Tracing metaphors in time through self-distance in vector spaces
Authors: Marco Del Tredici, Malvina Nissim, Andrea Zaninello (University of Amsterdam, University of Groningen, Zanichelli Editore, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
From a diachronic corpus of Ita\-lian, we build consecutive vector spaces in time and use them to compare a term's cosine similarity to itself in different time spans. We assume that a drop in similarity might be related to the emergence of a metaphorical sense at a given time. Similarity-based observations are matched to the actual year when a figurative meaning was documented in a reference dictionary and through manual inspection of corpus occurrences.

Introduction
It is widely acknowledged that metaphors are pervasive in language use, and that their detection and interpretation are crucial to language processing AUTHOR. One tricky aspect related to metaphors is their dynamic nature: new metaphors are created all the time. For example, in recent years the Italian term ``talebano'' (`Taliban'), previously only used to refer to the Islamic fundamentalist political movement founded in the Nineties in Afghanistan (Example~exa:talib-lit), has come to define more generally someone who is extreme in his or her positions, for example regarding food, use of medicines, and the like (Example~exa:talib-fig)., see Section~sec:corpus.} \item (lit.) l'operazione [...] ha permesso di arrestare un talebano esperto in esplosivi \itemsep 0pt \item (fig.) [...] senza l'atteso top player, e di un allenatore talebano della tattica \noindent If the metaphorical meaning becomes commonly used, it might get recorded in reference dictionaries, too. Indeed, for the case of ``talebano'' the Italian dictionary Zingarelli AUTHOR has recorded the metaphorical extension (``che (o chi) \`{e} dogmatico, integralista'') in the year 2009, while until then only the literal meaning was included. Most of the computational work on metaphors has focused on their identification and interpretation using a variety of techniques and models, such as clustering AUTHOR, LDA topic modeling AUTHOR, tree kernels AUTHOR, but all from a purely synchronic perspective..} The way metaphors develop across time, instead, and whether the shift of a word's literal meaning to a figurative one can be automatically detected and modelled is as of now a little investigated aspect. As a contribution in this sense, we build on the basic observation that if a metaphorical meaning is acquired by a term at a certain point in time, the context of use of that term will, at least partially, change. In this paper we offer a proof of concept of this assumption, based on a selection of terms. (Dis)similarity of contexts is measured relying on the distributional semantics approach, and thus on the terms' vector representations, and the existence of a metaphoric shift is derived from the Zingarelli dictionary of Italian.

Conclusion
This work was meant as an exploration of the assumption that the emergence of the metaphorical use of a term might be mirrored in changes in cosine similarity of the term to itself across time. Such assumption has been partially confirmed by the comparison to the Zingarelli dictionary, while we found a more robust evidence when inspecting the terms' contexts of use manually. Future work will stem from methodology and observations discussed here. Specifically, we plan to investigate further several aspects of this initial work, including the relation between changes in cosine similarity and frequency of use of a word: to which extent a change of the former relates to an increase of the latter? Mostly though, we plan to run experiments on larger sets of words with the aim to consolidate and then further exploit the mainly qualitative observations reported here towards the development of reliable predictive metrics which can serve to detect the emergence of shifts automatically, in a completely bottom-up fashion. Malvina Nissim would like to thank the ILC-CNR ItaliaNLP~Lab for their hospitality while working on this project. We are also grateful to the anonymous reviewers who provided insightful comments that doubtlessly contributed to improve this paper.