Grounding the Lexical Sets of Causative-Inchoative Verbs with Word Embedding
Authors: Edoardo Maria Ponti, Elisabetta Jezek, Bernardo Magnini (University of Cambridge, Università di Pavia, Fondazione Bruno Kessler, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Lexical sets contain the words filling the argument positions of a verb in one of its senses. They can be extracted from corpora automatically. The purpose of this paper is demonstrating that their vector representation based on word embedding provides insights onto many linguistic phenomena, such as causative-inchoative verbs. A first experiment aims at investigating the internal structure of the sets, which are known to be radial and continuous categories cognitively. A second experiment shows that the distance between the intransitive subject set and transitive object set is correlated with the spontaneity of the event expressed by the verb, defined according to morphological coding and frequency.

Introduction
Lexicographic attempts to cope with verb sense disambiguation often rely on ``lexical sets'' AUTHOR, which represent the lists of corpus-derived words that appear as arguments for each distinct verb sense. The arguments are the ``slots'' that have to be filled to satisfy the valency of a verb (subject, object, etc.). For example, \{gun, bullet, shot, projectile, rifle...\} is the lexical set of the object for the sense `to shoot' of to fire. In previous works, e.g. AUTHOR, lexical sets were collected manually and were compared through set analysis. The measure of similarity between two sets was proportional to the extent of their intersection. We believe that possible improvements may stem from deriving the lexical sets automatically and from exploiting the semantic information of the fillers fully. In this work, we devise an extraction method from a huge corpus and use a distributional semantics approach to perform our analyses. More specifically, we represent fillers as word vectors and compare them with spatial distance measures. In order to test the relevance for linguistic theory of this approach, we focus on a case study, namely the properties of verbs undergoing the causative-inchoative alternation. Section 1.1. outlines a framework for word embeddings and section 1.2 introduces the case study. Section 2 presents the method and the data, whereas section 3 reports the results of a couple of experiments.

Conclusion
Our work provided evidence that lexical sets of Italian causative-inchoative verbs are continuous and radial categories, whose distribution around the prototype vary to a great extent. It is sensitive to the grammatical role and sometimes to the position of the verb in the so-called spontaneity scale. Moreover, a correlation was discovered between the distance between transitive object and intransitive subject lexical sets of a given verb and its cross-linguistic tendency to appear more frequently as intransitive or as transitive. Figure fig3 is a synopsis of this result in the context of the correlations established in previous works. [ht] \centering [->,>=stealth',shorten >=1pt,auto,node distance=3cm, thick] \node (1) {Frequently Intransitive}; \node (2) [above of=1] {Spontaneous}; \node (3) [below left of=1] {Unmarked Intransitive}; \node (4) [below right of=1] {Distant S and O centres}; \path[->,dashed] (2) edge node [right] {?} (1); \path[<->] (1) edge node [right] {$\rho$=0.56} (4) (1) edge node [left] {$\tau$=0.65} (3); In Figure fig3, solid lines stand for correlations proven based on cross-linguistic evidence (frequency-form) and evidence from the Italian language (frequency-lexical sets). The dotted line, on the other hand, suggests the existence of and underlying motivation for the correlations, which nonetheless remains unproven and undetermined in its nature. Its possible validation is left to future research. Future works should also choose different pre-trained vector models, in order to try and replicate these results. In particular, the new vector models could be optimized for similarity through semantic lexica AUTHOR or based on syntactic dependencies AUTHOR. The experiments in this work may be extended to other languages, either individually or through a multi-lingual word embedding AUTHOR.