between the meaning of the MWE and
the meanings of the components can be used as the basis for determining how idiomatic a
given MWE is [41, 42, 43, 44]. The assumption is that the closer the meaning of the MWE is
to the meaning of the components, the more compositional it is, and the more they differ, the
more idiomatic the MWE is. This approach has been used with word embeddings, using the
proximity in a multidimensional space between the embedding of the MWE and the embedding
of the components combined by operations like vector addition as semantic proximity. The
results obtained with static word embeddings like word2vec [45] and GloVe [46] for MWE type
idiomaticity detection for instance for Noun Compounds in languages like English, French and
Portuguese have been strongly correlated with human judgments about idiomaticity [44].
For token idiomaticity detection, the challenge is to decide if a potentially ambiguous MWE
is used literally or idiomatically in a given sentence (e.g. big fish literally as a large aquatic
animal or idiomatically as an important person). For this task, contextualised word embeddings
like BERT [47] and ELMo [48] may be an attractive alternative, as they seem to represent
words more accurately than static word embeddings like GloVe, being able to encode different
usages of a given word that appear to form clusters that seem related to its various senses
[49]. However, analyses of whether and to what extent idiomaticity in MWEs is accurately
incorporated by word representation models have recently reported mixed results. On the one
hand, in an analysis of different classifiers initialised with contextualised and non-contextualised
embeddings evaluated in five tasks related to lexical composition (including the literality of
NCs) contextualised models, especially BERT, obtained the best performance across all tasks
[50]. On other analyses, static models like word2vec had better performance than contextualised
models [51, 52]. These mixed results suggest that a controlled evaluation setup may be needed
to obtain comparable results across models and languages.
In recent work a set of probing tasks were defined to assess the representation of noun
compounds (NCs) in vector space models in two languages, English and French. The probes
3
took into account the NCs and their paraphrases in contexts that involved minimal modifications
and were used to compare the idiomatic and literal representations of a given NC [53]. The goal
was to assess if word embeddings with different levels of contextualisation would be sensitive to
changes in idiomaticity caused by different paraphrases in naturalistic sentences and in context
neutral sentences.
A second set of probes was defined to assess if the models were more sensitive to idiomaticity
at type or at token level [54], and whether contextualised models could reach the performance
obtained by static embeddings for type idiomaticity detection [44]. The results obtained in
these evaluations suggest that these models are still not sensitive enough to detect idiomaticity
accurately. Moreover, even if contextualised embeddings outperform static models in many
tasks, they still have lower performance for idiomaticity detection. Further analyses that take
into account more fine-grained sense distinctions for MWEs, allowing for polysemy in literal
and in idiomatic senses, have revealed that fine-tuning improves performance, both for MWE
discovery in sentences and for sense identification [55]. This analysis is part of SEMEVAL 2022
Task4 , and includes datasets for English, Portuguese and Galician.
For downstream tasks like text simplification, these results mean that systems that adopt
state-of-the-art pre-trained models as they are, will lack accuracy when faced with MWEs,
interpreting an idiomatic MWE like eager beaver in a sentence such as When she first started
working she was a real eager beaver. as more similar to anxious castor than to its actual synonym
of hardworking person.
3. Conclusions
In this paper I discussed MWEs and some of the challenges they pose for tasks like text
simplification. In particular I concentrated on recent work on idiomaticity detection using
state-of-the-art language models. The results obtained suggest that there are still advances to
be made for more accurate representation of MWE idiomaticity. However, given the prevalence
of MWE in natural languages, and their role in both general and technical domains, approaches
for text simplification, especially lexical simplification, need to take them into account for more
precise and understandable results. Moreover, additional research on the impact of MWEs
for different groups of speakers can shed light into any additional complexity, given their
advantageous processing for native speakers, and their often opaque meaning for non-native
speakers.
Acknowledgments
I want to thank the many co-authors who have collaborated in this work, including Marco Idiart,
Carlos Ramisch, Carolina Scarton, Harish Tayyar Madabushi, Tiago Vieira, Marcos Garcia,
Rodrigo Wilkens, Leonardo Zilio, Renata Ramisch and Silvio Cordeiro (in no particular order).
