A methodology for the semiautomatic annotation of EPEC-RolSem, a basque corpus labeled at predicative level following the PropBank-Verb Net model

In this article we describe the methodology developed for the semiautomatic annotation of EPEC-RolSem, a Basque corpus labeled at predicate level following the PropBank-VerbNet model. The methodology presented is the product of detailed theoretical study of the semantic nature of verbs in Basque and of their similarities and differences with verbs in other languages. As part of the proposed methodology, we are creating a Basque lexicon on the PropBank-VerbNet model that we have named the Basque Verb Index (BVI). Our work thus dovetails the general trend toward building lexicons from tagged corpora that is clear in work conducted for other languages. EPEC-RolSem and BVI are two important resources for the computational semantic processing of Basque; as far as the authors are aware, they are also the first resources of their kind developed for Basque. In addition, each entry in BVI is linked to the corresponding verb-entry in well-known resources like PropBank, VerbNet, WordNet, Levin’s Classification and FrameNet. We have also implemented several automatic processes to aid in creating and annotating the BVI, including processes designed to facilitate the task of manual annotation.Lan honetan, EPEC-RolSem corpusa etiketatzeko jarraitu dugun metodologia deskribatuko dugu. EPEC-RolSem corpusa PropBank-VerbNet ereduari jarraiki predikatu-mailan etiketatutako euskarazko corpusa da. Etiketatze-lana aurrera eramateko euskal aditzen izaera semantikoa aztertu eta ingeleseko aditzekin konparatu dugu, azterketa horren emaitza da lan honetan proposatzen dugun metodologia. Metodologiaren atal bat PropBank-VerbNet eredura sortutako euskal aditzen lexikoiaren osaketa izan da, lexikoi hau Basque Verb Index (BVI) deitu dugu. Gure lanak alor honetan beste hizkuntzetan dagoen joera nagusia jarraitzen du, hau da, etiketatutako corpusetatik lexikoiak sortzea. EPEC-RolSem eta BVI oso baliabide garrantzitsuak dira euskararen semantika konputazionalaren alorrean, izan ere, euskararako sortutako mota honetako lehen baliabideak dira. Honetaz guztiaz gain, BVIko sarrera bakoitza PropBank, VerbNet, WordNet, Levinen sailkapena eta FrameNet bezalako baliabide ezagunekin lotua dago. Hainbat prozesu automatiko inplementatu ditugu EPEC-RolSem corpusaren eskuzko etiketatzea laguntzeko eta baita BVI sortzeko eta osatzeko ere

VerbAtlas: a novel large-scale verbal semantic resource and its application to semantic role labeling

We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-of-domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org

Predicate Matrix: an interoperable lexical knowledge base for predicates

183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas

Report on second selection of resources, revising selection in D2.1

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.Peer ReviewedPreprin

Rol semantikoen etiketatzeak testuetako espazio-denbora informazioaren prozesamenduan daukan ereaginaz

222 p.Tesi honen xede nagusia euskarazko rol semantikoen etiketatze automatikoa da ( Semantic RoleLabeling , SRL). Besteak beste, euskaraz idatzitako testuen analisi-kateanSRL edo azaleko analisi semantikoa egitea ahalbidetu dugu. Gainera, SRL atazarekin lotura daukateneuskarazko denbora eta espazio informazioaren etiketatze automatikorakoere aurrerapenak egin ditugu. Izan ere, gaur egungo estandarretara egokitutako denboraren etaespazioaren etiketatzeko tresnak garatu ditugu tesian. Orobat, diseinatu etainplementatutako sistema guztien emaitzak beste hizkuntza batzuk prozesatzen dituztentresnen emaitzekin alderatu ditugu.Gure lanaren beste helburua, euskararen analisi-katea hedatzeaz eta osatzeaz gainera,ondorengo bi hipotesiak baieztatzea izan da:Euskaraz denboraren adierazpen linguistikoa etiketatzeko orduan rol semantikoekdaukaten eragina positiboa dela, ingelesez eta gaztelaniaz bezala.Espazioaren adierazpen linguistikoa, denborarena bezala, fenomeno semantikoa dela,eta horregatik semantika eta, zehazkiago, rol semantikoek duten garrantzia nabarmenadela, informazio espazialaren etiketatze eraginkorra egin ahal izateko

Report on first selection of resources

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.Peer ReviewedPreprin

Parsing as Reduction

We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, "head-ordered dependency trees", shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser can be used to produce constituents. When this parser is non-projective, we can perform discontinuous parsing in a very natural manner. Despite the simplicity of our approach, experiments show that the resulting parsers are on par with strong baselines, such as the Berkeley parser for English and the best single system in the SPMRL-2014 shared task. Results are particularly striking for discontinuous parsing of German, where we surpass the current state of the art by a wide margin

NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news

Abstract In this article, we describe a system that reads news articles in four different languages and detects what happened, who is involved, where and when. This event-centric information is represented as episodic situational knowledge on individuals in an interoperable RDF format that allows for reasoning on the implications of the events. Our system covers the complete path from unstructured text to structured knowledge, for which we defined a formal model that links interpreted textual mentions of things to their representation as instances. The model forms the skeleton for interoperable interpretation across different sources and languages. The real content, however, is defined using multilingual and cross-lingual knowledge resources, both semantic and episodic. We explain how these knowledge resources are used for the processing of text and ultimately define the actual content of the episodic situational knowledge that is reported in the news. The knowledge and model in our system can be seen as an example how the Semantic Web helps NLP. However, our systems also generate massive episodic knowledge of the same type as the Semantic Web is built on. We thus envision a cycle of knowledge acquisition and NLP improvement on a massive scale. This article reports on the details of the system but also on the performance of various high-level components. We demonstrate that our system performs at state-of-the-art level for various subtasks in the four languages of the project, but that we also consider the full integration of these tasks in an overall system with the purpose of reading text. We applied our system to millions of news articles, generating billions of triples expressing formal semantic properties. This shows the capacity of the system to perform at an unprecedented scale