Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations

Graph-based methods for large-scale multilingual knowledge integration

Given that much of our knowledge is expressed in textual form, information systems are increasingly dependent on knowledge about words and the entities they represent. This thesis investigates novel methods for automatically building large repositories of knowledge that capture semantic relationships between words, names, and entities, in many different languages. Three major contributions are made, each involving graph algorithms and statistical techniques that combine evidence from multiple sources of information. The lexical integration method involves learning models that disambiguate word meanings based on contextual information in a graph, thereby providing a means to connect words to the entities that they denote. The entity integration method combines semantic items from different sources into a single unified registry of entities by reconciling equivalence and distinctness information and solving a combinatorial optimization problem. Finally, the taxonomic integration method adds a comprehensive and coherent taxonomic hierarchy on top of this registry, capturing how different entities relate to each other. Together, these methods can be used to produce a large-scale multilingual knowledge base semantically describing over 5 million entities and over 16 million natural language words and names in more than 200 different languages.Da ein großer Teil unseres Wissens in textueller Form vorliegt, sind Informationssysteme in zunehmendem Maße auf Wissen über Wörter und den von ihnen repräsentierten Entitäten angewiesen. Gegenstand dieser Arbeit sind neue Methoden zur automatischen Erstellung großer multilingualer Wissensbanken, welche semantische Beziehungen zwischen Wörtern bzw. Namen und Konzepten bzw. Entitäten formal erfassen. In drei Hauptbeiträgen werden jeweils graphtheoretische bzw. statistische Verfahren zur Verknüpfung von Indizien aus mehreren Wissensquellen vorgestellt. Bei der lexikalischen Integration werden statistische Modelle zur Disambiguierung gebildet. Die Entitäten-Integration fasst semantische Einheiten unter Auflösung von Konflikten zwischen Äquivalenz- und Verschiedenheitsinformationen zusammen. Diese werden schließlich bei der taxonomischen Integration durch eine umfassende taxonomische Hierarchie ergänzt. Zusammen können diese Methoden zur Induzierung einer großen multilingualen Wissensbank eingesetzt werden, welche über 5 Millionen Entitäten und über 16 Millionen Wörter und Namen in mehr als 200 Sprachen semantisch beschreibt

Learning of a multilingual bitaxonomy of Wikipedia and its application to semantic predicates

The ability to extract hypernymy information on a large scale is becoming increasingly important in natural language processing, an area of the artificial intelligence which deals with the processing and understanding of natural language. While initial studies extracted this type of information from textual corpora by means of lexico-syntactic patterns, over time researchers moved to alternative, more structured sources of knowledge, such as Wikipedia. After the first attempts to extract is-a information fromWikipedia categories, a full line of research gave birth to numerous knowledge bases containing information which, however, is either incomplete or irremediably bound to English. To this end we put forward MultiWiBi, the first approach to the construction of a multilingual bitaxonomy which exploits the inner connection between Wikipedia pages and Wikipedia categories to induce a wide-coverage and fine-grained integrated taxonomy. A series of experiments show state-of-the-art results against all the available taxonomic resources available in the literature, also with respect to two novel measures of comparison. Another dimension where existing resources usually fall short is their degree of multilingualism. While knowledge is typically language agnostic, currently resources are able to extract relevant information only in languages providing highquality tools. In contrast, MultiWiBi does not leave any language behind: we show how to taxonomize Wikipedia in an arbitrary language and in a way that is fully independent of additional resources. At the core of our approach lies, in fact, the idea that the English version of Wikipedia can be linguistically exploited as a pivot to project the taxonomic information extracted from English to any other Wikipedia language in order to have a bitaxonomy in a second, arbitrary language; as a result, not only concepts which have an English equivalent are covered, but also those concepts which are not lexicalized in the source language. We also present the impact of having the taxonomized encyclopedic knowledge offered by MultiWiBi embedded into a semantic model of predicates (SPred) which crucially leverages Wikipedia to generalize collections of related noun phrases to infer a probability distribution over expected semantic classes. We applied SPred to a word sense disambiguation task and show that, when MultiWiBi is plugged in to replace an internal component, SPred’s generalization power increases as well as its precision and recall. Finally, we also published MultiWiBi as linked data, a paradigm which fosters interoperability and interconnection among resources and tools through the publication of data on the Web, and developed a public interface which lets the users navigate through MultiWiBi’s taxonomic structure in a graphical, captivating manner