A Distributional Study of Negated Adjectives and Antonyms
Authors: Laura Aina, Raffaella Bernardi, Raquel Fernández (Universitat Pompeu Fabra, Università di Trento, VU Amsterdam, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper, we investigate the relation between negated adjectives and antonyms in English using Distributional Semantics methods. Results show that, on the basis of contexts of use, a negated adjective (e.g., not cold) is typically more similar to the adjective itself (cold) than to its antonym (hot); such effect is less strong for antonyms derived by afﬁxation (e.g., happy - unhappy).

Introduction
Negation has long represented a challenge for theoretical and computational linguists (see Horn (1989) and Morante and Sporleder (2012) for overviews): in spite of the relative simplicity of logical negation (¬p is true ↔p is false), complexity arises when negation interacts with morphosyntax, semantics and pragmatics. In this work, we focus on the negation of adjectives in English, expressed by the particle not modifying an adjective, as in not cold. A na¨ıve ∗Part of the work presented in this paper was carried out while the ﬁrst author was at the University of Amsterdam. account of these expressions would be to equate them to antonyms, and hence take them to convey the opposite of the adjective (e.g., not cold = hot). In fact, this simplifying assumption is sometimes made in computational approaches which model negation as a mapping from an adjective to its antonym (e.g., The Pham et al., (2015), Rimell et al., (2017)). However, a range of studies support what is known as mitigation hypothesis (Jespersen, 1965; Horn, 1972; Giora, 2006), according to which a negated adjective conveys an intermediate meaning between the adjective and its antonym (e.g., not large ≈medium-sized). The meaning of the adjective is mitigated by negation, while some emphasis on it still persists in memory (Giora et al., 2005). This view is compatible with pragmatic theories predicting that the use of a more complex expression (not large) when a simpler one is available (small) triggers the implicature that a different meaning is intended (e.g., medium-sized) (Grice, 1975; Horn, 1984). Computational models predicting similar mitigating effects are those by Hermann et al., (2013) and Socher et al., (2012; 2013). In this work, we investigate negated adjectives from the perspective of Distributional Semantics (Lenci, 2008; Turney and Pantel, 2010). We study antonymic adjectives and their negations in terms of their distribution across contexts of use: to this end, we employ an existing dataset of antonyms, whose annotation we further extend, and the distributional representations of these and their negated version, as derived with a standard distributional model. This allows us to conduct a data-driven study of negation and antonymy that covers a large set of instances. We compare pairs of antonyms with distinct lexical roots and those derived by afﬁxation, i.e., lexical and morphological antonyms (Joshi, 2012) (e.g., small large and happy - unhappy respectively). More over, we investigate the distinction between lexical antonyms that are contrary or contradictory, that is, those that have or do not have an available intermediate value (Fraenkel and Schul, 2008): e.g., something not cold is not necessarily hot - it could be lukewarm - but something not present is absent. As for negations of morphological antonyms, we compare instances of simple and double negation, where the latter occurs if the antonym that is negated is an afﬁxal negation (e.g., not unhappy). Our analyses show that, when considering distributional information, negated adjectives are more similar to the adjective itself than to the antonym (e.g., not cold is closer to cold than to hot), regardless of the type of antonym or of negation. However, we ﬁnd that morphological antonymy is closer to negation than lexical one is.

Conclusion
We have investigated negated adjectives using the tools of Distributional Semantics, which allows us to quantify the similarities between expressions on the basis of how they are used. Our analyses show that, when considering contexts of occurrence, negating an adjective does not make it closer to the antonym than to the adjective itself. This can be seen as a result of the various functions of negation (e.g., mitigation, contradiction to an expectation, politeness) that may lead to different patterns of use for negated adjectives and antonyms. Further research may shed light on which type of contexts actually discriminate them, for example through a corpus study, and which other properties negated adjectives have in a distributional space, such as their interaction with scalar dimensions (e.g., not hot vs. freezing, cold, lukewarm, hot etc.; Wilkinson and Tim (2016)). Finally, while for the purpose of this study we opted for a standard word2vec model, one could test for the same effects with differently obtained distributional vectors. Despite its current limitations in covering truthrelated aspects of meaning, Distributional Semantics was shown by Kruszewski et al. (2017) to be apt to model at least some aspects of negation, especially if graded in nature, such as alternativehood. Our study provides supporting evidence for this line of research and in addition points at the utility of using Distributional Semantics to uncover nuanced differences in use between a negation and other expressions, even when logically equivalent. Moreover, we regard our results to be of general interest for the NLP community, since effects of negation like the ones we studied and how they are represented in a distributional space can be critical for tasks like sentiment analysis (e.g., what does it imply that a costumer is not happy or not unhappy with a product?; Wiegand et al, (2010)).