Boosting terminology extraction through crosslingual resources

Terminology Extraction is an important Natural Language Processing task with multiple applications in many areas. The task has been approached from different points of view using different techniques. Language and domain independent systems have been proposed as well. Our contribution in this paper focuses on the improvements on Terminology Extraction using crosslingual resources and specifically the Wikipedia and on the use of a variant of PageRank for scoring the candidate terms. // La extracción de terminología es una tarea de procesamiento de la lengua sumamente importante y aplicable en numerosas áreas. La tarea se ha abordado desde múltiples perspectivas y utilizando técnicas diversas. También se han propuesto sistemas independientes de la lengua y del dominio. La contribución de este artículo se centra en las mejoras que los sistemas de extracción de terminología pueden lograr utilizando recursos translingües, y concretamente la Wikipedia y en el uso de una variante de PageRank para valorar los candidatos a términoPeer ReviewedPostprint (published version

Word-sense disambiguated multilingual Wikipedia corpus

This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assignsWordNet senses, andWordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.Peer ReviewedPostprint (published version

Integrating WordNet and Wiktionary with lemon

Nowadays, there is a significant quantity of linguistic data available on the Web. However, linguistic resources are often published using proprietary formats and, as such, it can be difficult to interface with one another and they end up confined in “data silos”. The creation of web standards for the publishing of data on the Web and projects to create Linked Data have lead to interest in the creation of resources that can be published using Web principles. One of the most important aspects of “Lexical Linked Data” is the sharing of lexica and machine readable dictionaries. It is for this reason, that the lemon format has been proposed, which we briefly describe. We then consider two resources that seem ideal candidates for the Linked Data cloud, namely WordNet 3.0 and Wiktionary, a large document based dictionary. We discuss the challenges of converting both resources to lemon , and in particular for Wiktionary, the challenge of processing the mark-up, and handling inconsistencies and underspecification in the source material. Finally, we turn to the task of creating links between the two resources and present a novel algorithm for linking lexica as lexical Linked Data