The work in this thesis offers a number of potential future research directions, both on the level of theory and on the level of experimentation.
On the theoretical side, this thesis gives a theoretical extension of the categorical frame- work of Coecke, Grefenstette, and Sadrzadeh [CGS13] by making use of the Lambek Calcu- lus with the structural control modalities of [Moo96]. A number of issues with our proposal remain open. First of all, we worked with a controlled form of contraction. However, the results of Kanovich, Kuznetsov, and Scedrov [KKS16;KKS17] show that even a controlled form of contraction may lead to undecidability of the employed grammar logic. Although the possibility of undecidability does not directly impede concrete experimentation with the models, it does beg further investigation on the formal properties of the proposed theoretical model. One possible solution is to bound the number of applications of the contraction rule to get a light version of the logic that by its bound becomes decidable. Another theoretical issue was that the direct interpretation of types and proofs as vector spaces and multi-linear maps was not able to distinguish the strict and sloppy readings of verb phrase ellipsis. Al- though we mended this by modelling the vector semantics with a lambda calculus as an intermediary language, a current approach to solve this issue without relying on lambdas, is to replace vector spaces with Fock spaces: these are spaces which offer a choice between any number of tensor products of a given vector space. One can then extract the relevant number of copies of a vector space whenever the grammar logic wills it.
On the experimental side, one of the limits of the type-driven approach to composition of distributional representation is scalability: while neural approaches are able to encode arbitrary sentences, the type-driven approach lacks this flexibility for two reasons. First, unbounded parsing remains a challenge in the typelogical approach; second, the tensor- based approach relies on high quality tensor representations for words, and methods to learn these have been theoretically established — especially in the last chapter of this thesis — but haven’t been implemented on full scale yet. Thus, one potential avenue for research is to make use of systems that have learned how to assign types to words in a typelogical grammar.
Work on extracting categorial lexicons form structured data is abundant: there is the unification algorithm of Buszkowski and Penn [BP90], extensions [BR01], and the thesis of Kanazawa [Kan95]. In addition, there is the CCGBank [HS07], which contains CCG deriva- tions for the dependency trees in the Penn Treebank. On top of the CCGBank, parsers and su- pertaggers have been developed [XAC15;LLZ16]. On the multimodal front, Moot [Moo15] describes a typelogical treebank for French and Moot [Moo18] a language-independent chart parsing method. One promising new direction that interacts well with the approach advo- cated in this thesis, uses self-attention neural networks to create a system that can, after training, map raw text to word types [KMD19].
Next to extracting grammatical types, the other main avenue for research is to expand the implementation that we gave in Chapter 5 of a tensor-based skipgram model to scale up to longer sentences, and be able to evaluate these representations on large datasets such as the Stanford Natural Language Inference datasets (SNLI, [Bow+15]) and it’s multi-genre and crosslingual incarnations (MNLI, [WNB18], XNLI, [Con+18]).
Finally, another potential line of research is to extract a large scale tensor-based model from current state of the art contextualised embeddings like ELMo [Pet+18] and BERT [Dev+19]. Such models assume that the embedding of a word is a vector that is a function of all other words in the sentence the word occurs in. This parameterisation naturally allows one to model some words as a higher order tensor that, by the fact that it encodes a multilinear map, contextualises itself in its arguments. For example, in the adjective-noun case, the vec- tor for the adjectivally modified noun depends on the vector for the noun and the effect of the adjective matrix on it.
Then, to end: although the contributions of this thesis provide support for the relevance of type-driven models in a world that appears to be focused on neural methods, many ques- tions arise. It is my hope that future research in this field may improve our understanding of the relevance of linguistic knowledge in semantic representations.
Bibliography
[Aba+16] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. “Tensorflow: A system for large-scale machine learning”. In: 12th {USENIX} Symposium on Operating Systems Design and Implementa- tion ({OSDI} 16). 2016, pp. 265–283.
[Abr09] Samson Abramsky. “No-cloning in categorical quantum mechanics”. In: Semantic Techniques in Quantum Computation (2009), pp. 1–28.
[ALM19] Sanjeev Arora, Yingyu Liang, and Tengyu Ma. “A simple but tough-to- beat baseline for sentence embeddings”. In: 5th International Conference on Learning Representations, ICLR 2017. 2019.
[BBZ14] Marco Baroni, Raffaela Bernardi, and Roberto Zamparelli. “Frege in space: A program of compositional distributional semantics”. In: LiLT (Linguis- tic Issues in Language Technology) 9 (2014).