This research has been partly funded by EPSRC project MIA.
4
https://sites.google.com/view/semeval2022task2-idiomaticity
4
References
[1] J. De Belder, M.-F. Moens, Text simplification for children, ACM; New York, 2010, pp.
19ś26.
[2] G. H. Paetzold, Lexical simplification for non-native english speakers, 2016.
[3] J. A. Carroll, G. Minnen, D. Pearce, Y. Canning, S. Devlin, J. Tait, Simplifying text for
language-impaired readers, in: EACL 1999, 9th Conference of the European Chapter
of the Association for Computational Linguistics, June 8-12, 1999, University of Bergen,
Bergen, Norway, The Association for Computer Linguistics, 1999, pp. 269ś270. URL:
https://aclanthology.org/E99-1042/.
[4] L. Rello, R. Baeza-Yates, The effect of font type on screen readability by people with dyslexia,
ACM Trans. Access. Comput. 8 (2016) 15:1ś15:33. URL: https://doi.org/10.1145/2897736.
doi:10.1145/2897736.
[5] R. Evans, C. Orasan, I. Dornescu, An evaluation of syntactic simplification rules for
people with autism, in: S. Williams, A. Siddharthan, A. Nenkova (Eds.), Proceedings
of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader
Populations, PITR@EACL 2014, Gothenburg, Sweden, April 27, 2014, Association for
Computational Linguistics, 2014, pp. 131ś140. URL: https://doi.org/10.3115/v1/W14-1215.
doi:10.3115/v1/W14-1215.
[6] S. Aluísio, C. Gasperin, Fostering digital inclusion and accessibility: The PorSimples project
for simplification of Portuguese texts, in: Proceedings of the NAACL HLT 2010 Young
Investigators Workshop on Computational Approaches to Languages of the Americas,
Association for Computational Linguistics, Los Angeles, California, 2010, pp. 46ś53. URL:
https://aclanthology.org/W10-1607.
[7] A. Siddharthan, An architecture for a text simplification system, in: 2002 Language Engi-
neering Conference (LEC 2002), 13-15 December 2002, Hyderabad, India, IEEE Computer
Society, 2002, p. 64. URL: https://doi.org/10.1109/LEC.2002.1182292. doi:10.1109/LEC.
2002.1182292.
[8] M. Shardlow, A comparison of techniques to automatically identify complex words, in: 51st
Annual Meeting of the Association for Computational Linguistics, ACL 2013, Proceedings
of the Student Research Workshop, 4-9 August 2013, Sofia, Bulgaria, The Association for
Computer Linguistics, 2013, pp. 103ś109. URL: https://aclanthology.org/P13-3015/.
[9] S. Štajner, H. Saggion, Data-driven text simplification, in: Proceedings of the 27th
International Conference on Computational Linguistics: Tutorial Abstracts, Association
for Computational Linguistics, Santa Fe, New Mexico, USA, 2018, pp. 19ś23. URL: https:
//aclanthology.org/C18-3005.
[10] F. Alva-Manchego, C. Scarton, L. Specia, Data-driven sentence simplification: Survey and
benchmark, Computational Linguistics 46 (2020) 135ś187. URL: https://aclanthology.org/
2020.cl-1.4. doi:10.1162/coli_a_00370.
[11] S. Štajner, R. Mitkov, H. Saggion, One step closer to automatic evaluation of text sim-
plification systems, in: Proceedings of the 3rd Workshop on Predicting and Improving
Text Readability for Target Reader Populations (PITR), Association for Computational
Linguistics, Gothenburg, Sweden, 2014, pp. 1ś10. URL: https://aclanthology.org/W14-1201.
doi:10.3115/v1/W14-1201.
5
[12] J. A. Wagner Filho, R. Wilkens, A. Villavicencio, Automatic construction of large readability
corpora, in: Proceedings of the Workshop on Computational Linguistics for Linguistic
Complexity (CL4LC), The COLING 2016 Organizing Committee, Osaka, Japan, 2016, pp.
164ś173. URL: https://aclanthology.org/W16-4119.
