# Towards a Universal Semantic Dictionary
## 1 Introduction  
Computer-driven natural language processing plays an increasingly important role in our everyday life. In the current digital world, using natural language for human-machine communication has become a basic requirement. To meet this requirement, it is inevitable to analyze human languages semantically. Nowadays, state-of-the-art systems represent word meaning with high dimensional vectors, known as word embeddings.  
Current embedding models are learned from monolingual corpora, and therefore infer language dependency. However, one might ask if the structure of the different embeddings, i.e., different meaning representations, are universal among all human languages. [PERSON] et al. [1] proposed a procedure for building graphs from concepts of different languages. They found that these graphs reflected a certain structure of meaning with respect to the languages they were built of. They concluded that the structural properties of these graphs are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of literary traditions. Such findings led to a new research direction within the field of computational semantics, which focuses on the construction of universal meaning representations, most of the times in the form of cross-lingual word embedding models [2]. One way to create such models is to find mappings between embeddings of different languages [3; 4; 5]. These mappings are usually transformation matrices that serve to map an embedding from one language space to another. In most of the cases, one pivot space is chosen, typically English, where every other language will be mapped into. In our work, we do not chose any language to serve as a pivot space, but instead, we create a language independent space, i.e., a universal space, to where we map all languages.  
Our work proposes a novel procedure for learning such mappings in the form of translation matrices that serve to map each language to a shared space. The method was first tested on bilingual, and later on multilingual corpora as well. With the bilingual experiments, we obtained on [PERSON]'s benchmark data [6] a 0.377 precision@1 score for English-Italian and a 0.310 precision@1 score for Italian-English translation. These results, however, are far from the current state-of-the-art result on this dataset [5], but they are in the same order of magnitude or even better than many previous attempts [3; 6; 7]. For further bilingual and for some multilingual experiments, an own dataset was created from the PanLex database [8]. We published the obtained scores of various experimental settings using this dataset [9]. Generally, bilingual experiments using only the PanLex dataset resulted in worse scores than using only [PERSON]'s dataset, but combining the two showed a slight improvement in the Italian-English direction. Multilingual experiments were carried out using three different languages--English, Italian, and Spanish--at the same time. The obtained pairwise precision values showed worse results than when the system was trained in bilingual mode. However, these results are still promising considering that a completely new approach was implemented, and they showed that the system definitely learned from data which are available for a wide range of languages. To reach state-of-the-art performance, the system has to deal with dimension reduction in the multilingual space and further experimentation in multilingual mode with an extended number of languages could also provide meaningful outputs.  
Section 2 summarizes the progress made on learning translation matrices between word embeddings over the last five years. Section 3 discusses the proposed method in detail. Following that, Section 4 describes our experimental setup, and Sections 5 and 6 report and analyze the obtained results. Finally, Section 7 concludes with the advantages and disadvantages of the proposed model, and also discusses some improvements for future work.