[Bim14] Katalin Bimbó. Proof theory: Sequent calculi and related formalisms. CRC Press, 2014.
[Boj+17] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. “Enriching word vectors with subword information”. In: Transactions of the Association of Computational Linguistics 5.1 (2017), pp. 135–146. URL: https://www.aclweb.org/anthology/Q17-1010.
[Bow+15] Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. “A large annotated corpus for learning natural language inference”. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015, pp. 632–642.
[BP90] Wojciech Buszkowski and Gerald Penn. “Categorial grammars deter- mined from linguistic data by unification”. In: Studia Logica 49.4 (1990), pp. 431–454.
[BR01] Roberto Bonato and Christian Retoré. “Learning rigid lambek grammars and minimalist grammars from structured sentences”. In: Third workshop on learning language in logic, Strasbourg. 2001, pp. 23–34.
[Bru+12] Elia Bruni, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. “Dis- tributional semantics in technicolor”. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume
1. Association for Computational Linguistics. 2012, pp. 136–145. URL: https://www.aclweb.org/anthology/P12-1015.
[BS11] Johan Bos and Jennifer Spenader. “An annotated corpus for the analysis of VP ellipsis”. In: Language Resources and Evaluation 45.4 (2011), pp. 463– 494.
[BTB14] Elia Bruni, Nam-Khanh Tran, and Marco Baroni. “Multimodal distribu- tional semantics”. In: Journal of Artificial Intelligence Research 49 (2014), pp. 1–47.
[Bus01] Wojciech Buszkowski. “Lambek grammars based on pregroups”. In: In- ternational Conference on Logical Aspects of Computational Linguistics. Springer. 2001, pp. 95–109.
[BZ10] Marco Baroni and Roberto Zamparelli. “Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space”. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Lan- guage Processing. Association for Computational Linguistics. 2010, pp. 1183– 1193.URL:https://www.aclweb.org/anthology/D10-1115.
[Cer+18] Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al. “Universal sentence encoder”. In: arXiv preprint arXiv:1803.11175 (2018).URL:https://arxiv.org/pdf/1803.11175.pdf.
[CGS13] Bob Coecke, Edward Grefenstette, and Mehrnoosh Sadrzadeh. “Lam- bek vs. Lambek: Functorial vector space semantics and string diagrams for Lambek calculus”. In: Annals of pure and applied logic 164.11 (2013), pp. 1079–1100.
[CH16] Karel Chvalovsk`y and Rostislav Horˇcík. “Full lambek calculus with con- traction is undecidable”. In: The Journal of Symbolic Logic 81.2 (2016), pp. 524–540.
[Chu40] Alonzo Church. “A formulation of the simple theory of types”. In: The Journal of Symbolic Logic 5.2 (1940), pp. 56–68.
[Cla13] Stephen Clark. “Type-driven syntax and semantics for composing mean- ing vectors”. In: Quantum Physics and Linguistics: A Compositional, Dia- grammatic Discourse (2013), pp. 359–377.
[Cla15] Stephen Clark. “Vector Space Models of Lexical Meaning”. In: The Hand- book of Contemporary Semantic Theory. John Wiley & Sons, Ltd, 2015. Chap. 16, pp. 493–522.ISBN: 9781118882139.DOI:10.1002/9781118882139. ch16. eprint: https://onlinelibrary.wiley.com/doi/pdf/ 10.1002/9781118882139.ch16.URL:https://onlinelibrary. wiley.com/doi/abs/10.1002/9781118882139.ch16.
[Con+17] Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and An- toine Bordes. “Supervised Learning of Universal Sentence Represen- tations from Natural Language Inference Data”. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017, pp. 670–680.
[Con+18] Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. “XNLI: Evaluating Cross-lingual Sentence Representations”. In: Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing. Brussels, Bel- gium: Association for Computational Linguistics, 2018, pp. 2475–2485. DOI:10.18653/v1/D18-1269.URL:https://www.aclweb.org/ anthology/D18-1269.
[CP01] Peter W Culicover and Paul M Postal. Parasitic gaps: A history. MIT Press, 2001.
[CSC10] Bob Coecke, Mehrnoosh Sadrzadeh, and Stephen Clark. “Mathematical foundations for a compositional distributional model of meaning”. In: Linguistic Analysis 36.1 (2010), pp. 345–384.
[CWB18] WooJin Chung, Sheng-Fu Wang, and Samuel R. Bowman. “The Lifted Matrix-Space Model for Semantic Composition”. In: Proceedings of the 22nd Conference on Computational Natural Language Learning. 2018, pp. 508– 518.