[13] M. Maddela, W. Xu, A word-complexity lexicon and a neural readability ranking model
for lexical simplification, in: Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing, Association for Computational Linguistics, Brussels,
Belgium, 2018, pp. 3749ś3760. URL: https://aclanthology.org/D18-1410. doi:10.18653/
v1/D18-1410.
[14] W. Coster, D. Kauchak, Simple english wikipedia: A new text simplification task, in:
The 49th Annual Meeting of the Association for Computational Linguistics: Human
Language Technologies, Proceedings of the Conference, 19-24 June, 2011, Portland, Oregon,
USA - Short Papers, The Association for Computer Linguistics, 2011, pp. 665ś669. URL:
https://aclanthology.org/P11-2117/.
[15] F. Alva-Manchego, L. Martin, A. Bordes, C. Scarton, B. Sagot, L. Specia, ASSET: A dataset for
tuning and evaluation of sentence simplification models with multiple rewriting transfor-
mations, in: Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics, Association for Computational Linguistics, Online, 2020, pp. 4668ś4679. URL:
https://aclanthology.org/2020.acl-main.424. doi:10.18653/v1/2020.acl-main.424.
[16] G. Paetzold, L. Specia, SemEval 2016 task 11: Complex word identification, in: Proceedings
of the 10th International Workshop on Semantic Evaluation (SemEval-2016), Association
for Computational Linguistics, San Diego, California, 2016, pp. 560ś569. URL: https://
aclanthology.org/S16-1085. doi:10.18653/v1/S16-1085.
[17] K. Woodsend, M. Lapata, Wikisimple: Automatic simplification of wikipedia articles, in:
W. Burgard, D. Roth (Eds.), Proceedings of the Twenty-Fifth AAAI Conference on Artificial
Intelligence, AAAI 2011, San Francisco, California, USA, August 7-11, 2011, AAAI Press,
2011. URL: http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3505.
[18] F. Alva-Manchego, C. Scarton, L. Specia, The (Un)Suitability of Automatic
Evaluation Metrics for Text Simplification, Computational Linguistics (2021).
arXiv:https://direct.mit.edu/coli/article/doi/10.1162/coli𝑎0 0418/106930/𝑇 ℎ𝑒−
𝑈 𝑛 − 𝑆𝑢𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦 − 𝑜𝑓 − 𝐴𝑢𝑡𝑜𝑚𝑎𝑡𝑖𝑐 − 𝐸𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛 − 𝑀 𝑒𝑡𝑟𝑖𝑐𝑠.
[19] S. Štajner, I. Calixto, H. Saggion, Automatic text simplification for Spanish: Compara-
tive evaluation of various simplification strategies, in: Proceedings of the International
Conference Recent Advances in Natural Language Processing, INCOMA Ltd. Shoumen,
BULGARIA, Hissar, Bulgaria, 2015, pp. 618ś626. URL: https://aclanthology.org/R15-1080.
[20] H. Saggion, S. Štajner, S. Bott, S. Mille, L. Rello, B. Drndarevic, Making it simplext:
Implementation and evaluation of a text simplification system for spanish, ACM Trans.
Access. Comput. 6 (2015). URL: https://doi.org/10.1145/2738046. doi:10.1145/2738046.
[21] N. Hartmann, G. H. Paetzold, S. Aluísio, SIMPLEX-PB 2.0: A reliable dataset for lexical
simplification in Brazilian Portuguese, in: Proceedings of the The Fourth Widening Natural
Language Processing Workshop, Association for Computational Linguistics, Seattle, USA,
2020, pp. 18ś22. URL: https://aclanthology.org/2020.winlp-1.6. doi:10.18653/v1/2020.
winlp-1.6.
[22] S. Evaldo Leal, J. M. Munguba Vieira, E. dos Santos Rodrigues, E. Nogueira Teixeira, S. Aluí-
6
sio, Using eye-tracking data to predict the readability of Brazilian Portuguese sentences in
single-task, multi-task and sequential transfer learning approaches, in: Proceedings of
the 28th International Conference on Computational Linguistics, International Committee
on Computational Linguistics, Barcelona, Spain (Online), 2020, pp. 5821ś5831. URL: https:
//aclanthology.org/2020.coling-main.512. doi:10.18653/v1/2020.coling-main.512.
