609 research outputs found
Ontology and argument structure in nominalizations
Based on data from German -ung nominalizations, I argue that selection restriction tests are not suitable as linguistic tools for ontological disambiguation. Consequently, I question the significance of ontology as a starting point for linguistic theorizing. Instead, I argue for an underspecified account of the ontology of nominalizations, in which disambiguation looses its central role in the commerce with ambiguity
Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations
International audienceAn important trend in recent works on lexical semantics has been the development of learning methods capable of extracting semantic information from text corpora. The majority of these methods are based on the distributional hypothesis of meaning and acquire semantic information by identifying distributional patterns in texts. In this article, we present a distributional analysis method for extracting nominalization relations from monolingual corpora. The acquisition method makes use of distributional and morphological information to select nominalization candidates. We explain how the learning is performed on a dependency annotated corpus and describe the nominalization results. Furthermore, we show how these results served to enrich an existing lexical resource, the WOLF (Wordnet Libre du Français). We present the techniques that we developed in order to integrate the new information into WOLF, based on both its structure and content. Finally, we evaluate the validity of the automatically obtained information and the correctness of its integration into the semantic resource. The method proved to be useful for boosting the coverage of WOLF and presents the advantage of filling verbal synsets, which are particularly difficult to handle due to the high level of verbal polysemy
Empirical methods for the study of denotation in nominalizations in Spanish
This article deals with deverbal nominalizations in Spanish; concretely, we focus on the denotative distinction between event and result nominalizations. The goals of this work is twofold: first, to detect the most relevant features for this denotative distinction; and, second, to build an automatic classification system of deverbal nominalizations according to their denotation. We have based our study on theoretical hypotheses dealing with this semantic distinction and we have analyzed them empirically by means of Machine Learning techniques which are the basis of the ADN-Classifier. This is the first tool that aims to automatically classify deverbal nominalizations in event, result, or underspecified denotation types in Spanish. The ADN-Classifier has helped us to quantitatively evaluate the validity of our claims regarding deverbal nominalizations. We set up a series of experiments in order to test the ADN-Classifier with different models and in different realistic scenarios depending on the knowledge resources and natural language processors available. The ADN-Classifier achieved good results (87.20% accuracy)
Recognizing deverbal events in context
Abstract. Event detection is a key task in order to access informa- tion through content. This paper focuses on events realized by deverbal nouns in Italian. Deverbal nouns obtained through transpositional suf- fixes (such as -zione; -mento, -tura and -aggio) are commonly known as nouns of action, i.e. nouns which denote the process/action described by the corresponding verbs. However, this class of nouns is also known for a specific polysemous alternation: they may denote the result of the process/action of the corresponding verb. This paper describes a sta- tistically based analysis that helps to develop a classifier for automatic identification of deverbal nouns denoting events in context by exploit- ing rules obtained from syntagmatic and collocational cues identified by linguists
The Automatic Interpretation of Nominalizations
This paper discusses the interpretation of nominalizations in domain independent wide-coverage text. We present a statistical model which interprets nominalizations based on the cooccurrence of verb-argument tuples in a large balanced corpus. We propose an algorithm which treats the interpretation task as a disambiguation problem and achieves a performance of approximately 80 % by combining partial parsing, smoothing techniques and domain independent taxonomic information (e.g., WordNet)
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
Morphological Cues for Lexical Semantics
Most natural language processing tasks require lexical semantic information.
Automated acquisition of this information would thus increase the robustness
and portability of NLP systems. This paper describes an acquisition method
which makes use of fixed correspondences between derivational affixes and
lexical semantic information. One advantage of this method, and of other
methods that rely only on surface characteristics of language, is that the
necessary input is currently available
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