[DC19] András Dobó and János Csirik. “A comprehensive study of the param- eters in the creation and comparison of feature vectors in distributional semantic models”. In: Journal of Quantitative Linguistics (2019), pp. 1–28. [Dev+19] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-training of deep bidirectional transformers for language un- derstanding”. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2019.
[DSP91] Mary Dalrymple, Stuart M Shieber, and Fernando CN Pereira. “Ellipsis and higher-order unification”. In: Linguistics and philosophy 14.4 (1991), pp. 399–452.
[Fin+01] Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. “Placing search in context: The concept revisited”. In: Proceedings of the 10th international conference on World Wide Web. ACM. 2001, pp. 406–414.
[Fir57] John R Firth. “A synopsis of linguistic theory, 1930-1955”. In: Studies in linguistic analysis (1957).
[FPC15] Daniel Fried, Tamara Polajnar, and Stephen Clark. “Low-rank tensors for verbs in compositional distributional semantics”. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Vol. 2. 2015, pp. 731–736.
[Ger+16] Daniela Gerz, Ivan Vuli´c, Felix Hill, Roi Reichart, and Anna Korhonen. “SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity”. In: Pro- ceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016, pp. 2173–2182.
[Gre+13] Edward Grefenstette, Georgiana Dinu, Yao-Zhong Zhang, Mehrnoosh Sadrzadeh, and Marco Baroni. “Multi-step regression learning for com- positional distributional semantics”. In: Proceedings of the 10th Interna- tional Conference on Computational Semantics (IWCS 2013). 2013.
[Gre13] Edward Grefenstette. “Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics”. PhD thesis. Ox- ford, UK: University of Oxford, 2013.URL:http://arxiv.org/abs/ 1311.1539.
[Gro01] Philippe de Groote. “Towards Abstract Categorial Grammars”. In: Pro- ceedings of the 39th Annual Meeting on Association for Computational Lin- guistics. ACL ’01. Toulouse, France: Association for Computational Lin- guistics, 2001, pp. 252–259.
[GS11a] Edward Grefenstette and Mehrnoosh Sadrzadeh. “Experimental sup- port for a categorical compositional distributional model of meaning”. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2011, pp. 1394– 1404.
[GS11b] Edward Grefenstette and Mehrnoosh Sadrzadeh. “Experimenting with transitive verbs in a discocat”. In: Proceedings of the GEMS 2011 Work- shop on GEometrical Models of Natural Language Semantics. Association for Computational Linguistics. 2011, pp. 62–66.
[GS15] Edward Grefenstette and Mehrnoosh Sadrzadeh. “Concrete models and empirical evaluations for the categorical compositional distributional model of meaning”. In: Computational Linguistics 41.1 (2015), pp. 71–118. [Har54] Zellig S Harris. “Distributional structure”. In: Word 10.2-3 (1954), pp. 146–
162.
[Hen95] P. Hendriks. Comparatives and Categorial Grammar. Groningen disserta- tions in linguistics. Grodil, 1995.URL:https://books.google.co. uk/books?id=FeNUuAAACAAJ.
[HRK15] Felix Hill, Roi Reichart, and Anna Korhonen. “Simlex-999: Evaluating semantic models with (genuine) similarity estimation”. In: Computational Linguistics 41.4 (2015), pp. 665–695.
[HS07] Julia Hockenmaier and Mark Steedman. “CCGbank: a corpus of CCG derivations and dependency structures extracted from the Penn Tree- bank”. In: Computational Linguistics 33.3 (2007), pp. 355–396.
[HS19] Jules Hedges and Mehrnoosh Sadrzadeh. “A Generalised Quantifier The- ory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras”. In: Mathematical Structures in Computer Sci- ence 29.6 (2019), pp. 783–809.
[Iyy+15] Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. “Deep unordered composition rivals syntactic methods for text clas- sification”. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Nat- ural Language Processing (Volume 1: Long Papers). Vol. 1. 2015, pp. 1681– 1691.
[Jac11] Bart Jacobs. “Bases as coalgebras”. In: International Conference on Algebra and Coalgebra in Computer Science. Springer. 2011, pp. 237–252.
[Jac99] Pauline Jacobson. “Towards a variable-free semantics”. In: Linguistics and philosophy 22.2 (1999), pp. 117–185.
[Jäg06] Gerhard Jäger. Anaphora and type logical grammar. Vol. 24. Springer Sci- ence & Business Media, 2006.
[Jäg98] Gerhard Jäger. “A Multi-Modal Analysis of Anaphora and Ellipsis”. In: University of Pennsylvania Working Papers in Linguistics 5.2 (1998), p. 2. [Kan95] Makoto Kanazawa. “Learnable Classes of Categorial Grammars”. PhD
thesis. Stanford University, 1995.
