605 research outputs found
Automatic prediction of aspectual class of verbs in context
This paper describes a new approach to predicting the aspectual class of verbs in context, i.e., whether a verb is used in a stative or dynamic sense. We identify two challenging cases of this problem: when
the verb is unseen in training data, and when the verb is ambiguous for aspectual class. A semi-supervised approach using linguistically-motivated features and a novel set of distributional features based
on representative verb types allows us to predict classes accurately, even for unseen verbs. Many frequent verbs can be either stative or dynamic in different contexts, which has not been modeled by previous
work; we use contextual features to resolve this ambiguity. In addition, we introduce two new datasets of clauses marked for aspectual class
Automatic Identification of Aspectual Classes across Verbal Readings
International audienceThe automatic prediction of aspectual classes is very challenging for verbs whose aspectual value varies across readings, which are the rule rather than the exception. This paper sheds a new perspective on this problem by using a machine learning approach and a rich morpho-syntactic and semantic valency lexicon.In contrast to previous work, where the aspectual value of corpus clauses is determined on the basis of features retrieved from the corpus, we use features extracted from the lexicon, and aim to predict the aspectual value of verbal \textit{readings} rather than verbs.Studying the performance of the classifiers on a set of manually annotated verbal readings, we found that our lexicon provided enough information to reliably predict the aspectual value of verbs across their readings.We additionally tested our predictions for unseen predicates through a task based evaluation, by using them in the automatic detection of temporal relation types in TempEval 2007 tasks for French. These experiments also confirmed the reliability of our aspectual predictions, even for unseen verbs
Proceedings of the International Conference Sensory Motor Concepts in Language & Cognition
This volume contains selected papers of the 2008 annual conference of the German Association for Social Science Research on Japan (Vereinigung für sozialwissenschaftliche Japanforschung e.V. – VSJF). The academic meeting has addressed the issue of demographic change in Japan in comparison to the social developments of ageing in Germany and other member states of the European Union. The conference was organized by the Institute for Modern Japanese Studies at Heinrich-Heine-University of Duesseldorf and took place at the Mutter Haus in Kaiserswerth (an ancient part of Duesseldorf). Speakers from Germany, England, Japan and the Netherlands presented their papers in four sessions on the topics “Demographic Trends and Social Analysis”, “Family and Welfare Policies”, “Ageing Society and the Organization of Households” and “Demographic Change and the Economy”. Central to all transnational and national studies on demographic change is the question of how societies can be reconstructed and be made adaptive to these changes in order to survive as solidarity communities. The authors of this volume attend to this question by discussing on recent trends of social and economic restructuring and giving insight into new research developments such as in the area of households and housing, family care work, medical insurance, robot technology or the employment sector
Investigating 'Aspect' in NMT and SMT: translating the English simple past and present perfect
One of the important differences between English and French grammar is related to
how their verbal systems handle aspectual information. While the English simple past tense
is aspectually neutral, the French and Spanish past tenses are linked with a particular
imperfective/perfective aspect. This study examines what Statistical Machine Translation
(SMT) and Neural Machine Translation (NMT) learn about 'aspect'and how this is reflected in
the translations they produce. We use their main knowledge sources, phrase-tables (SMT)
and encoding vectors (NMT), to examine what kind of aspectual information they encode.
Furthermore, we examine whether this encoded 'knowledge'is actually transferred during
decoding and thus reflected in the actual translations. Our study is based on the translations
of the English simple past and present perfect tenses into French and Spanish
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