Exploring Cross-Lingual Sense Mapping in a Multilingual Parallel Corpus
Authors: Francis Bond, Giulia Bonansinga (Nanyang Technological University, Università di Pisa, 2015)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Cross-lingual approaches can make sense annotation of existing parallel corpora inexpensive, thus giving new means to improve any supervised Word Sense Disambiguation system. We compare two such approaches that can be applied to any multilingual parallel corpus, as long as large inter-linked sense inventories exist for all the languages involved.

Introduction
Clwsd aims to automatically disambiguate a text in one language by exploiting its differences with other language(s) in a parallel corpus. Since the introduction of a dedicated task in SemEval-2013 AUTHOR, work on clwsd has increased, but parallel corpora have been used to this purpose for a long time; see for instance AUTHOR, AUTHOR, AUTHOR, AUTHOR and, more recently, AUTHOR and AUTHOR. AUTHOR exploit the semantic information inferred by translation correspondences in parallel corpora as a clue for WSD; AUTHOR represent the milestone behind one of the approaches here evaluated, i.e. sense disambiguation exploiting the polysemic differential between two languages. As AUTHOR pointed out, wsd is so challenging mainly because most approaches require large amounts of high-quality sense-annotated data. Ten years later, the knowledge acquisition bottleneck still needs to be addressed for most languages. Given an ambiguous word in a parallel corpus, having access to the semantic space (here intended as all the senses associated to its lemma) of each of its aligned translations allows one to exploit similarities and differences in the languages involved and, consequently, to make more educated guesses of the intended meaning. This simple, yet powerful, intuition can be decisive, if not in disambiguating all words, at least in reducing ambiguity and thus the human effort in annotating a whole text from scratch. We explore two approaches of annotating a multilingual parallel corpus in English, Italian and Romanian built upon sc AUTHOR. We describe it in Section 2 along with a brief outline of the first approach, sp, which was pioneered by AUTHOR. In Section~sec:requirements we list the requirements and the necessary preprocessing steps common to both approaches. In Section 2 we present the second approach, multilingual si. Section 3 discusses the results achieved on the multilingual corpus with each method. We conclude in Section 4 anticipating future work.

Conclusion
To our knowledge, this is the first attempt to disambiguate a parallel corpus by using multilingual SI. The more languages are considered, the more ambiguity should be reduced and the better SI is expected to perform. In future work, we plan to include the Japanese SemCor (Bond et al., 2012) to test our hypothesis that translations from a different language family will discriminate further. We also plan to use a different parallel corpus built on open translations of The Adventure of the Speckled Band by Sir Arthur Conan Doyle. We will also try to calculate SFS from untagged text, following McCarthy and Carroll (2003). Furthermore, we are investigating alternative ways to solve the ambiguity left whenever SI does not lead to a single synset; for instance, we plan to apply some implementation of Lesk (Lesk, 1986) on the subset found by SI. Finally, we aim to port to WN 3.0 the sense clustering carried out by Navigli (2006) to perform a coarse-grained evaluation, which would ignore minor sense distinctions. An initial comparison with Babelfly (Moro et al., 2014) would certainly be enlightening as well. All data and scripts derived by our work will be made available, except for those derived from RSC, as its license currently forbids it.