[23] G. Barlacchi, S. Tonelli, ERNESTA: A sentence simplification tool for children’s sto-
ries in italian, in: A. F. Gelbukh (Ed.), Computational Linguistics and Intelligent Text
Processing - 14th International Conference, CICLing 2013, Samos, Greece, March 24-30,
2013, Proceedings, Part II, volume 7817 of Lecture Notes in Computer Science, Springer,
2013, pp. 476ś487. URL: https://doi.org/10.1007/978-3-642-37256-8_39. doi:10.1007/
978-3-642-37256-8\_39.
[24] I. Gonzalez-Dios, M. J. Aranzabe, A. D. de Ilarraza, The corpus of basque simplified
texts (CBST), Lang. Resour. Evaluation 52 (2018) 217ś247. URL: https://doi.org/10.1007/
s10579-017-9407-6. doi:10.1007/s10579-017-9407-6.
[25] T. Baldwin, S. N. Kim, Multiword expressions, in: N. Indurkhya, F. J. Damerau (Eds.),
Handbook of Natural Language Processing, Second Edition, Chapman and Hall/CRC, 2010,
pp. 267ś292. URL: http://www.crcnetbase.com/doi/abs/10.1201/9781420085938-c12.
[26] M. Carpuat, M. T. Diab, Task-based evaluation of multiword expressions: a pilot study
in statistical machine translation, in: Human Language Technologies: Conference of
the North American Chapter of the Association of Computational Linguistics, Proceed-
ings, June 2-4, 2010, Los Angeles, California, USA, The Association for Computational
Linguistics, 2010, pp. 242ś245. URL: https://aclanthology.org/N10-1029/.
[27] O. Acosta, A. Villavicencio, V. Moreira, Identification and treatment of multiword
expressions applied to information retrieval, in: Proceedings of the Workshop on
Multiword Expressions: from Parsing and Generation to the Real World, Association
for Computational Linguistics, Portland, Oregon, USA, 2011, pp. 101ś109. URL: https:
//aclanthology.org/W11-0815.
[28] M. Constant, J. Nivre, A transition-based system for joint lexical and syntactic analysis, in:
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics
(Volume 1: Long Papers), Association for Computational Linguistics, Berlin, Germany, 2016,
pp. 161ś171. URL: https://aclanthology.org/P16-1016. doi:10.18653/v1/P16-1016.
[29] M. Constant, G. Eryiǧit, J. Monti, L. van der Plas, C. Ramisch, M. Rosner, A. Todirascu,
Survey: Multiword expression processing: A Survey, Computational Linguistics 43 (2017)
837ś892. URL: https://aclanthology.org/J17-4005. doi:10.1162/COLI_a_00302.
[30] N. Calzolari, C. J. Fillmore, R. Grishman, N. Ide, A. Lenci, C. MacLeod, A. Zampolli, Towards
best practice for multiword expressions in computational lexicons., in: Proceedings of
the Third International Conference on Language Resources and Evaluation (LREC-2002),
European Language Resources Association (ELRA), Las Palmas, Canary Islands - Spain,
2002. URL: http://www.lrec-conf.org/proceedings/lrec2002/pdf/259.pdf, aCL Anthology
Identifier: L02-1259.
[31] I. A. Sag, T. Baldwin, F. Bond, A. A. Copestake, D. Flickinger, Multiword expressions: A pain
in the neck for NLP, in: A. F. Gelbukh (Ed.), Computational Linguistics and Intelligent Text
Processing, Third International Conference, CICLing 2002, Mexico City, Mexico, February
17-23, 2002, Proceedings, volume 2276 of Lecture Notes in Computer Science, Springer, 2002,
7
pp. 1ś15. URL: https://doi.org/10.1007/3-540-45715-1_1. doi:10.1007/3-540-45715-1\
_1.
