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Journals and Conferences
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We use logical inference techniques for recognising textual entailment, with theorem proving operating on deep semantic interpretations as the backbone of our system. However, the performance of theorem proving on its own turns out to be highly dependent on a wide range of background knowledge, which is not necessarily included in publically available… (More)
We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparseness. We partially overcome this problem by integrating a thesaurus and… (More)
We investigate the task of learning models for visual object recognition from natural language descriptions alone. The approach contributes to the recognition of fine-grain object categories, such as animal and plant species, where it may be difficult to collect many images for training, but where textual descriptions of visual attributes are readily… (More)
We present the first effort towards producing an Arabic Discourse Treebank, a news corpus where all discourse connectives are identified and annotated with the discourse relations they convey as well as with the two arguments they relate. We discuss our collection of Arabic discourse connectives as well as principles for identifying and annotating them in… (More)
We present a novel method for resolving non-pronominal anaphora. Instead of using handcrafted lexical resources, we search the Web with shallow patterns which can be predetermined for the type of anaphoric phenomenon. In experiments for other-anaphora and bridging, our shallow, almost knowledge-free and unsupervised method achieves state-ofthe-art results.
We reformulate metonymy resolution as a classification task. This is motivated by the regularity of metonymic readings and makes general classification and word sense disambiguation methods available for metonymy resolution. We then present a case study for location names, presenting both a corpus of location names annotated for metonymy as well as… (More)
We compare two ways of obtaining lexical knowledge for antecedent selection in other-anaphora and definite noun phrase coreference. Specifically, we compare an algorithm that relies on links encoded in the manually created lexical hierarchy WordNet and an algorithm that mines corpora by means of shallow lexico-semantic patterns. As corpora we use the… (More)
We provide an overview of the metonymy resolution shared task organised within SemEval-2007. We describe the problem, the data provided to participants, and the evaluation measures we used to assess performance. We also give an overview of the systems that have taken part in the task, and discuss possible directions for future work.
We determine the subjectivity of word senses. To avoid costly annotation, we evaluate how useful existing resources established in opinion mining are for this task. We show that results achieved with existing resources that are not tailored towards word sense subjectivity classification can rival results achieved with supervision on a manually annotated… (More)
At the moment, language resources do not contain the necessary information for large-scale metonymy processing. As a contribution, we here present a corpus annotated for metonymies. We describe a framework for annotating metonymies in domain-independent text that considers the regularity, productivity and underspecification of metonymic usage. We then… (More)