[Kar15] Dimitri Kartsaklis. “Compositional Distributional Semantics with Com- pact Closed Categories and Frobenius Algebras”. PhD thesis. University of Oxford, 2015.
[Kar16] Dimitri Kartsaklis. “Coordination in categorical compositional distribu- tional semantics”. In: arXiv preprint arXiv:1606.01515 (2016).
[KC14] Douwe Kiela and Stephen Clark. “A systematic study of semantic vector space model parameters”. In: Proceedings of the 2nd Workshop on Contin- uous Vector Space Models and their Compositionality (CVSC). 2014, pp. 21– 30.
[Kem+15] Ruth Kempson, Ronnie Cann, Eleni Gregoromichelaki Arasheshghi, and Matthew Purver. “Ellipsis”. In: Chapter 4 of The Handbook of Contemporary Semantic Theory 3 (2015), p. 114.
[Kin01] Walter Kintsch. “Predication”. In: Cognitive science 25.2 (2001), pp. 173– 202.
[Kir+15] Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. “Skip-thought vectors”. In: Advances in neural information processing systems. 2015, pp. 3294–3302. [KKS16] Max Kanovich, Stepan Kuznetsov, and Andre Scedrov. “Undecidability
of the Lambek calculus with a relevant modality”. In: International Con- ference on Formal Grammar. Springer. 2016, pp. 240–256.
[KKS17] Max Kanovich, Stepan Kuznetsov, and Andre Scedrov. “Undecidability of the Lambek calculus with subexponential and bracket modalities”. In: International Symposium on Fundamentals of Computation Theory. Springer. 2017, pp. 326–340.
[KKS19] Max Kanovich, Stepan Kuznetsov, and Andre Scedrov. “Undecidability of a Newly Proposed Calculus for CatLog3”. In: International Conference on Formal Grammar. Springer. 2019, pp. 67–83.
[KL17] Yusuke Kubota and Robert Levine. “Pseudogapping as Pseudo-VP-Ellipsis”. In: Linguistic Inquiry 48.2 (2017), pp. 213–257.
[KM97] Natasha Kurtonina and Michael Moortgat. “Structural control”. In: Spec- ifying syntactic structures (1997), pp. 75–113.
[KMD19] Konstantinos Kogkalidis, Michael Moortgat, and Tejaswini Deoskar. “Con- structive Type-Logical Supertagging with Self-Attention Networks”. In: 4th Workshop on Representation Learning for NLP, ACL (2019).
[KPS16] Dimitri Kartsaklis, Matthew Purver, and Mehrnoosh Sadrzadeh. “Verb Phrase Ellipsis using Frobenius Algebras in Categorical Compositional Distributional Semantics”. In: DSALT Workshop, European Summer School on Logic, Language and Information (2016).
[Kru+16] Germán Kruszewski, Denis Paperno, Raffaella Bernardi, and Marco Ba- roni. “There is no logical negation here, but there are alternatives: Mod- eling conversational negation with distributional semantics”. In: Com- putational Linguistics 42.4 (2016), pp. 637–660.
[KS13] Dimitri Kartsaklis and Mehrnoosh Sadrzadeh. “Prior disambiguation of word tensors for constructing sentence vectors”. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013, pp. 1590–1601.
[KS14] Dimitri Kartsaklis and Mehrnoosh Sadrzadeh. “A Study of Entangle- ment in a Categorical Framework of Natural Language”. In: Proceedings of the 11th Workshop on Quantum Physics and Logic (QPL). Kyoto ‚Japan. 2014.
[KSP12] Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, and Stephen Pulman. “A Uni- fied Sentence Space for Categorical Distributional- Compositional Se- mantics: Theory and Experiments”. In: Proceedings of 24th International Conference on Computational Linguistics (COLING): Posters. Mumbai ‚In- dia. 2012.
[KSP13] Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, and Stephen Pulman. “Sepa- rating Disambiguation from Composition in Distributional Semantics”. In: Proceedings of the Seventeenth Conference on Computational Natural Lan- guage Learning. Sofia, Bulgaria: Association for Computational Linguis- tics, Aug. 2013, pp. 114–123. URL: https : / / www . aclweb . org / anthology/W13-3513.
[Lam58] Joachim Lambek. “The mathematics of sentence structure”. In: The Amer- ican Mathematical Monthly 65.3 (1958), pp. 154–170.