[32] D. Biber, S. Johansson, G. Leech, S. Conrad, E. Finegan, Longman Grammar of Spoken and
Written English, Pearson Education, 1999.
[33] R. Jackendoff, The Architecture of the Language Faculty, volume 28, MIT Press, 1997.
[34] M. H. Christiansen, I. Arnon, E. Lieven, A. Wray, Multiword sequences as building
blocks for language: Insights into first and second language learning, in: M. Knauff,
M. Pauen, N. Sebanz, I. Wachsmuth (Eds.), Proceedings of the 35th Annual Meeting of
the Cognitive Science Society, CogSci 2013, Berlin, Germany, July 31 - August 3, 2013,
cognitivesciencesociety.org, 2013. URL: https://mindmodeling.org/cogsci2013/papers/0029/
index.html.
[35] A. Siyanova-Chanturia, K. Conklin, N. Schmitt, Adding more fuel to the fire: An eye-
tracking study of idiom processing by native and non-native speakers, Second Language
Research 27 (2011) 251ś272. URL: https://doi.org/10.1177/0267658310382068. doi:10.1177/
0267658310382068. arXiv:https://doi.org/10.1177/0267658310382068.
[36] P. Pecina, Lexical association measures and collocation extraction, Lang. Resour. Evalu-
ation 44 (2010) 137ś158. URL: https://doi.org/10.1007/s10579-009-9101-4. doi:10.1007/
s10579-009-9101-4.
[37] J. S. Justeson, S. M. Katz, Technical terminology: some linguistic properties and an
algorithm for identification in text, Nat. Lang. Eng. 1 (1995) 9ś27. URL: https://doi.org/10.
1017/S1351324900000048. doi:10.1017/S1351324900000048.
[38] T. Baldwin, Deep lexical acquisition of verb-particle constructions, Comput. Speech
Lang. 19 (2005) 398ś414. URL: https://doi.org/10.1016/j.csl.2005.02.004. doi:10.1016/j.
csl.2005.02.004.
[39] C. Ramisch, Multiword Expressions Acquisition - A Generic and Open Framework, Theory
and Applications of Natural Language Processing, Springer, 2015. URL: https://doi.org/10.
1007/978-3-319-09207-2. doi:10.1007/978-3-319-09207-2.
[40] N. Schneider, D. Hovy, A. Johannsen, M. Carpuat, SemEval-2016 task 10: Detecting minimal
semantic units and their meanings (DiMSUM), in: Proceedings of the 10th International
Workshop on Semantic Evaluation (SemEval-2016), Association for Computational Lin-
guistics, San Diego, California, 2016, pp. 546ś559. URL: https://aclanthology.org/S16-1084.
doi:10.18653/v1/S16-1084.
[41] J. Mitchell, M. Lapata, Composition in distributional models of semantics, Cogn. Sci. 34
(2010) 1388ś1429. URL: https://doi.org/10.1111/j.1551-6709.2010.01106.x. doi:10.1111/j.
1551-6709.2010.01106.x.
[42] S. Reddy, D. McCarthy, S. Manandhar, An empirical study on compositionality in com-
pound nouns, in: Fifth International Joint Conference on Natural Language Processing,
IJCNLP 2011, Chiang Mai, Thailand, November 8-13, 2011, The Association for Computer
Linguistics, 2011, pp. 210ś218. URL: https://aclanthology.org/I11-1024/.
[43] M. Farahmand, J. Henderson, Modeling the non-substitutability of multiword expressions
with distributional semantics and a log-linear model, in: Proceedings of the 12th Workshop
on Multiword Expressions, Association for Computational Linguistics, Berlin, Germany,
2016, pp. 61ś66. URL: https://www.aclweb.org/anthology/W16-1809. doi:10.18653/v1/
W16-1809.
8
[44] S. Cordeiro, A. Villavicencio, M. Idiart, C. Ramisch, Unsupervised compositionality
prediction of nominal compounds, Computational Linguistics 45 (2019) 1ś57. URL:
https://www.aclweb.org/anthology/J19-1001. doi:10.1162/coli_a_00341.
