Due to the growing volume of textual information available in multiple languages, there is a great demand for Natural Language Processing (NLP) techniques that can automatically process and manage multi-lingual texts, supporting information access and communication in core areas of society (e.g. healthcare, business, science). Many NLP tasks and applications rely on task-specific lexicons (e.g. dictionaries, word classifications) for optimal performance. Recently, automatic acquisition of lexicons from relevant texts has proved a promising, cost-effective alternative to manual lexicography. It has the potential to considerably enhance the viability and portability of NLP technology both within and across languages. However, this approach has been explored for a very small number of resource-rich languages only, leaving the vast majority of worlds’ languages without useful technology. The ambitious goal of this project was to take research in lexical acquisition to the level where it can support multi-lingual NLP, involving also languages for which no parallel language resources (e.g. corpora, knowledge resources) are available. Building on an emerging line of research which uses mainly naturally occurring supervision (connections between languages) to guide cross-lingual NLP, we developed a radically novel approach to lexical acquisition. This approach is capable of transferring lexical knowledge from one language to another as well as simultaneously learning it for a diverse set of languages using new methodology based on guiding joint learning and inference with rich knowledge about cross-lingual connections. The project has not only created novel lexical acquisition technology but has also taken cross-lingual NLP a big step toward to the direction where it is no longer dependent on parallel resources. We have demonstrated that our approach can support fundamental tasks and applications aimed at broadening the global reach of NLP to areas where it is now critically needed.