[Lam61] Joachim Lambek. “On the calculus of syntactic types”. In: Structure of language and its mathematical aspects 166 (1961), p. C178.
[Lam68] Joachim Lambek. “Deductive systems and categories”. In: Theory of Com- puting Systems 2.4 (1968), pp. 287–318.
[Lam88] Joachim Lambek. “Categorial and categorical grammars”. In: Categorial grammars and natural language structures. Springer, 1988, pp. 297–317. [Lam99] Joachim Lambek. “Type grammar revisited”. In: International Conference
on Logical Aspects of Computational Linguistics. Springer. 1999, pp. 1–27. [LB16] Jey Han Lau and Timothy Baldwin. “An Empirical Evaluation of doc2vec
with Practical Insights into Document Embedding Generation”. In: Pro- ceedings of the 1st Workshop on Representation Learning for NLP. 2016, pp. 78– 86. DOI: 10.18653/v1/W16- 1609. URL: https://www.aclweb. org/anthology/W16-1609.
[LB96] Kevin Lund and Curt Burgess. “Producing high-dimensional semantic spaces from lexical co-occurrence”. In: Behavior research methods, instru- ments, & computers 28.2 (1996), pp. 203–208.
[LD97] Thomas K Landauer and Susan T Dumais. “A solution to Plato’s prob- lem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.” In: Psychological review 104.2 (1997), p. 211. [Len08] Alessandro Lenci. “Distributional semantics in linguistic and cognitive
research”. In: Italian journal of linguistics 20.1 (2008), pp. 1–31.
[LG14] Omer Levy and Yoav Goldberg. “Dependency-based word embeddings”. In: Proceedings of the 52nd Annual Meeting of the Association for Computa- tional Linguistics (Volume 2: Short Papers). Vol. 2. 2014, pp. 302–308.
[LGD15] Omer Levy, Yoav Goldberg, and Ido Dagan. “Improving distributional similarity with lessons learned from word embeddings”. In: Transactions of the Association for Computational Linguistics 3 (2015), pp. 211–225. [LLZ16] Mike Lewis, Kenton Lee, and Luke Zettlemoyer. “Lstm ccg parsing”. In:
Proceedings of the 2016 Conference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Language Technologies. 2016, pp. 221–231.
[LM14] Quoc Le and Tomas Mikolov. “Distributed representations of sentences and documents”. In: International Conference on Machine Learning. 2014, pp. 1188–1196.
[LS88] Joachim Lambek and Philip J Scott. Introduction to higher-order categorical logic. Vol. 7. Cambridge University Press, 1988.
[Mar+14] Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli. “A SICK cure for the evaluation of compositional distributional semantic models”. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014). Reykjavik, Iceland: European Languages Resources Association (ELRA),
May 2014, pp. 216–223.URL:http://www.lrec-conf.org/proceedings/ lrec2014/pdf/363_Paper.pdf.
[MC15] Jean Maillard and Stephen Clark. “Learning adjective meanings with a tensor-based skip-gram model”. In: Proceedings of the Nineteenth Confer- ence on Computational Natural Language Learning. 2015, pp. 327–331. [MC91] George A Miller and Walter G Charles. “Contextual correlates of seman-
tic similarity”. In: Language and cognitive processes 6.1 (1991), pp. 1–28. [MCG14] Jean Maillard, Stephen Clark, and Edward Grefenstette. “A type-driven
tensor-based semantics for CCG”. In: Proceedings of the EACL 2014 Type Theory and Natural Language Semantics Workshop. 2014, pp. 46–54.
[Mer01] Jason Merchant. The syntax of silence: Sluicing, islands, and the theory of ellipsis. Oxford University Press on Demand, 2001.
[Mik+13] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. “Distributed representations of words and phrases and their compo- sitionality”. In: Advances in neural information processing systems. 2013, pp. 3111–3119.
[Mil+14] Dmitrijs Milajevs, Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, and Matthew Purver. “Evaluating neural word representations in tensor-based com- positional settings”. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014, pp. 708–719.URL: https://www.aclweb.org/anthology/D14-1079.
[ML08] Jeff Mitchell and Mirella Lapata. “Vector-based models of semantic com- position”. In: proceedings of ACL-08: HLT (2008), pp. 236–244.
[ML10] Jeff Mitchell and Mirella Lapata. “Composition in distributional models of semantics”. In: Cognitive science 34.8 (2010), pp. 1388–1429.
[MMS96] Glyn Morrill and Josep-Maria Merenciano Saladrigas. “Generalising dis- continuity”. In: TAL. Traitement automatique des langues 37.2 (1996), pp. 119–