[45] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean, Distributed representations of
words and phrases and their compositionality, CoRR abs/1310.4546 (2013). URL: http:
//arxiv.org/abs/1310.4546. arXiv:1310.4546.
[46] J. Pennington, R. Socher, C. D. Manning, Glove: Global vectors for word representation,
in: A. Moschitti, B. Pang, W. Daelemans (Eds.), Proceedings of the 2014 Conference on
Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014,
Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, ACL, 2014, pp.
1532ś1543. URL: https://doi.org/10.3115/v1/d14-1162. doi:10.3115/v1/d14-1162.
[47] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidirectional
transformers for language understanding, in: Proceedings of the 2019 Conference of
the North American Chapter of the Association for Computational Linguistics: Human
Language Technologies, Volume 1 (Long and Short Papers), Association for Computational
Linguistics, Minneapolis, Minnesota, 2019, pp. 4171ś4186. URL: https://aclanthology.org/
N19-1423. doi:10.18653/v1/N19-1423.
[48] M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer, Deep
contextualized word representations, in: Proceedings of the 2018 Conference of the North
American Chapter of the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers), Association for Computational Linguistics, New Or-
leans, Louisiana, 2018, pp. 2227ś2237. URL: https://www.aclweb.org/anthology/N18-1202.
doi:10.18653/v1/N18-1202.
[49] T. Schuster, O. Ram, R. Barzilay, A. Globerson, Cross-lingual alignment of contextual word
embeddings, with applications to zero-shot dependency parsing, in: Proceedings of the
2019 Conference of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association
for Computational Linguistics, Minneapolis, Minnesota, 2019, pp. 1599ś1613. URL: https:
//aclanthology.org/N19-1162. doi:10.18653/v1/N19-1162.
[50] V. Shwartz, I. Dagan, Still a pain in the neck: Evaluating text representations on lexical
composition, Transactions of the Association for Computational Linguistics 7 (2019)
403ś419. URL: https://aclanthology.org/Q19-1027. doi:10.1162/tacl_a_00277.
[51] N. Nandakumar, T. Baldwin, B. Salehi, How Well Do Embedding Models Capture Non-
compositionality? A View from Multiword Expressions, in: Proceedings of the 3rd
Workshop on Evaluating Vector Space Representations for NLP, Association for Compu-
tational Linguistics, Minneapolis, USA, 2019, pp. 27ś34. URL: https://www.aclweb.org/
anthology/W19-2004. doi:10.18653/v1/W19-2004.
[52] M. King, P. Cook, Leveraging distributed representations and lexico-syntactic fixedness
for token-level prediction of the idiomaticity of english verb-noun combinations, in:
I. Gurevych, Y. Miyao (Eds.), Proceedings of the 56th Annual Meeting of the Association
for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume
2: Short Papers, Association for Computational Linguistics, 2018, pp. 345ś350. URL: https:
//www.aclweb.org/anthology/P18-2055/. doi:10.18653/v1/P18-2055.
[53] M. Garcia, T. Kramer Vieira, C. Scarton, M. Idiart, A. Villavicencio, Probing for id-
9
iomaticity in vector space models, in: Proceedings of the 16th Conference of the
European Chapter of the Association for Computational Linguistics: Main Volume,
Association for Computational Linguistics, Online, 2021, pp. 3551ś3564. URL: https:
//aclanthology.org/2021.eacl-main.310.
[54] M. Garcia, T. Kramer Vieira, C. Scarton, M. Idiart, A. Villavicencio, Assessing the rep-
resentations of idiomaticity in vector models with a noun compound dataset labeled at
type and token levels, in: Proceedings of the 59th Annual Meeting of the Association
for Computational Linguistics and the 11th International Joint Conference on Natural
Language Processing (Volume 1: Long Papers), Association for Computational Linguistics,
Online, 2021, pp. 2730ś2741. URL: https://aclanthology.org/2021.acl-long.212.
[55] H. Tayyar Madabushi, E. Gow-Smith, C. Scarton, A. Villavicencio, Astitchintransformers:
Dataset and methods for the exploration of idiomaticity in pre-trained language models, in:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.,
Association for Computational Linguistics, 2021.
10